From Experimentally Identified User Loads to Design Decisions: A Case Study of a Precision Camera Tripod

From Experimentally Identified User Loads to Design Decisions: A Case Study of a Precision Camera Tripod | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Experimentally Identified User Loads to Design Decisions: A Case Study of a Precision Camera Tripod Jakub Duczmalewski, Szymon Cygan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8483808/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Design decisions in precision mechanical systems are often based on assumed or simplified load cases, even though user interaction can dominate structural response during operation. While experimental load identification is widely used for validation, its role in shaping design decisions remains insufficiently documented in design research. This paper investigates how experimentally identified user-induced loads influence design decisions and design quality, using the development of a precision camera tripod as a case study. User-induced loads were experimentally identified during representative camera operation and translated into bending moments acting on the structure. These empirically derived loads were then used as fixed inputs in a decision-oriented design process, guiding targeted construction modifications. Structural modelling was employed in a comparative manner to evaluate alternative design variants under consistent load conditions. The results show that integrating experimentally identified user loads enabled more focused design decisions, leading to improved stiffness and stability under representative operating conditions without disproportionate increases in mass. Rather than driving global reinforcement, empirical load information revealed dominant load paths and deformation mechanisms, supporting selective structural refinement. Beyond the specific case, the study demonstrates a transferable process for embedding experimental load identification into design decision-making. The findings highlight that the primary value of empirical load data lies not in validation alone, but in reducing ambiguity and improving the quality and transparency of design decisions in precision mechanical design. Design decision-making Experimental load identification User-induced loads Precision mechanical design Design quality evaluation Test-assisted design Figures Figure 1 Figure 2 Figure 3 1 Introduction The design of precision mechanical systems increasingly requires a close alignment between assumed loading conditions and the actual conditions encountered during use. Traditional engineering practice often relies on simplified or conservative load assumptions to ensure safety and robustness; however, such assumptions may obscure the true mechanical demands imposed on a structure, leading to over-dimensioned components, suboptimal stiffness–mass trade-offs, or limited insight into performance-limiting mechanisms. As precision devices become lighter, more compact, and more sensitive to deformation, the shortcomings of assumption-driven design approaches become increasingly apparent (Fischer 2008 ; Heuler et al. 2011 ). Recent research has shown growing interest in integrating experimentally identified user and operational loads directly into mechanical product design. Across mechanical and structural engineering domains, empirical load measurements—obtained via direct sensing, indirect identification, or operational testing—have been shown to improve the fidelity of numerical models and the relevance of design evaluations (Augustine et al. 2016 ; Štrba et al. 2020 ; Liu et al. 2022 ). By calibrating structural analyses with measured load spectra rather than hypothetical worst-case scenarios, designers can achieve more accurate predictions of stiffness and stability under real operating conditions, with additional improvements in fatigue-related assessments reported in the literature (Nelson and Fuchs 1975 ; Melnikov et al. 2017 ; Darnieder et al. 2017 ; Wittke et al. 2024 ). Beyond improved modelling accuracy, several studies emphasize that empirical load identification can act as a source of design-relevant insight, rather than merely a validation step. Case studies in precision load cells, machining centers, and deployable structures demonstrate that experimentally identified load paths and load combinations often differ substantially from assumed conditions, motivating targeted geometric or structural modifications (Milberg et al. 1986 ; Jeon and Murphey 2012 ; Brasil 2019 ). Experimental investigations have further highlighted load-dependent effects, including joint behavior and geometric influences, which may affect stiffness distribution and structural response (Kalaycıoğlu and Özgüven 2014 ; Brasil 2019 ). Nevertheless, much of this literature concentrates on measurement techniques or model updating procedures, offering limited insight into how experimental load characteristics are systematically translated into concrete construction decisions. In parallel, research on design decision-making in mechanical engineering increasingly frames design as an iterative process in which empirical evidence progressively refines construction choices. Methodological studies describe how experimental data can be mapped onto design parameters through iterative empirical modelling, experimental modal analysis, and successive evaluation cycles (Gabbert et al. 1995 ; Lu et al. 1998 ; Ge et al. 2002 ). More formal frameworks, such as Bayesian and multi-fidelity approaches, have been proposed in the literature to propagate experimental uncertainty into parameter selection and structural modification strategies (Gray et al. 2022 ; Jia et al. 2022 ). Case-based investigations confirm that empirically informed approaches can support informed trade-offs between stiffness, mass, and robustness, particularly when available experimental data are limited or noisy (Perera and Ruiz 2008 ; Meruane and Heylen 2011 ). Despite these advances, existing studies rarely document the decision-making pathway linking experimentally identified loads to specific construction changes within a complete design process. While numerous contributions demonstrate improved model calibration or optimization outcomes, fewer explicitly trace how measured load characteristics motivate particular design decisions and how these decisions affect overall design quality (Nichols and Olewnik 2012 ; Boothby 2023 ). This gap is especially evident for small-scale, precision mechanical products, where design decisions are highly sensitive to localized load effects and geometric constraints. This article addresses this gap through a focused case study of a lightweight camera tripod. The study examines how experimentally identified user loads are extracted, interpreted, and translated into specific construction decisions, and how these decisions influence key indicators of design quality, including stiffness, stability, and mass efficiency. Accordingly, the research is guided by the following question: How do experimentally identified user loads influence design decisions and design quality in a lightweight camera tripod? By explicitly linking empirical load identification to subsequent construction choices, the paper aims to contribute to design research by clarifying the methodological role of experimental data in shaping design decisions. 2 Design Problem and Requirements The subject of this study is the design of a lightweight camera tripod intended for stable support of imaging equipment during manual handling and adjustment by the user. In this context, the tripod is not treated as a passive load-bearing structure, but as a mechanical system whose performance is directly influenced by user interaction, adjustment forces, and dynamic handling during typical operations. The design problem therefore lies at the intersection of structural stiffness, geometric stability, and mass efficiency, under conditions where even small deformations may degrade functional performance. The primary functional objective of the tripod is to maintain the camera in a prescribed spatial position and orientation with high repeatability during use. This requirement translates directly into structural stiffness demands, particularly in the load paths connecting the camera mounting interface to the ground contact points. Insufficient stiffness may result in perceptible deflections, loss of alignment, or oscillatory behavior, all of which are detrimental in precision imaging applications. At the same time, excessive stiffness achieved through over-dimensioning leads to unnecessary mass increase, reducing portability and usability. Stability represents a second key requirement and is closely coupled to both geometry and stiffness. The tripod must remain statically stable during camera adjustment, repositioning, and incidental user contact. This includes resistance to tipping and excessive angular displacement when subjected to off-axis forces and moments applied at the camera mount or along the structural members. Geometric configuration, including leg spread, joint positioning, and overall proportions, therefore plays a critical role in ensuring stable support across typical usage scenarios. Mass constitutes a competing requirement that constrains achievable stiffness and stability. As a portable device, the tripod is expected to balance mechanical robustness with user comfort and transportability. The design task is thus not to maximise stiffness or stability in isolation, but to achieve an appropriate trade-off between structural performance and mass efficiency. This trade-off is particularly sensitive in precision devices, where marginal stiffness improvements may require disproportionate increases in material usage if not guided by representative loading conditions. Geometric constraints further delimit the design space. These include limits on overall dimensions, leg length, cross-sectional profiles, and joint geometries imposed by usability, manufacturability, and functional integration with the camera system. Such constraints restrict the range of admissible structural configurations and necessitate informed prioritisation of design parameters that most strongly affect performance. In the present case, the geometric design space was constrained by the compact configuration of a hi-hat tripod. The overall height of the structure was limited to approximately 120 mm, with a fixed leg spacing of 290 mm. The tripod was designed to interface with standard professional camera mounting systems, including a Mitchell-type adapter and articulated M8 feet. These geometric constraints significantly restricted admissible structural configurations and amplified the influence of localized stiffness and load-path design on overall performance. Prior to experimental load identification, baseline engineering assumptions were adopted to define initial load cases for preliminary design and evaluation. These assumptions reflected typical engineering practice for similar support structures and provided an initial reference for assessing stiffness and stability. However, at this stage, the assumed loading conditions were not explicitly derived from measured user interaction, and their representativeness for real operating conditions was uncertain. The resulting baseline design therefore served as a starting point for subsequent refinement, rather than as a final or optimal solution. In summary, the design problem addressed in this work involves developing a lightweight camera tripod that satisfies stringent requirements for stiffness, stability, and mass within a constrained geometric design space. These requirements define the criteria against which later design decisions are evaluated and provide the framework for assessing the impact of experimentally identified user loads on construction choices and overall design quality. 3 Experimental Identification of User-Induced Loads The experimental identification of user-induced loads was motivated by the specific operational characteristics of low-profile camera tripods (so-called hi-hat tripods), which are commonly used in constrained spaces and low camera-height applications. In contrast to conventional tripods with widely spread legs, this class of support structure exhibits compact geometry and relatively short load paths, making its mechanical response particularly sensitive to forces and moments introduced by the operator during camera handling. Similar sensitivity of compact precision support structures to operational loading has been reported in studies on precision mechanical systems and machine supports (Milberg et al. 1986 ; Darnieder et al. 2017 ). During typical operation, the tripod supports a camera and a fluid head assembly while being actively manipulated by the operator. Camera motion is performed through two principal degrees of freedom: horizontal rotation ( pan ) and vertical rotation ( tilt ). These motions are achieved by manual actuation of the camera head, which introduces not only the gravitational load of the supported equipment, but also additional user-induced loads in the form of bending and torsional moments. Such combined force–moment loading patterns, driven by human interaction, are known to dominate operational loading in manually actuated mechanical systems (Fischer 2008 ; Heuler et al. 2011 ). From a structural design perspective, these operator-induced moments represent the dominant non-gravitational loading mechanism acting on the tripod. Their magnitude and direction depend on the camera mass, the geometry of the tripod–head assembly, and the manner in which the operator performs camera movements. As demonstrated in multiple engineering domains, including vehicle structures and portable devices, these factors are highly usage-dependent and cannot be reliably inferred from static considerations alone (Heuler and Klätschke 2005 ; Johannesson, P and Speckert, M. 2013 ). This motivated the use of experimental measurements to identify representative user-induced loads for subsequent design evaluation. 