A Bibliometric Analysis of the Applications of Artificial Intelligence Techniques to Engineering Design

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Traditional processes are no longer effective in solving engineering problems. This study systematically examines scientific publications in the field of selected AI techniques in engineering design in various applications, such as deep learning, machine learning, and simulation. This mini-review also adopted a PRISMA framework-based methodology to extract data from the Scopus database and, then analyzed such data to identify research gaps and future directions in AI’s application to engineering design. A bibliometric analysis of AI in Engineering design was conducted to examine its role in engineering design, interdisciplinary collaboration, geographic distribution, and research focus. This study identifies critical research gaps and offers recommendations for future directions in AI applications for engineering design. The findings provide valuable insights into the state of the art and showcase valuable information for stakeholders to tune into the advances in AI techniques to enhance traditional engineering design processes." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/15-619", "name": "A Bibliometric Analysis of the Applications of Artificial Intelligence..." } } ] } Home Browse A Bibliometric Analysis of the Applications of Artificial Intelligence... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Nurudeen AH and Sada AY. A Bibliometric Analysis of the Applications of Artificial Intelligence Techniques to Engineering Design [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :619 ( https://doi.org/10.12688/f1000research.179509.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Review A Bibliometric Analysis of the Applications of Artificial Intelligence Techniques to Engineering Design [version 1; peer review: 1 not approved] Abdulhakeem Hassan Nurudeen https://orcid.org/0000-0002-9624-8224 1 , Abdullahi Yusuf Sada 2 Abdulhakeem Hassan Nurudeen https://orcid.org/0000-0002-9624-8224 1 , Abdullahi Yusuf Sada 2 PUBLISHED 24 Apr 2026 Author details Author details 1 Mechanical Engineering, University of Abuja Faculty of Engineering, Abuja, Federal Capital Territory, Nigeria 2 Electrical and Computer Engineering, Baze University, Abuja, Federal Capital Territory, Nigeria Abdulhakeem Hassan Nurudeen Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Abdullahi Yusuf Sada Roles: Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. Abstract In the era of technological sophistication, advancements and integration of artificial intelligence (AI) techniques in engineering design are revolutionizing numerous fields. Traditional processes are no longer effective in solving engineering problems. This study systematically examines scientific publications in the field of selected AI techniques in engineering design in various applications, such as deep learning, machine learning, and simulation. This mini-review also adopted a PRISMA framework-based methodology to extract data from the Scopus database and, then analyzed such data to identify research gaps and future directions in AI’s application to engineering design. A bibliometric analysis of AI in Engineering design was conducted to examine its role in engineering design, interdisciplinary collaboration, geographic distribution, and research focus. This study identifies critical research gaps and offers recommendations for future directions in AI applications for engineering design. The findings provide valuable insights into the state of the art and showcase valuable information for stakeholders to tune into the advances in AI techniques to enhance traditional engineering design processes. READ ALL READ LESS Keywords Artificial Intelligence, Engineering Design, Machine Learning, Simulation Corresponding Author(s) Abdulhakeem Hassan Nurudeen ( [email protected] ) Close Corresponding author: Abdulhakeem Hassan Nurudeen Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Nurudeen AH and Sada AY. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Nurudeen AH and Sada AY. A Bibliometric Analysis of the Applications of Artificial Intelligence Techniques to Engineering Design [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :619 ( https://doi.org/10.12688/f1000research.179509.1 ) First published: 24 Apr 2026, 15 :619 ( https://doi.org/10.12688/f1000research.179509.1 ) Latest published: 24 Apr 2026, 15 :619 ( https://doi.org/10.12688/f1000research.179509.1 ) 1. Introduction Traditional approaches to Engineering Design (ED) processes are based on intuition involving scientific, experimental, creative methods, and expert knowledge of the process. For instance, traditional process planning is executed primarily based on experience in creating products using machine tools. 1 , 2 Recent advancements and the rapid adoption of artificial intelligence approaches have simplified and revolutionized engineering design. 3 Artificial Intelligence (AI) is known as “using computer algorithms to imitate biotic mental processes or activities” 4 to execute tasks such as learning about processes, understanding, estimating, problem-solving and decision-making in several areas of engineering applications that cut across all disciplines. The adoption of this novel methodology transforms how processes are designed and executed in the shortest possible time, with the least cost, precise solutions and further completion rates than generic human approaches. 5 The desire for better solutions to challenging world problems has led engineers to push for optimal solutions using modern technologies to conceptualize, evaluate, and compare many candidate solutions without the need for physical prototypes, 6 using computational tools and Computer-Aided Design (CAD) systems. 7 These systems have evolved significantly with their sophisticated modelling, simulation and analysis capabilities. 8 AI has further transformed these systems by introducing automation, decision-making support and advanced analytic support through Machine Learning (ML), Neural Networks (NN), and Generic Algorithms (GA), which are now integrated into CAD systems, making it possible to explore and execute complex and data-driven tasks. 