3.1 Measurement Model To quantify the bending moment generated during camera operation, an experimental setup was developed in which reaction forces at the ground contact were measured (Fig. 1 ). A force sensor was mounted at the location of a tripod foot in order to capture the vertical reaction force \(\:{F}_{p}\) during operation (Fig. 2 ). The gravitational force \(\:{F}_{g}\) , corresponding to the static weight of the camera and head assembly, was determined from measurements performed under stationary conditions, without operator input. Similar reaction-based load identification strategies have been widely applied when direct measurement of all acting forces is impractical (Nelson and Fuchs 1975 ; Liu et al. 2022 ). Assuming a known lever arm \(\:r\) between the camera’s centre of mass and the ground contact point, the bending moment \(\:{M}_{g}\) acting on the tripod structure was determined from the difference between the measured reaction force and the gravitational force: The plane of action of the bending moment coincided with the symmetry plane of one of the tripod legs, reflecting the asymmetric load transfer induced by operator manipulation during camera motion. Comparable asymmetric loading effects have been observed in experimental investigations of precision supports and machine structures subjected to operational forces (Milberg et al. 1986 ; Brasil 2019 ). 3.2 Experimental Procedure The experimental study involved three professional camera operators with experience in tripod-based camera work. Measurements were conducted for two representative camera configurations, corresponding to lightweight and heavyweight professional camera systems. The total masses of the camera assemblies, including accessories, were approximately 13 kg and 38 kg, respectively. In both cases, the camera was mounted on a professional fluid head with a mass of 10.4 kg. For each configuration, the operators performed a series of dynamic camera movements over time intervals of 14–20 s. Each trial consisted of combined pan and tilt motions representative of typical operational use. In total, six measurement trials were conducted. Reaction forces were recorded using a force sensor with a nominal capacity of 10 kN, providing sufficient resolution to capture both static and dynamic load variations during operation. Comparable experimental protocols, combining representative user actions with reaction-force measurement, have been employed in studies aiming to derive realistic operational loads for design purposes (Johannesson, P and Speckert, M. 2013 ; Štrba et al. 2020 ). The measured force signals were subsequently used to extract representative bending moments associated with user-induced camera motion. Consistent with prior work on measurement-informed design and load identification, these experimentally identified load cases were not treated as exhaustive descriptions of all possible operating conditions, but as representative inputs suitable for guiding structural evaluation and construction decisions (Fischer 2008 ; Štrba et al. 2020 ; Liu et al. 2022 ). These load cases formed the basis for defining input conditions in the subsequent modelling and design decision-making stages. Across all measurement trials, the recorded reaction forces at the ground contact ranged from approximately 360 N to 563 N, depending on the camera configuration and operator input. For the heavier camera setup, the maximum measured reaction force reached 563 N, corresponding to a bending moment of approximately 63 Nm for the assumed lever arm. To account for measurement variability and ensure representativeness of the identified load cases, a design bending moment of 70 Nm was adopted for subsequent evaluation. An equivalent torsional moment of 70 Nm was assumed for pan motion, while the gravitational force of the supported camera and head assembly was conservatively rounded to 500 N for design purposes. 4 Translation of Experimentally Identified Loads into Design Decisions This section describes how the experimentally identified user-induced loads were translated into concrete construction decisions within the design process of the lightweight camera tripod. The focus is placed on the decision logic linking measured load characteristics to structural modifications, rather than on the chronological sequence of modelling or optimisation steps. Candidate structural configurations were generated using a commercially available CAD environment incorporating AI-assisted generative design capabilities. This method served as a tool to maximize the benefits of the approach, allowing for geometry optimization within selected criteria based on defined boundary conditions—specifically the experimentally identified loads. The generative process was constrained by experimentally identified user loads, geometric boundary conditions, and manufacturing assumptions, while the final design selection, chosen from AI-proposed solutions after FEA analysis of the structural assembly, and refinement remained engineer-driven. 4.1 From Load Characteristics to Design-Relevant Parameters The experimentally identified load cases revealed that user-induced bending moments during combined pan and tilt motions constituted the dominant non-gravitational loading mechanism acting on the tripod structure. Compared to initial baseline assumptions, the measured loads exhibited pronounced directionality and asymmetry, concentrating bending effects along specific load paths associated with individual tripod legs and joint interfaces. In design decision-making terms, these observations translated into a prioritisation of parameters governing bending stiffness and load transfer efficiency along the affected structural members. Rather than treating the tripod as a uniformly loaded structure, the design focus shifted toward reinforcing and geometrically optimising those elements most directly engaged by the experimentally identified moments. This load-driven prioritisation of design parameters is consistent with decision-oriented design frameworks that emphasise mapping empirical observations onto a reduced set of influential variables (Lu et al. 1998 ; Nichols and Olewnik 2012 ). 4.2 Load-Informed Modification of Structural Configuration The identification of representative bending moments enabled targeted evaluation of alternative structural configurations under realistic loading conditions. Candidate configurations were evaluated under the experimentally derived loads. Design modifications were therefore not introduced uniformly across the structure, but selectively in response to load activation patterns. In particular, changes in cross-sectional geometry, joint stiffness, and load path continuity were evaluated based on their effectiveness in mitigating the experimentally identified bending effects. Such selective reinforcement strategies align with measurement-informed design approaches reported in the literature, where empirical load data guide local rather than global structural changes (Milberg et al. 1986 ; Štrba et al. 2020 ). Importantly, the load-informed design improvements were achieved without changing the baseline material or manufacturing technology. Both the baseline configuration and the final design were based on aluminum alloy PA13 and assumed conventional three-axis CNC milling. It should be noted that while the technology remained constant, the machining process itself, especially regarding machining time, fixture strategies, and the number of tools required, varied due to geometric differences, potentially impacting production costs. Nevertheless, the application of the same material and technology served as a control variable, allowing for a realistic and direct comparison of the two models' structural performance. This constraint ensured that observed performance improvements resulted from construction decisions and load-path refinement rather than from material substitution or advanced manufacturing processes. 4.3 Iterative Refinement and Decision Closure The translation of loads into design decisions proceeded iteratively, with each modification assessed against the same experimentally identified load cases. This iterative loop allowed the design team to evaluate whether specific construction changes produced meaningful improvements in design-relevant performance indicators, particularly stiffness under user-induced loading, without incurring disproportionate mass penalties. Importantly, the experimentally identified loads served as a fixed reference throughout this process, providing a stable basis for comparing alternative design states. This role of empirical data as a decision anchor is emphasised in design research as a means of reducing ambiguity and preventing overfitting to assumed or idealised conditions (Nichols and Olewnik 2012 ; Boothby 2023 ). By maintaining consistent load inputs, design decisions could be evaluated in terms of their actual contribution to performance improvement rather than artefacts of changing assumptions. 4.4 Implications for Design Quality The integration of experimentally identified user loads into the decision-making process directly influenced the resulting design quality. Improvements in stiffness and stability were achieved primarily through informed redistribution of material and refinement of load paths, rather than through uniform scaling or conservative over-dimensioning. As a result, the final design exhibited a more favourable balance between structural performance and mass compared to the baseline configuration defined prior to load identification. From a methodological perspective, this outcome illustrates how experimental load identification can function as a decision-shaping mechanism rather than a post hoc validation step. Similar conclusions have been reported in empirical design studies, where explicit linkage between measured operational conditions and construction choices was shown to improve the efficiency and transparency of design decisions (Štrba et al. 2020 ; Boothby 2023 ). 5 Structural Modelling and Design Evaluation Structural modelling was applied to quantitatively evaluate the consequences of construction decisions derived from experimentally identified user loads. In contrast to the earlier sections, which established why and how specific design modifications were introduced, the purpose of modelling in this stage was strictly evaluative: to verify whether the selected construction changes produced measurable improvements in performance under the fixed, experimentally derived loading conditions. The modelling scope was intentionally limited to those structural components and interfaces identified in Section 5 as decisive for load transfer and global compliance. The model therefore focused on the dominant load paths activated by user-induced bending moments, while secondary geometric features with negligible influence on stiffness were simplified. This selective representation ensured that modelling results could be directly interpreted in terms of design decisions, rather than obscured by model complexity. Experimentally identified load cases were applied consistently across all evaluated design variants. Boundary conditions and load application points were held unchanged, ensuring that differences in predicted deformation and stiffness could be attributed solely to construction changes rather than to altered assumptions. This controlled comparison framework enabled direct assessment of the effectiveness of individual design decisions. Model outputs were evaluated using a reduced set of performance indicators aligned with the requirements defined in Section 2. These indicators included global stiffness under user-induced loading, local deformation patterns at critical joints and members, and qualitative stability-related deformation modes relevant to camera operation. Mass was tracked as a comparative attribute rather than as an optimisation objective, supporting assessment of stiffness-to-mass efficiency across design variants. The modelling results were used to confirm or reject candidate construction changes proposed in the decision-making phase. Rather than serving as a tool for automated optimisation, structural modelling functioned as a validation layer that supported decision closure by demonstrating whether targeted modifications effectively addressed the experimentally activated deformation mechanisms. This role of modelling as a decision-support instrument, rather than as a predictive end in itself, is consistent with empirical and decision-oriented design practices reported in the literature (Milberg et al. 1986 ; Nichols and Olewnik 2012 ; Boothby 2023 ). Overall, the modelling stage provided quantitative evidence that the final design configuration offered reduced deformation and increased stability under representative user-induced loading without increases in mass. These results formed the basis for the comparative evaluation of design quality presented in the subsequent section. 6 Results: Impact on Design Quality This section presents the results of the design process in terms of their impact on design quality, evaluated relative to the baseline configuration defined prior to experimental load identification. The results are reported exclusively with respect to decision-relevant performance indicators and are structured to highlight the consequences of load-informed construction choices. Figure 3 provides a visual comparison between the baseline tripod configuration and the final design resulting from the load-informed decision process. 6.1 Stiffness and Deformation Behaviour The load-informed design variants exhibited a clear improvement in stiffness under experimentally identified user-induced loading. Compared to the baseline configuration, the final design showed reduced global displacement at the camera mounting interface when subjected to representative bending moments. This reduction was achieved primarily through targeted modifications of load-bearing members and joint regions activated by user-induced loads, rather than through uniform increases in cross-sectional dimensions. Local deformation patterns further confirmed that the dominant compliance mechanisms identified in the baseline design were effectively mitigated. In particular, deformation previously concentrated in specific joints and slender members was redistributed along more favourable load paths in the final configuration. These changes directly correspond to the experimentally observed load directionality and asymmetry, demonstrating that stiffness improvements were aligned with actual usage conditions. From a structural complexity perspective, the final design did not introduce additional assembly elements relative to the baseline configuration. The load-informed solution consisted of four primary structural components, matching the baseline part count, while achieving substantially improved stiffness and deformation behavior. This confirms that the observed performance gains were not obtained through increased design complexity, but through targeted geometric and load-path refinement. 6.2 Stability under User-Induced Loading Stability-related performance improved as a consequence of the refined load transfer characteristics. Under representative user-induced moments, the final design exhibited reduced angular displacement and more uniform support reactions compared to the baseline configuration. This behavior is indicative of increased resistance to tipping and unwanted rotational motion during camera operation. Importantly, the observed stability improvements were not the result of increased footprint or geometric enlargement, but stemmed from construction changes that enhanced the structural response to off-axis loading. This confirms that stability gains were achieved through load-informed structural refinement rather than through conservative geometric adjustments. 6.3 Mass Efficiency and Performance Trade-Offs Despite substantial improvements in stiffness and stability, the load-informed design achieved these gains with a markedly reduced structural mass. Relative to the baseline configuration, whose total mass was 1634 g, the final design exhibited a mass of 529.79 g, corresponding to a mass reduction of 67.58%. At the same time, the maximum structural displacement under the experimentally identified user-induced loads was reduced from 0.442 mm in the baseline configuration to 0.043 mm in the load-informed design, representing a reduction of approximately 90%. This combination of substantially reduced deformation and markedly lower mass resulted in a significantly more favourable stiffness-to-mass balance. A consolidated comparison of the baseline and load-informed designs, including mass, deformation, and structural robustness, is provided in Table 1 . These results reflect the selective nature of the applied design modifications, which introduced material only in regions directly activated by the experimentally identified load paths, while avoiding unnecessary reinforcement elsewhere. Consequently, experimentally identified user loads enabled a more precise allocation of material resources, supporting substantial performance improvements without disproportionate penalties in weight. This balance is particularly relevant for portable precision devices, where mass directly affects usability. Table 1 Comparison of baseline and load-informed tripod designs. Parameter Baseline configuration Load-informed design Relative change Design implication Structural mass 1634 g 529.79 g −67.6% Substantial weight reduction without material substitution Maximum displacement under representative user load 0.442 mm 0.043 mm −90.3% Strong reduction of deformation under operating conditions Minimum safety factor 2.14 4.94 + 131% Increased robustness without conservative scaling Dominant load path Diffuse, multi-member Concentrated, load-aligned — Improved load transfer efficiency Number of structural components 4 4 0 No increase in design complexity Material Aluminium PA13 Aluminium PA13 — Performance gain achieved without material change Manufacturing process 3-axis CNC milling 3-axis CNC milling — Industrial feasibility preserved 6.4 Summary of Design Quality Improvements Taken together, the results show that integrating experimentally identified user loads into the design process led to the following improvements in design quality (see Table 1 for a consolidated comparison): Substantially reduced structural deformation under representative operating conditions, indicating improved stiffness behavior when evaluated under experimentally identified user-induced loads. Improved stability during user interaction, reflected in more favourable deformation modes and reduced sensitivity to asymmetric loading during combined pan–tilt motion. A markedly enhanced stiffness–to–mass balance, achieved through a simultaneous reduction in structural deformation and overall mass relative to the baseline configuration. No increase in design complexity, as the number of structural components remained unchanged between the baseline and load-informed designs. Preserved material and manufacturing assumptions, with performance gains achieved without material substitution or adoption of advanced manufacturing processes. These improvements were achieved without relying on conservative scaling strategies. Instead, they emerged from targeted construction decisions directly informed by measured operational loads, demonstrating the value of experimentally identified user loads as a decision-shaping input in precision mechanical design. 7 Discussion The presented case study provides broader insight into how experimentally identified user-induced loads can influence design decision-making beyond a single application. While the results are grounded in the design of a lightweight camera tripod, the underlying methodological implications extend to a wider class of precision and manually actuated mechanical structures. While AI-assisted generative design tools facilitated the exploration of alternative structural configurations, the present results indicate that the decisive factor for design quality improvement was the integration of experimentally identified user loads into the decision-making process, rather than the use of generative techniques per se. 7.1 Experimental Load Identification as a Design-Shaping Input The results reinforce observations reported in the literature that experimentally identified operational loads are most valuable when they inform which structural features govern performance , rather than when they merely refine assumed load magnitudes. Studies on load collectives and test-assisted design show that simplified or conservative load assumptions often obscure dominant load paths and lead to inefficient material allocation (Fischer 2008 ; Heuler et al. 2011 ). In contrast, empirical load identification can reveal load directionality, asymmetry, and coupling effects that directly shape construction decisions. In the present study, experimentally identified user-induced moments highlighted specific load paths and deformation mechanisms that were not evident from baseline assumptions. This enabled targeted structural refinement rather than uniform reinforcement, consistent with prior reports in precision mechanical systems and machine structures where experimental stiffness investigations guided focused design changes (Milberg et al. 1986 ; Darnieder et al. 2017 ). 7.2 Managing Trade-Offs between Stiffness, Stability, and Mass A recurring theme in the literature on precision structures is the trade-off between stiffness, stability, and mass under realistic operating conditions. Measurement-informed approaches have been shown to reduce unnecessary conservatism by aligning structural reinforcement with actual service loads rather than hypothetical extremes (Štrba et al. 2020 ; Boothby 2023 ). The results presented here support this perspective: improvements in stiffness and stability were achieved primarily through load-informed redistribution of material, yielding a more favourable stiffness-to-mass balance than the baseline design. This outcome reflects a general principle applicable beyond the present case: when user-induced loads dominate functional performance, design efficiency depends less on increasing overall structural capacity and more on understanding how operating conditions activate specific compliance mechanisms. Similar conclusions have been reported in studies of portable devices and manually actuated systems, where empirical load characterization enabled mass reduction without compromising usability or stability (Boothby 2023 ). Although cost was not treated as a primary optimisation objective, it is noteworthy that the final load-informed design remained comparable in manufacturing cost to commercially available baseline solutions. The improved stiffness–mass balance was therefore achieved without resorting to expensive materials or manufacturing processes, reinforcing the practical relevance of the proposed decision-oriented design approach. 7.3 Role of Modelling in Empirically Informed Design Workflows The study also illustrates a broader methodological role of structural modelling in empirically informed design processes. Rather than serving as a tool for exhaustive prediction or formal optimisation, modelling was used here to compare alternative design states under fixed, experimentally derived load cases. This comparative use of models aligns with design decision-making frameworks that emphasize transparency, interpretability, and support for decision closure (Lu et al. 1998 ; Nichols and Olewnik 2012 ). Prior work shows that such modelling strategies are particularly effective when empirical data constrain the design space, allowing relatively simple models to deliver high decision value (Štrba et al. 2020 ; Boothby 2023 ). The present results confirm that meaningful improvements in design quality can be achieved without resorting to complex probabilistic or data-driven modelling, provided that the applied loads are representative of actual use. 7.4 Generalisability and Variability of User-Induced Loads As noted in earlier studies, user-induced operational loads exhibit inherent variability due to differences in user behavior, configuration, and context of use (Fischer 2008 ; Johannesson, P and Speckert, M. 2013 ). Consequently, the specific load magnitudes identified in this study should not be interpreted as universally applicable. Instead, they represent a limited but informative sample of realistic operating conditions. The transferable contribution of this work therefore lies in the process rather than in the numerical values of the identified loads. The combination of experimental load identification, decision-focused modelling, and comparative evaluation provides a generalizable framework that can be adapted to other precision and manually actuated structures. This process-oriented perspective is consistent with calls in the design research literature for greater transparency in how empirical data influence construction decisions (Nichols and Olewnik 2012 ). 7.5 Implications for Design Research From a design research standpoint, this study contributes a documented example of how experimentally identified operational loads can be translated into construction decisions and evaluated in terms of design quality. By explicitly tracing the pathway from empirical measurement to structural modification and performance outcome, the work addresses a gap identified in prior studies concerning the visibility of decision pathways in engineering design (Boothby 2023 ). More broadly, the findings support the view that improvements in design quality arise not from the isolated application of experimental methods or modelling tools, but from their integration into coherent, decision-oriented design processes. This insight is applicable across domains where user interaction plays a central role in structural loading and performance. 8 Conclusions This study examined how experimentally identified user-induced loads influence design decisions and design quality in the development of a lightweight camera tripod. By embedding empirical load identification into the design process, the work demonstrates a clear and traceable pathway from measured operating conditions to construction decisions and performance outcomes. The results show that experimentally identified user loads provide decision-relevant information that is not captured by baseline engineering assumptions alone. In the investigated case, empirical load data revealed dominant bending effects and asymmetric load paths activated during typical camera operation. When explicitly translated into construction decisions, these insights enabled targeted structural modifications that improved stiffness and stability under representative operating conditions. Importantly, these performance improvements were achieved alongside a substantial reduction in structural mass, resulting in a markedly improved stiffness-to-mass balance relative to the baseline design. This outcome illustrates that realistic load characterization can support more efficient allocation of material resources, avoiding conservative over-dimensioning while maintaining functional performance in portable precision devices. From a methodological perspective, the study confirms that experimental load identification can function as a decision-shaping element rather than solely as a post hoc validation step. When combined with decision-focused structural modelling and comparative evaluation under fixed load conditions, empirical data directly support decision closure and improve the transparency of the design process. While the numerical load values reported here are specific to the investigated configuration and user sample, the transferable contribution of this work lies in the demonstrated process. The integration of experimentally identified loads, decision-oriented modelling, and performance-based comparison provides a generalizable framework for improving design quality in precision and manually actuated mechanical structures. Statements and Declarations Competing Interests The authors declare that they have no competing interests. Funding This research received no external funding. Author Contributions Conceptualization and study design were led by both authors. Experimental investigations, data acquisition, and practical design work were carried out by Jakub Duczmalewski as part of his master’s thesis. Data analysis and interpretation were performed jointly by Jakub Duczmalewski and Szymon Cygan. The original draft of the manuscript was prepared by Szymon Cygan, with contributions to technical content from Jakub Duczmalewski. Manuscript review, editing, and final approval were conducted by both authors. Data Availability The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. References Augustine P, Hunter T, Sievers N, Guo X (2016) Load Identification of a Suspension Assembly Using True-Load Self Transducer Generation. https://doi.org/10.4271/2016-01-0429 . SAE Tech Pap Boothby T (2023) Empirical Design in Structural Engineering. ICE Publishing Brasil RMLRF (2019) Dynamic analysis of unbalanced rotary machine support structures considering geometric stiffness. In: Proceedings of the International Conference on Structural Engineering Darnieder M, Marangoni RR, Theska R, Rahneberg I (2017) Contribution to the mechanical enhancement of load cells in precision weighing technology. In: Proceedings of the European Society for Precision Engineering and Nanotechnology (EUSPEN). Hannover Fischer W (2008) Load assumptions, load collectives, dimensioning and component testing. Mater Test 50:447–450. https://doi.org/10.3139/120.100899 Gabbert U, Zehn M, Wah F (1995) Improved results in structural dynamic calculations by linking FEA and experimental modal analysis. In: ASME Design Engineering Technical Conference. pp 1321–1328 Ge P, Wang N, Lu SC-Y (2002) An evolutionary modeling approach for automotive bumper system design and analysis. J Comput Inf Sci Eng. https://doi.org/10.1115/1.1529212 Gray A, Wimbush A, de Angelis M, Rocchetta R (2022) From inference to design: A comprehensive framework for uncertainty quantification. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2021.108210 Heuler P, Frost M, Rochlitz H (2011) Approaches of load assumption for durability assessment of automotive structure. Mater Werkst. https://doi.org/10.1002/mawe.201100762 Heuler P, Klätschke H (2005) Generation and use of standardised load spectra and load–time histories. Int J Fatigue 27:974–990. https://doi.org/10.1016/j.ijfatigue.2004.09.012 Jeon SK, Murphey TW (2012) Fundamental design of tensioned precision deployable space structures. In: AIAA/ASME Structures, Structural Dynamics and Materials Conference Jia X, Sedehi O, Papadimitriou C et al (2022) Hierarchical Bayesian modeling framework for model updating and robust predictions in structural dynamics using modal features. Mech Syst Signal Process 170:108784. https://doi.org/10.1016/j.ymssp.2021.108784 Johannesson P, Speckert M (eds) (2013) Load Variation and Reliability. Guide to Load Analysis for Durability in Vehicle Engineering. Wiley, Ltd, pp 253–284 Kalaycıoğlu T, Özgüven HN (2014) Nonlinear structural modification and nonlinear coupling. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2014.01.016 Liu R, Dobriban E, Hou Z, Qian K (2022) Dynamic Load Identification for Mechanical Systems: A Review. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-021-09594-7 Lu SC-Y, Ge P, Wang N (1998) Application of an evolutionary modeling approach to automotive bumper system design. In: ASME Design Engineering Technical Conference Melnikov A, Soal K, Bienert J (2017) Determination of static stiffness of mechanical structures from operational modal analysis. In: International Operational Modal Analysis Conference (IOMAC). Ingolstadt Meruane V, Heylen W (2011) An hybrid real genetic algorithm to detect structural damage using modal properties. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2010.11.020 Milberg J, Eibelshaeuser P, Kirchknopf P (1986) Comments on the Dynamic Behaviour of Machine Tools. Experimental Determination of Stiffness of Rigid Joint Connections. VDI-Z 128:161–166 Nelson DV, Fuchs HO (1975) Predictions of cumulative fatigue damage using condensed load histories. SAE Tech Pap. https://doi.org/10.4271/750045 Nichols A, Olewnik A (2012) A pilot study of engineering design-decision methods in practice. In: ASME Design Engineering Technical Conference. ASME, Chicago, Illinois, USA Perera R, Ruiz A (2008) A multistage FE updating procedure for damage identification in large-scale structures. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2007.10.004 Štrba M, Karmazínová M, Bukovská P (2020) Experiences with using of loading tests and the design assisted by testing method. Int J Mech Wittke M, Darnieder M, Fröhlich T, Theska R (2024) Enhanced stiffness characterization of load cells by relative change of the natural frequency. Tech Mess. https://doi.org/10.1515/teme-2024-0087 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8483808","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":568413682,"identity":"056d35f7-2db4-4b95-b2df-66f6b9eddca9","order_by":0,"name":"Jakub Duczmalewski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie3PMWrDMBSAYRmvgqwSDsoJCk8IMrpXkfHgRZ26FDpUJoMXdQ/0FF00uxTqxXQOZGjA0MlDvCWQobahpYXYzRiIfpCQ4H0gIeRynWGk33PEPC3747RdPpLjxNMoB/FN8Mkk0ug3GYtmj8VmVx6SRaZ4s1chnpCkgg0K2ZU+TgL8HqVmBTepqQWhNsZ0qYSUKBbz/DhhRHGNty1Zqjni1sewVn4uUR7ZEZIetpB4HYnsA75eJ9UoCVqywCuQPXmxrxgC2T1smFBT8qdpKXhqPm9pagtMPmoBEob/QgoDTf3GZjyLn5u9vWcTU1R0dxeyIfIT13+u8M941+yEGZfL5brQvgBdbF8s/JmrWwAAAABJRU5ErkJggg==","orcid":"","institution":"Warsaw University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jakub","middleName":"","lastName":"Duczmalewski","suffix":""},{"id":568413683,"identity":"696db009-7f12-4a0d-aaf1-41f7c193ce07","order_by":1,"name":"Szymon Cygan","email":"","orcid":"","institution":"Warsaw University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Szymon","middleName":"","lastName":"Cygan","suffix":""}],"badges":[],"createdAt":"2025-12-30 19:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8483808/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8483808/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99790523,"identity":"caac762c-27f5-4fd2-8fb7-e3b764a1c9e6","added_by":"auto","created_at":"2026-01-08 12:58:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4023371,"visible":true,"origin":"","legend":"","description":"","filename":"PapertripodJD.docx","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/7b75fec94310e65fafc55195.docx"},{"id":99513915,"identity":"074c50b3-6d8d-4567-a933-4d5cc2d46444","added_by":"auto","created_at":"2026-01-05 09:58:32","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4844,"visible":true,"origin":"","legend":"","description":"","filename":"16c70facb1c3471482f4756cc7570a20.json","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/020643a0a65d808ce1625c19.json"},{"id":99513919,"identity":"a4c846c8-7bba-4b10-b043-f786f893c5f6","added_by":"auto","created_at":"2026-01-05 09:58:32","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84194,"visible":true,"origin":"","legend":"","description":"","filename":"16c70facb1c3471482f4756cc7570a201enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/eeae769e494c36230e4b1589.xml"},{"id":99790524,"identity":"42fe4d2f-ef63-4cc0-ab77-8a239ee00992","added_by":"auto","created_at":"2026-01-08 12:58:17","extension":"emf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2827984,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/effd095c5c5352d7c9e9637c.emf"},{"id":99513924,"identity":"9be5678e-6ae4-4418-875e-6eb9f7e9d6ad","added_by":"auto","created_at":"2026-01-05 09:58:32","extension":"emf","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2789540,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.emf","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/0be537574f64bf9aa8664159.emf"},{"id":99513918,"identity":"15227dba-3699-4705-9d7c-b2943ec09794","added_by":"auto","created_at":"2026-01-05 09:58:32","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":428848,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/999346c1710cf5d7dd1a8ff0.jpeg"},{"id":99513922,"identity":"f308b389-2d2d-4ee6-8133-abe9fdadfa9e","added_by":"auto","created_at":"2026-01-05 09:58:32","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194510,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/53868f06a6ef8b9f44c7dd73.png"},{"id":99513923,"identity":"1b95947e-c448-461f-99aa-2cc165a94b70","added_by":"auto","created_at":"2026-01-05 09:58:32","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":242086,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/4bc676556bdd102d18f5b528.png"},{"id":99791693,"identity":"25389bd2-f50b-4957-8085-323315c5115e","added_by":"auto","created_at":"2026-01-08 13:08:07","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":210474,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/94042a667d28ced84e558fe9.png"},{"id":99513926,"identity":"e0aa98ef-b564-4ab0-97e5-ecb9fe881836","added_by":"auto","created_at":"2026-01-05 09:58:32","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83393,"visible":true,"origin":"","legend":"","description":"","filename":"16c70facb1c3471482f4756cc7570a201structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/6cf93f91b622b83cff63a116.xml"},{"id":99791751,"identity":"1512d556-06a2-4036-9df4-fc821312f4c3","added_by":"auto","created_at":"2026-01-08 13:09:39","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89940,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/e67b28299ba068ce54741e09.html"},{"id":99513914,"identity":"e6f706fe-e970-4e89-a73e-843b34fc60aa","added_by":"auto","created_at":"2026-01-05 09:58:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":457308,"visible":true,"origin":"","legend":"\u003cp\u003eThe camera on a tripod mount as assembled for experimental measurements\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/9f5b73bea4ae79b24f3a9eff.png"},{"id":99791643,"identity":"e8e48631-ba42-488b-b744-a5d4c96fc116","added_by":"auto","created_at":"2026-01-08 13:06:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":405068,"visible":true,"origin":"","legend":"\u003cp\u003eClose-up view of the force sensor used to measure forces of the tripod support\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/a1381df31e70332012d9e52a.png"},{"id":99791622,"identity":"4fd2fade-a8d3-46cd-bc5f-34aca32998f9","added_by":"auto","created_at":"2026-01-08 13:05:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":217334,"visible":true,"origin":"","legend":"\u003cp\u003eTripods: (left) Baseline tripod configuration prior to experimental load identification; (right) Load-informed tripod configuration resulting from the decision-oriented design process\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/220f957cbd7e2f0c540528e7.png"},{"id":102142316,"identity":"6a5cc5d5-46e4-4d07-917a-f5defd44b4fa","added_by":"auto","created_at":"2026-02-08 11:39:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2526725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8483808/v1/ce409818-3922-4c94-903d-99ac665171db.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Experimentally Identified User Loads to Design Decisions: A Case Study of a Precision Camera Tripod","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe design of precision mechanical systems increasingly requires a close alignment between assumed loading conditions and the actual conditions encountered during use. Traditional engineering practice often relies on simplified or conservative load assumptions to ensure safety and robustness; however, such assumptions may obscure the true mechanical demands imposed on a structure, leading to over-dimensioned components, suboptimal stiffness\u0026ndash;mass trade-offs, or limited insight into performance-limiting mechanisms. As precision devices become lighter, more compact, and more sensitive to deformation, the shortcomings of assumption-driven design approaches become increasingly apparent (Fischer \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Heuler et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent research has shown growing interest in integrating experimentally identified user and operational loads directly into mechanical product design. Across mechanical and structural engineering domains, empirical load measurements\u0026mdash;obtained via direct sensing, indirect identification, or operational testing\u0026mdash;have been shown to improve the fidelity of numerical models and the relevance of design evaluations (Augustine et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Štrba et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By calibrating structural analyses with measured load spectra rather than hypothetical worst-case scenarios, designers can achieve more accurate predictions of stiffness and stability under real operating conditions, with additional improvements in fatigue-related assessments reported in the literature (Nelson and Fuchs \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Melnikov et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Darnieder et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wittke et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond improved modelling accuracy, several studies emphasize that empirical load identification can act as a source of design-relevant insight, rather than merely a validation step. Case studies in precision load cells, machining centers, and deployable structures demonstrate that experimentally identified load paths and load combinations often differ substantially from assumed conditions, motivating targeted geometric or structural modifications (Milberg et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Jeon and Murphey \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Brasil \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Experimental investigations have further highlighted load-dependent effects, including joint behavior and geometric influences, which may affect stiffness distribution and structural response (Kalaycıoğlu and \u0026Ouml;zg\u0026uuml;ven \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Brasil \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nevertheless, much of this literature concentrates on measurement techniques or model updating procedures, offering limited insight into how experimental load characteristics are systematically translated into concrete construction decisions.\u003c/p\u003e \u003cp\u003eIn parallel, research on design decision-making in mechanical engineering increasingly frames design as an iterative process in which empirical evidence progressively refines construction choices. Methodological studies describe how experimental data can be mapped onto design parameters through iterative empirical modelling, experimental modal analysis, and successive evaluation cycles (Gabbert et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Ge et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). More formal frameworks, such as Bayesian and multi-fidelity approaches, have been proposed in the literature to propagate experimental uncertainty into parameter selection and structural modification strategies (Gray et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jia et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Case-based investigations confirm that empirically informed approaches can support informed trade-offs between stiffness, mass, and robustness, particularly when available experimental data are limited or noisy (Perera and Ruiz \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Meruane and Heylen \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these advances, existing studies rarely document the decision-making pathway linking experimentally identified loads to specific construction changes within a complete design process. While numerous contributions demonstrate improved model calibration or optimization outcomes, fewer explicitly trace how measured load characteristics motivate particular design decisions and how these decisions affect overall design quality (Nichols and Olewnik \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Boothby \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This gap is especially evident for small-scale, precision mechanical products, where design decisions are highly sensitive to localized load effects and geometric constraints.\u003c/p\u003e \u003cp\u003eThis article addresses this gap through a focused case study of a lightweight camera tripod. The study examines how experimentally identified user loads are extracted, interpreted, and translated into specific construction decisions, and how these decisions influence key indicators of design quality, including stiffness, stability, and mass efficiency. Accordingly, the research is guided by the following question:\u003c/p\u003e \u003cp\u003e \u003cem\u003eHow do experimentally identified user loads influence design decisions and design quality in a lightweight camera tripod?\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBy explicitly linking empirical load identification to subsequent construction choices, the paper aims to contribute to design research by clarifying the methodological role of experimental data in shaping design decisions.\u003c/p\u003e"},{"header":"2 Design Problem and Requirements","content":"\u003cp\u003eThe subject of this study is the design of a lightweight camera tripod intended for stable support of imaging equipment during manual handling and adjustment by the user. In this context, the tripod is not treated as a passive load-bearing structure, but as a mechanical system whose performance is directly influenced by user interaction, adjustment forces, and dynamic handling during typical operations. The design problem therefore lies at the intersection of structural stiffness, geometric stability, and mass efficiency, under conditions where even small deformations may degrade functional performance.\u003c/p\u003e \u003cp\u003eThe primary functional objective of the tripod is to maintain the camera in a prescribed spatial position and orientation with high repeatability during use. This requirement translates directly into structural stiffness demands, particularly in the load paths connecting the camera mounting interface to the ground contact points. Insufficient stiffness may result in perceptible deflections, loss of alignment, or oscillatory behavior, all of which are detrimental in precision imaging applications. At the same time, excessive stiffness achieved through over-dimensioning leads to unnecessary mass increase, reducing portability and usability.\u003c/p\u003e \u003cp\u003eStability represents a second key requirement and is closely coupled to both geometry and stiffness. The tripod must remain statically stable during camera adjustment, repositioning, and incidental user contact. This includes resistance to tipping and excessive angular displacement when subjected to off-axis forces and moments applied at the camera mount or along the structural members. Geometric configuration, including leg spread, joint positioning, and overall proportions, therefore plays a critical role in ensuring stable support across typical usage scenarios.\u003c/p\u003e \u003cp\u003eMass constitutes a competing requirement that constrains achievable stiffness and stability. As a portable device, the tripod is expected to balance mechanical robustness with user comfort and transportability. The design task is thus not to maximise stiffness or stability in isolation, but to achieve an appropriate trade-off between structural performance and mass efficiency. This trade-off is particularly sensitive in precision devices, where marginal stiffness improvements may require disproportionate increases in material usage if not guided by representative loading conditions.\u003c/p\u003e \u003cp\u003eGeometric constraints further delimit the design space. These include limits on overall dimensions, leg length, cross-sectional profiles, and joint geometries imposed by usability, manufacturability, and functional integration with the camera system. Such constraints restrict the range of admissible structural configurations and necessitate informed prioritisation of design parameters that most strongly affect performance.\u003c/p\u003e \u003cp\u003eIn the present case, the geometric design space was constrained by the compact configuration of a hi-hat tripod. The overall height of the structure was limited to approximately 120 mm, with a fixed leg spacing of 290 mm. The tripod was designed to interface with standard professional camera mounting systems, including a Mitchell-type adapter and articulated M8 feet. These geometric constraints significantly restricted admissible structural configurations and amplified the influence of localized stiffness and load-path design on overall performance.\u003c/p\u003e \u003cp\u003ePrior to experimental load identification, baseline engineering assumptions were adopted to define initial load cases for preliminary design and evaluation. These assumptions reflected typical engineering practice for similar support structures and provided an initial reference for assessing stiffness and stability. However, at this stage, the assumed loading conditions were not explicitly derived from measured user interaction, and their representativeness for real operating conditions was uncertain. The resulting baseline design therefore served as a starting point for subsequent refinement, rather than as a final or optimal solution.\u003c/p\u003e \u003cp\u003eIn summary, the design problem addressed in this work involves developing a lightweight camera tripod that satisfies stringent requirements for stiffness, stability, and mass within a constrained geometric design space. These requirements define the criteria against which later design decisions are evaluated and provide the framework for assessing the impact of experimentally identified user loads on construction choices and overall design quality.\u003c/p\u003e"},{"header":"3 Experimental Identification of User-Induced Loads","content":"\u003cp\u003eThe experimental identification of user-induced loads was motivated by the specific operational characteristics of low-profile camera tripods (so-called \u003cem\u003ehi-hat\u003c/em\u003e tripods), which are commonly used in constrained spaces and low camera-height applications. In contrast to conventional tripods with widely spread legs, this class of support structure exhibits compact geometry and relatively short load paths, making its mechanical response particularly sensitive to forces and moments introduced by the operator during camera handling. Similar sensitivity of compact precision support structures to operational loading has been reported in studies on precision mechanical systems and machine supports (Milberg et al. \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e; Darnieder et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eDuring typical operation, the tripod supports a camera and a fluid head assembly while being actively manipulated by the operator. Camera motion is performed through two principal degrees of freedom: horizontal rotation (\u003cem\u003epan\u003c/em\u003e) and vertical rotation (\u003cem\u003etilt\u003c/em\u003e). These motions are achieved by manual actuation of the camera head, which introduces not only the gravitational load of the supported equipment, but also additional user-induced loads in the form of bending and torsional moments. Such combined force\u0026ndash;moment loading patterns, driven by human interaction, are known to dominate operational loading in manually actuated mechanical systems (Fischer \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Heuler et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFrom a structural design perspective, these operator-induced moments represent the dominant non-gravitational loading mechanism acting on the tripod. Their magnitude and direction depend on the camera mass, the geometry of the tripod\u0026ndash;head assembly, and the manner in which the operator performs camera movements. As demonstrated in multiple engineering domains, including vehicle structures and portable devices, these factors are highly usage-dependent and cannot be reliably inferred from static considerations alone (Heuler and Kl\u0026auml;tschke \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Johannesson, P and Speckert, M. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). This motivated the use of experimental measurements to identify representative user-induced loads for subsequent design evaluation.\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Measurement Model\u003c/h2\u003e\n \u003cp\u003eTo quantify the bending moment generated during camera operation, an experimental setup was developed in which reaction forces at the ground contact were measured (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A force sensor was mounted at the location of a tripod foot in order to capture the vertical reaction force \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{p}\\)\u003c/span\u003e\u003c/span\u003eduring operation (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The gravitational force \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{g}\\)\u003c/span\u003e\u003c/span\u003e, corresponding to the static weight of the camera and head assembly, was determined from measurements performed under stationary conditions, without operator input. Similar reaction-based load identification strategies have been widely applied when direct measurement of all acting forces is impractical (Nelson and Fuchs \u003cspan class=\"CitationRef\"\u003e1975\u003c/span\u003e; Liu et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAssuming a known lever arm \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e between the camera\u0026rsquo;s centre of mass and the ground contact point, the bending moment \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{g}\\)\u003c/span\u003e\u003c/span\u003eacting on the tripod structure was determined from the difference between the measured reaction force and the gravitational force:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe plane of action of the bending moment coincided with the symmetry plane of one of the tripod legs, reflecting the asymmetric load transfer induced by operator manipulation during camera motion. Comparable asymmetric loading effects have been observed in experimental investigations of precision supports and machine structures subjected to operational forces (Milberg et al. \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e; Brasil \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Experimental Procedure\u003c/h2\u003e\n \u003cp\u003eThe experimental study involved three professional camera operators with experience in tripod-based camera work. Measurements were conducted for two representative camera configurations, corresponding to lightweight and heavyweight professional camera systems. The total masses of the camera assemblies, including accessories, were approximately 13 kg and 38 kg, respectively. In both cases, the camera was mounted on a professional fluid head with a mass of 10.4 kg.\u003c/p\u003e\n \u003cp\u003eFor each configuration, the operators performed a series of dynamic camera movements over time intervals of 14\u0026ndash;20 s. Each trial consisted of combined pan and tilt motions representative of typical operational use. In total, six measurement trials were conducted. Reaction forces were recorded using a force sensor with a nominal capacity of 10 kN, providing sufficient resolution to capture both static and dynamic load variations during operation. Comparable experimental protocols, combining representative user actions with reaction-force measurement, have been employed in studies aiming to derive realistic operational loads for design purposes (Johannesson, P and Speckert, M. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u0026Scaron;trba et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe measured force signals were subsequently used to extract representative bending moments associated with user-induced camera motion. Consistent with prior work on measurement-informed design and load identification, these experimentally identified load cases were not treated as exhaustive descriptions of all possible operating conditions, but as representative inputs suitable for guiding structural evaluation and construction decisions (Fischer \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; \u0026Scaron;trba et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). These load cases formed the basis for defining input conditions in the subsequent modelling and design decision-making stages.\u003c/p\u003e\n \u003cp\u003eAcross all measurement trials, the recorded reaction forces at the ground contact ranged from approximately 360 N to 563 N, depending on the camera configuration and operator input. For the heavier camera setup, the maximum measured reaction force reached 563 N, corresponding to a bending moment of approximately 63 Nm for the assumed lever arm. To account for measurement variability and ensure representativeness of the identified load cases, a design bending moment of 70 Nm was adopted for subsequent evaluation. An equivalent torsional moment of 70 Nm was assumed for pan motion, while the gravitational force of the supported camera and head assembly was conservatively rounded to 500 N for design purposes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Translation of Experimentally Identified Loads into Design Decisions","content":"\u003cp\u003eThis section describes how the experimentally identified user-induced loads were translated into concrete construction decisions within the design process of the lightweight camera tripod. The focus is placed on the \u003cem\u003edecision logic\u003c/em\u003e linking measured load characteristics to structural modifications, rather than on the chronological sequence of modelling or optimisation steps.\u003c/p\u003e \u003cp\u003eCandidate structural configurations were generated using a commercially available CAD environment incorporating AI-assisted generative design capabilities. This method served as a tool to maximize the benefits of the approach, allowing for geometry optimization within selected criteria based on defined boundary conditions\u0026mdash;specifically the experimentally identified loads. The generative process was constrained by experimentally identified user loads, geometric boundary conditions, and manufacturing assumptions, while the final design selection, chosen from AI-proposed solutions after FEA analysis of the structural assembly, and refinement remained engineer-driven.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 From Load Characteristics to Design-Relevant Parameters\u003c/h2\u003e \u003cp\u003eThe experimentally identified load cases revealed that user-induced bending moments during combined pan and tilt motions constituted the dominant non-gravitational loading mechanism acting on the tripod structure. Compared to initial baseline assumptions, the measured loads exhibited pronounced directionality and asymmetry, concentrating bending effects along specific load paths associated with individual tripod legs and joint interfaces.\u003c/p\u003e \u003cp\u003eIn design decision-making terms, these observations translated into a prioritisation of parameters governing bending stiffness and load transfer efficiency along the affected structural members. Rather than treating the tripod as a uniformly loaded structure, the design focus shifted toward reinforcing and geometrically optimising those elements most directly engaged by the experimentally identified moments. This load-driven prioritisation of design parameters is consistent with decision-oriented design frameworks that emphasise mapping empirical observations onto a reduced set of influential variables (Lu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Nichols and Olewnik \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Load-Informed Modification of Structural Configuration\u003c/h2\u003e \u003cp\u003eThe identification of representative bending moments enabled targeted evaluation of alternative structural configurations under realistic loading conditions. Candidate configurations were evaluated under the experimentally derived loads.\u003c/p\u003e \u003cp\u003eDesign modifications were therefore not introduced uniformly across the structure, but selectively in response to load activation patterns. In particular, changes in cross-sectional geometry, joint stiffness, and load path continuity were evaluated based on their effectiveness in mitigating the experimentally identified bending effects. Such selective reinforcement strategies align with measurement-informed design approaches reported in the literature, where empirical load data guide local rather than global structural changes (Milberg et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Štrba et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, the load-informed design improvements were achieved without changing the baseline material or manufacturing technology. Both the baseline configuration and the final design were based on aluminum alloy PA13 and assumed conventional three-axis CNC milling. \u003cb\u003eIt should be noted that while the technology remained constant, the machining process itself, especially regarding machining time, fixture strategies, and the number of tools required, varied due to geometric differences, potentially impacting production costs. Nevertheless, the application of the same material and technology served as a control variable, allowing for a realistic and direct comparison of the two models' structural performance.\u003c/b\u003e This constraint ensured that observed performance improvements resulted from construction decisions and load-path refinement rather than from material substitution or advanced manufacturing processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Iterative Refinement and Decision Closure\u003c/h2\u003e \u003cp\u003eThe translation of loads into design decisions proceeded iteratively, with each modification assessed against the same experimentally identified load cases. This iterative loop allowed the design team to evaluate whether specific construction changes produced meaningful improvements in design-relevant performance indicators, particularly stiffness under user-induced loading, without incurring disproportionate mass penalties.\u003c/p\u003e \u003cp\u003eImportantly, the experimentally identified loads served as a \u003cem\u003efixed reference\u003c/em\u003e throughout this process, providing a stable basis for comparing alternative design states. This role of empirical data as a decision anchor is emphasised in design research as a means of reducing ambiguity and preventing overfitting to assumed or idealised conditions (Nichols and Olewnik \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Boothby \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By maintaining consistent load inputs, design decisions could be evaluated in terms of their actual contribution to performance improvement rather than artefacts of changing assumptions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Implications for Design Quality\u003c/h2\u003e \u003cp\u003eThe integration of experimentally identified user loads into the decision-making process directly influenced the resulting design quality. Improvements in stiffness and stability were achieved primarily through informed redistribution of material and refinement of load paths, rather than through uniform scaling or conservative over-dimensioning. As a result, the final design exhibited a more favourable balance between structural performance and mass compared to the baseline configuration defined prior to load identification.\u003c/p\u003e \u003cp\u003eFrom a methodological perspective, this outcome illustrates how experimental load identification can function as a \u003cem\u003edecision-shaping mechanism\u003c/em\u003e rather than a post hoc validation step. Similar conclusions have been reported in empirical design studies, where explicit linkage between measured operational conditions and construction choices was shown to improve the efficiency and transparency of design decisions (Štrba et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Boothby \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Structural Modelling and Design Evaluation","content":"\u003cp\u003eStructural modelling was applied to quantitatively evaluate the consequences of construction decisions derived from experimentally identified user loads. In contrast to the earlier sections, which established \u003cem\u003ewhy\u003c/em\u003e and \u003cem\u003ehow\u003c/em\u003e specific design modifications were introduced, the purpose of modelling in this stage was strictly evaluative: to verify whether the selected construction changes produced measurable improvements in performance under the fixed, experimentally derived loading conditions.\u003c/p\u003e \u003cp\u003eThe modelling scope was intentionally limited to those structural components and interfaces identified in Section 5 as decisive for load transfer and global compliance. The model therefore focused on the dominant load paths activated by user-induced bending moments, while secondary geometric features with negligible influence on stiffness were simplified. This selective representation ensured that modelling results could be directly interpreted in terms of design decisions, rather than obscured by model complexity.\u003c/p\u003e \u003cp\u003eExperimentally identified load cases were applied consistently across all evaluated design variants. Boundary conditions and load application points were held unchanged, ensuring that differences in predicted deformation and stiffness could be attributed solely to construction changes rather than to altered assumptions. This controlled comparison framework enabled direct assessment of the effectiveness of individual design decisions.\u003c/p\u003e \u003cp\u003eModel outputs were evaluated using a reduced set of performance indicators aligned with the requirements defined in Section 2. These indicators included global stiffness under user-induced loading, local deformation patterns at critical joints and members, and qualitative stability-related deformation modes relevant to camera operation. Mass was tracked as a comparative attribute rather than as an optimisation objective, supporting assessment of stiffness-to-mass efficiency across design variants.\u003c/p\u003e \u003cp\u003eThe modelling results were used to confirm or reject candidate construction changes proposed in the decision-making phase. Rather than serving as a tool for automated optimisation, structural modelling functioned as a validation layer that supported decision closure by demonstrating whether targeted modifications effectively addressed the experimentally activated deformation mechanisms. This role of modelling as a decision-support instrument, rather than as a predictive end in itself, is consistent with empirical and decision-oriented design practices reported in the literature (Milberg et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Nichols and Olewnik \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Boothby \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the modelling stage provided quantitative evidence that the final design configuration offered reduced deformation and increased stability under representative user-induced loading without increases in mass. These results formed the basis for the comparative evaluation of design quality presented in the subsequent section.\u003c/p\u003e"},{"header":"6 Results: Impact on Design Quality","content":"\u003cp\u003eThis section presents the results of the design process in terms of their impact on design quality, evaluated relative to the baseline configuration defined prior to experimental load identification. The results are reported exclusively with respect to decision-relevant performance indicators and are structured to highlight the consequences of load-informed construction choices. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e provides a visual comparison between the baseline tripod configuration and the final design resulting from the load-informed decision process.\u003c/p\u003e\n\u003ch2\u003e6.1 Stiffness and Deformation Behaviour\u003c/h2\u003e\n\u003cp\u003eThe load-informed design variants exhibited a clear improvement in stiffness under experimentally identified user-induced loading. Compared to the baseline configuration, the final design showed reduced global displacement at the camera mounting interface when subjected to representative bending moments. This reduction was achieved primarily through targeted modifications of load-bearing members and joint regions activated by user-induced loads, rather than through uniform increases in cross-sectional dimensions.\u003c/p\u003e\n\u003cp\u003eLocal deformation patterns further confirmed that the dominant compliance mechanisms identified in the baseline design were effectively mitigated. In particular, deformation previously concentrated in specific joints and slender members was redistributed along more favourable load paths in the final configuration. These changes directly correspond to the experimentally observed load directionality and asymmetry, demonstrating that stiffness improvements were aligned with actual usage conditions.\u003c/p\u003e\n\u003cp\u003eFrom a structural complexity perspective, the final design did not introduce additional assembly elements relative to the baseline configuration. The load-informed solution consisted of four primary structural components, matching the baseline part count, while achieving substantially improved stiffness and deformation behavior. This confirms that the observed performance gains were not obtained through increased design complexity, but through targeted geometric and load-path refinement.\u003c/p\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e6.2 Stability under User-Induced Loading\u003c/h2\u003e\n \u003cp\u003eStability-related performance improved as a consequence of the refined load transfer characteristics. Under representative user-induced moments, the final design exhibited reduced angular displacement and more uniform support reactions compared to the baseline configuration. This behavior is indicative of increased resistance to tipping and unwanted rotational motion during camera operation.\u003c/p\u003e\n \u003cp\u003eImportantly, the observed stability improvements were not the result of increased footprint or geometric enlargement, but stemmed from construction changes that enhanced the structural response to off-axis loading. This confirms that stability gains were achieved through load-informed structural refinement rather than through conservative geometric adjustments.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e6.3 Mass Efficiency and Performance Trade-Offs\u003c/h2\u003e\n \u003cp\u003eDespite substantial improvements in stiffness and stability, the load-informed design achieved these gains with a markedly reduced structural mass. Relative to the baseline configuration, whose total mass was 1634 g, the final design exhibited a mass of 529.79 g, corresponding to a mass reduction of 67.58%.\u003c/p\u003e\n \u003cp\u003eAt the same time, the maximum structural displacement under the experimentally identified user-induced loads was reduced from 0.442 mm in the baseline configuration to 0.043 mm in the load-informed design, representing a reduction of approximately 90%. This combination of substantially reduced deformation and markedly lower mass resulted in a significantly more favourable stiffness-to-mass balance. A consolidated comparison of the baseline and load-informed designs, including mass, deformation, and structural robustness, is provided in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThese results reflect the selective nature of the applied design modifications, which introduced material only in regions directly activated by the experimentally identified load paths, while avoiding unnecessary reinforcement elsewhere. Consequently, experimentally identified user loads enabled a more precise allocation of material resources, supporting substantial performance improvements without disproportionate penalties in weight. This balance is particularly relevant for portable precision devices, where mass directly affects usability.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of baseline and load-informed tripod designs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBaseline configuration\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLoad-informed design\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRelative change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDesign implication\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStructural mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1634 g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e529.79 g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;67.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubstantial weight reduction without material substitution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum displacement under representative user load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.442 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;90.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrong reduction of deformation under operating conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum safety factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;131%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncreased robustness without conservative scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDominant load path\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiffuse, multi-member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConcentrated, load-aligned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved load transfer efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of structural components\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo increase in design complexity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaterial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAluminium PA13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAluminium PA13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerformance gain achieved without material change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManufacturing process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-axis CNC milling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-axis CNC milling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrial feasibility preserved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e6.4 Summary of Design Quality Improvements\u003c/h2\u003e\n \u003cp\u003eTaken together, the results show that integrating experimentally identified user loads into the design process led to the following improvements in design quality (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for a consolidated comparison):\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eSubstantially reduced structural deformation under representative operating conditions, indicating improved stiffness behavior when evaluated under experimentally identified user-induced loads.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eImproved stability during user interaction, reflected in more favourable deformation modes and reduced sensitivity to asymmetric loading during combined pan\u0026ndash;tilt motion.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eA markedly enhanced stiffness\u0026ndash;to\u0026ndash;mass balance, achieved through a simultaneous reduction in structural deformation and overall mass relative to the baseline configuration.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eNo increase in design complexity, as the number of structural components remained unchanged between the baseline and load-informed designs.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePreserved material and manufacturing assumptions, with performance gains achieved without material substitution or adoption of advanced manufacturing processes.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThese improvements were achieved without relying on conservative scaling strategies. Instead, they emerged from targeted construction decisions directly informed by measured operational loads, demonstrating the value of experimentally identified user loads as a decision-shaping input in precision mechanical design.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"7 Discussion","content":"\u003cp\u003eThe presented case study provides broader insight into how experimentally identified user-induced loads can influence design decision-making beyond a single application. While the results are grounded in the design of a lightweight camera tripod, the underlying methodological implications extend to a wider class of precision and manually actuated mechanical structures.\u003c/p\u003e \u003cp\u003eWhile AI-assisted generative design tools facilitated the exploration of alternative structural configurations, the present results indicate that the decisive factor for design quality improvement was the integration of experimentally identified user loads into the decision-making process, rather than the use of generative techniques per se.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Experimental Load Identification as a Design-Shaping Input\u003c/h2\u003e \u003cp\u003eThe results reinforce observations reported in the literature that experimentally identified operational loads are most valuable when they inform \u003cem\u003ewhich structural features govern performance\u003c/em\u003e, rather than when they merely refine assumed load magnitudes. Studies on load collectives and test-assisted design show that simplified or conservative load assumptions often obscure dominant load paths and lead to inefficient material allocation (Fischer \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Heuler et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In contrast, empirical load identification can reveal load directionality, asymmetry, and coupling effects that directly shape construction decisions.\u003c/p\u003e \u003cp\u003eIn the present study, experimentally identified user-induced moments highlighted specific load paths and deformation mechanisms that were not evident from baseline assumptions. This enabled targeted structural refinement rather than uniform reinforcement, consistent with prior reports in precision mechanical systems and machine structures where experimental stiffness investigations guided focused design changes (Milberg et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Darnieder et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Managing Trade-Offs between Stiffness, Stability, and Mass\u003c/h2\u003e \u003cp\u003eA recurring theme in the literature on precision structures is the trade-off between stiffness, stability, and mass under realistic operating conditions. Measurement-informed approaches have been shown to reduce unnecessary conservatism by aligning structural reinforcement with actual service loads rather than hypothetical extremes (Štrba et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Boothby \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The results presented here support this perspective: improvements in stiffness and stability were achieved primarily through load-informed redistribution of material, yielding a more favourable stiffness-to-mass balance than the baseline design.\u003c/p\u003e \u003cp\u003eThis outcome reflects a general principle applicable beyond the present case: when user-induced loads dominate functional performance, design efficiency depends less on increasing overall structural capacity and more on understanding how operating conditions activate specific compliance mechanisms. Similar conclusions have been reported in studies of portable devices and manually actuated systems, where empirical load characterization enabled mass reduction without compromising usability or stability (Boothby \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough cost was not treated as a primary optimisation objective, it is noteworthy that the final load-informed design remained comparable in manufacturing cost to commercially available baseline solutions. The improved stiffness\u0026ndash;mass balance was therefore achieved without resorting to expensive materials or manufacturing processes, reinforcing the practical relevance of the proposed decision-oriented design approach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Role of Modelling in Empirically Informed Design Workflows\u003c/h2\u003e \u003cp\u003eThe study also illustrates a broader methodological role of structural modelling in empirically informed design processes. Rather than serving as a tool for exhaustive prediction or formal optimisation, modelling was used here to compare alternative design states under fixed, experimentally derived load cases. This comparative use of models aligns with design decision-making frameworks that emphasize transparency, interpretability, and support for decision closure (Lu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Nichols and Olewnik \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrior work shows that such modelling strategies are particularly effective when empirical data constrain the design space, allowing relatively simple models to deliver high decision value (Štrba et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Boothby \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The present results confirm that meaningful improvements in design quality can be achieved without resorting to complex probabilistic or data-driven modelling, provided that the applied loads are representative of actual use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e7.4 Generalisability and Variability of User-Induced Loads\u003c/h2\u003e \u003cp\u003eAs noted in earlier studies, user-induced operational loads exhibit inherent variability due to differences in user behavior, configuration, and context of use (Fischer \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Johannesson, P and Speckert, M. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Consequently, the specific load magnitudes identified in this study should not be interpreted as universally applicable. Instead, they represent a limited but informative sample of realistic operating conditions.\u003c/p\u003e \u003cp\u003eThe transferable contribution of this work therefore lies in the \u003cem\u003eprocess\u003c/em\u003e rather than in the numerical values of the identified loads. The combination of experimental load identification, decision-focused modelling, and comparative evaluation provides a generalizable framework that can be adapted to other precision and manually actuated structures. This process-oriented perspective is consistent with calls in the design research literature for greater transparency in how empirical data influence construction decisions (Nichols and Olewnik \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e7.5 Implications for Design Research\u003c/h2\u003e \u003cp\u003eFrom a design research standpoint, this study contributes a documented example of how experimentally identified operational loads can be translated into construction decisions and evaluated in terms of design quality. By explicitly tracing the pathway from empirical measurement to structural modification and performance outcome, the work addresses a gap identified in prior studies concerning the visibility of decision pathways in engineering design (Boothby \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore broadly, the findings support the view that improvements in design quality arise not from the isolated application of experimental methods or modelling tools, but from their integration into coherent, decision-oriented design processes. This insight is applicable across domains where user interaction plays a central role in structural loading and performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"8 Conclusions","content":"\u003cp\u003eThis study examined how experimentally identified user-induced loads influence design decisions and design quality in the development of a lightweight camera tripod. By embedding empirical load identification into the design process, the work demonstrates a clear and traceable pathway from measured operating conditions to construction decisions and performance outcomes.\u003c/p\u003e \u003cp\u003eThe results show that experimentally identified user loads provide decision-relevant information that is not captured by baseline engineering assumptions alone. In the investigated case, empirical load data revealed dominant bending effects and asymmetric load paths activated during typical camera operation. When explicitly translated into construction decisions, these insights enabled targeted structural modifications that improved stiffness and stability under representative operating conditions.\u003c/p\u003e \u003cp\u003eImportantly, these performance improvements were achieved alongside a substantial reduction in structural mass, resulting in a markedly improved stiffness-to-mass balance relative to the baseline design. This outcome illustrates that realistic load characterization can support more efficient allocation of material resources, avoiding conservative over-dimensioning while maintaining functional performance in portable precision devices.\u003c/p\u003e \u003cp\u003eFrom a methodological perspective, the study confirms that experimental load identification can function as a decision-shaping element rather than solely as a post hoc validation step. When combined with decision-focused structural modelling and comparative evaluation under fixed load conditions, empirical data directly support decision closure and improve the transparency of the design process.\u003c/p\u003e \u003cp\u003eWhile the numerical load values reported here are specific to the investigated configuration and user sample, the transferable contribution of this work lies in the demonstrated process. The integration of experimentally identified loads, decision-oriented modelling, and performance-based comparison provides a generalizable framework for improving design quality in precision and manually actuated mechanical structures.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eConceptualization and study design were led by both authors. Experimental investigations, data acquisition, and practical design work were carried out by Jakub Duczmalewski as part of his master’s thesis. Data analysis and interpretation were performed jointly by Jakub Duczmalewski and Szymon Cygan. The original draft of the manuscript was prepared by Szymon Cygan, with contributions to technical content from Jakub Duczmalewski. Manuscript review, editing, and final approval were conducted by both authors.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAugustine P, Hunter T, Sievers N, Guo X (2016) Load Identification of a Suspension Assembly Using True-Load Self Transducer Generation. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4271/2016-01-0429\u003c/span\u003e\u003cspan address=\"10.4271/2016-01-0429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. SAE Tech Pap\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoothby T (2023) Empirical Design in Structural Engineering. ICE Publishing\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrasil RMLRF (2019) Dynamic analysis of unbalanced rotary machine support structures considering geometric stiffness. In: Proceedings of the International Conference on Structural Engineering\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarnieder M, Marangoni RR, Theska R, Rahneberg I (2017) Contribution to the mechanical enhancement of load cells in precision weighing technology. In: Proceedings of the European Society for Precision Engineering and Nanotechnology (EUSPEN). Hannover\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischer W (2008) Load assumptions, load collectives, dimensioning and component testing. Mater Test 50:447\u0026ndash;450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3139/120.100899\u003c/span\u003e\u003cspan address=\"10.3139/120.100899\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabbert U, Zehn M, Wah F (1995) Improved results in structural dynamic calculations by linking FEA and experimental modal analysis. In: ASME Design Engineering Technical Conference. pp 1321\u0026ndash;1328\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGe P, Wang N, Lu SC-Y (2002) An evolutionary modeling approach for automotive bumper system design and analysis. J Comput Inf Sci Eng. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1115/1.1529212\u003c/span\u003e\u003cspan address=\"10.1115/1.1529212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGray A, Wimbush A, de Angelis M, Rocchetta R (2022) From inference to design: A comprehensive framework for uncertainty quantification. Mech Syst Signal Process. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ymssp.2021.108210\u003c/span\u003e\u003cspan address=\"10.1016/j.ymssp.2021.108210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeuler P, Frost M, Rochlitz H (2011) Approaches of load assumption for durability assessment of automotive structure. Mater Werkst. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/mawe.201100762\u003c/span\u003e\u003cspan address=\"10.1002/mawe.201100762\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeuler P, Kl\u0026auml;tschke H (2005) Generation and use of standardised load spectra and load\u0026ndash;time histories. Int J Fatigue 27:974\u0026ndash;990. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijfatigue.2004.09.012\u003c/span\u003e\u003cspan address=\"10.1016/j.ijfatigue.2004.09.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeon SK, Murphey TW (2012) Fundamental design of tensioned precision deployable space structures. In: AIAA/ASME Structures, Structural Dynamics and Materials Conference\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia X, Sedehi O, Papadimitriou C et al (2022) Hierarchical Bayesian modeling framework for model updating and robust predictions in structural dynamics using modal features. Mech Syst Signal Process 170:108784. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ymssp.2021.108784\u003c/span\u003e\u003cspan address=\"10.1016/j.ymssp.2021.108784\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohannesson P, Speckert M (eds) (2013) Load Variation and Reliability. Guide to Load Analysis for Durability in Vehicle Engineering. Wiley, Ltd, pp 253\u0026ndash;284\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalaycıoğlu T, \u0026Ouml;zg\u0026uuml;ven HN (2014) Nonlinear structural modification and nonlinear coupling. Mech Syst Signal Process. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ymssp.2014.01.016\u003c/span\u003e\u003cspan address=\"10.1016/j.ymssp.2014.01.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu R, Dobriban E, Hou Z, Qian K (2022) Dynamic Load Identification for Mechanical Systems: A Review. Arch Comput Methods Eng. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11831-021-09594-7\u003c/span\u003e\u003cspan address=\"10.1007/s11831-021-09594-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu SC-Y, Ge P, Wang N (1998) Application of an evolutionary modeling approach to automotive bumper system design. In: ASME Design Engineering Technical Conference\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelnikov A, Soal K, Bienert J (2017) Determination of static stiffness of mechanical structures from operational modal analysis. In: International Operational Modal Analysis Conference (IOMAC). Ingolstadt\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeruane V, Heylen W (2011) An hybrid real genetic algorithm to detect structural damage using modal properties. Mech Syst Signal Process. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ymssp.2010.11.020\u003c/span\u003e\u003cspan address=\"10.1016/j.ymssp.2010.11.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilberg J, Eibelshaeuser P, Kirchknopf P (1986) Comments on the Dynamic Behaviour of Machine Tools. Experimental Determination of Stiffness of Rigid Joint Connections. VDI-Z 128:161\u0026ndash;166\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson DV, Fuchs HO (1975) Predictions of cumulative fatigue damage using condensed load histories. SAE Tech Pap. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4271/750045\u003c/span\u003e\u003cspan address=\"10.4271/750045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNichols A, Olewnik A (2012) A pilot study of engineering design-decision methods in practice. In: ASME Design Engineering Technical Conference. ASME, Chicago, Illinois, USA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerera R, Ruiz A (2008) A multistage FE updating procedure for damage identification in large-scale structures. Mech Syst Signal Process. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ymssp.2007.10.004\u003c/span\u003e\u003cspan address=\"10.1016/j.ymssp.2007.10.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠtrba M, Karmaz\u0026iacute;nov\u0026aacute; M, Bukovsk\u0026aacute; P (2020) Experiences with using of loading tests and the design assisted by testing method. Int J Mech\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWittke M, Darnieder M, Fr\u0026ouml;hlich T, Theska R (2024) Enhanced stiffness characterization of load cells by relative change of the natural frequency. Tech Mess. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/teme-2024-0087\u003c/span\u003e\u003cspan address=\"10.1515/teme-2024-0087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Design decision-making, Experimental load identification, User-induced loads, Precision mechanical design, Design quality evaluation, Test-assisted design","lastPublishedDoi":"10.21203/rs.3.rs-8483808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8483808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDesign decisions in precision mechanical systems are often based on assumed or simplified load cases, even though user interaction can dominate structural response during operation. While experimental load identification is widely used for validation, its role in shaping design decisions remains insufficiently documented in design research. This paper investigates how experimentally identified user-induced loads influence design decisions and design quality, using the development of a precision camera tripod as a case study.\u003c/p\u003e \u003cp\u003eUser-induced loads were experimentally identified during representative camera operation and translated into bending moments acting on the structure. These empirically derived loads were then used as fixed inputs in a decision-oriented design process, guiding targeted construction modifications. Structural modelling was employed in a comparative manner to evaluate alternative design variants under consistent load conditions.\u003c/p\u003e \u003cp\u003eThe results show that integrating experimentally identified user loads enabled more focused design decisions, leading to improved stiffness and stability under representative operating conditions without disproportionate increases in mass. Rather than driving global reinforcement, empirical load information revealed dominant load paths and deformation mechanisms, supporting selective structural refinement.\u003c/p\u003e \u003cp\u003eBeyond the specific case, the study demonstrates a transferable process for embedding experimental load identification into design decision-making. The findings highlight that the primary value of empirical load data lies not in validation alone, but in reducing ambiguity and improving the quality and transparency of design decisions in precision mechanical design.\u003c/p\u003e","manuscriptTitle":"From Experimentally Identified User Loads to Design Decisions: A Case Study of a Precision Camera Tripod","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-05 09:58:27","doi":"10.21203/rs.3.rs-8483808/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6dc10f8-d2ae-40bf-ab4c-7c0957a82746","owner":[],"postedDate":"January 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-08T11:39:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-05 09:58:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8483808","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8483808","identity":"rs-8483808","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}