9 , 10 This study provides a comprehensive review of the development and applications of AI in Engineering Design over three broad clusters of application: AI technologies in the ED, Engineering Design Methodologies and their practical applications across engineering. To highlight the impact of AI, this study reviewed several studies using AI techniques approximately 1653 studies were found between 1970–2025, a filter was used to narrow the review to between 2010–2025 which reduced the number to 142, on subject areas limited to engineering, energy and chemical engineering showed 763 results from articles, conference papers, reviews, conference reviews etc, and the keyword search showed 5962 with a minimum of five occurrences resulting in over 316 keywords:- engineering design, artificial intelligence, deep learning, machine learning, product design, design, optimization, decision support systems etc., as shown in Figure 1 . Figure 1. Presents the keyword search by occurrence. In Figure 1, the magnified red circumference denotes the dominance of the 'engineering design' search index, substantively outperforming concurrent keywords such as computer-aided design and machine learning frameworks. 2. Methodology 2.1 Bibliometric analysis An in-depth bibliometric examination was conducted, focusing on AI within the engineering design framework. The current research and development landscape in AI and Engineering Design has been analyzed through a wide range of articles and reviews. This approach allows researchers to examine trends, interconnections and trends among scholarly publications, providing a deeper exploration of research themes and collaborative networks within the field. Through analysis of citation patterns, co-authorship relations and keyword frequencies, among others. In this study, co-citation, co-authorship, and co-author maps are used as methods to highlight the relationships between studies, authors and topics. The VOS viewer software was utilized to automatically generate occurrences and co-occurrence matrices, clustering of related research and similarity measures, offering a detailed map of how AI is integrated into engineering design. 11 2.2 Literature retrieval This step involved the identification of relevant search terms and keywords to search for relevant literature related to the chosen subject area. The selection of relevant publications within AI and Engineering design from the Scopus database is due to its distinction in comprehensive coverage of wide academic articles and publications with high quality, accessibility and global reach among other features. This search was achieved through set of keywords “Artificial Intelligence”, “Machine Learning” and “Engineering Design” with a focus on title, abstract and keyword search, resulting in the compilation of 1653 papers between 1976–2025. A search filter was applied, which reduced the number of papers compiled to 1142 and publication year from to 2010–2025. Table 1 presents the breakdown of the top searches, average publication year, citations and occurrence of keywords. Table 1. Breakdown of the top 30 searches. Table 1 presents the breakdown of the top searches, average publication year, citations and occurrence of keywords. The Table was extracted from the VOS Viewer software. It shows the keyword search and the research output in the field. Label Links Total Link Strength Occurrences Avg. Pub. Year Avg. Citations Avg. Norm. Citations Engineering Design 112 998 226 2021 17.6239 0.9175 Artificial Intelligence 107 975 220 2020 46.2818 1.1718 Machine Learning 104 869 184 2022 22.9076 1.1599 Machine-Learning 98 670 129 2023 9.2636 0.9828 Engineering Education 80 432 81 2021 17.3704 0.612 Design 91 359 77 2018 17.7792 0.9384 Learning Systems 98 422 76 2021 30.75 0.9603 Product Design 75 313 62 2020 14.9032 1.2555 Optimization 73 276 53 2020 108.8491 1.6613 Students 52 296 52 2020 9.2308 0.4247 Deep Learning 78 244 48 2022 36.4792 0.9726 Computer Aided Design 77 318 46 2021 25.3913 0.8925 Learning Algorithms 82 268 44 2021 14.6364 0.9708 Machine Design 78 237 39 2023 9.4103 0.8795 Forecasting 51 182 38 2022 26.5263 1.4376 Neural Networks 70 224 38 2021 41.1842 1.4366 Curricula 45 215 37 2020 8.2973 0.398 Decision Making 79 194 37 2020 54.8919 1.1479 Optimisations 61 183 31 2022 67.7742 1.1182 Teaching 42 177 29 2019 15.5517 0.555 Engineering Design Process 54 143 28 2018 20.1429 1.0021 Performance 50 124 28 2023 78.3929 1.3744 Benchmarking 52 152 27 2022 192.2963 2.5121 Iterative Methods 61 145 25 2021 12.76 0.8013 Data Driven 58 133 24 2023 10.9583 1.0108 Genetic Algorithms 59 139 24 2020 23.7083 1.0071 Data-Driven Design 61 182 23 2023 22.4348 1.3811 Optimization Algorithms 38 123 23 2022 150.7826 2.1106 Computer-Aided Design 53 162 22 2022 14.4091 0.8236 Decision Support Systems 39 97 22 2017 18.5455 1.0247 Figure 2 shows the research output on engineering design and AI by country, showing that the United States is the leading country, followed by China and the United Kingdom in research output based on engineering design-induced AI. Figure 2. Bibliometric distribution of research output by country. The analysis identifies the United States (represented by the pink node) and China (represented by the blue node) as the leading contributors to the global literature on engineering design and AI frameworks. From Table 2 , there are over 200 documents from the United States with China having 141 output per annum and the United Kingdom as the third largest in research projects on AI-based Engineering Design. Table 2. Top 20 countries by research output on engineering design and AI. Table 2 presents the top 20 countries that engage in research related to engineering design and artificial intelligence techniques. Country Documents Citations Total Link Strength United States 227 1 7633 China 141 16 9731 United Kingdom 45 450 2680 India 34 213 4853 Canada 32 3 2144 Australia 31 33 3132 Germany 22 209 331 Singapore 18 7 2158 Italy 16 1749 876 Malaysia 16 1 1573 Turkey 16 564 1253 Iran 15 942 2714 Saudi Arabia 15 17 3841 Hong Kong 14 0 1232 South Korea 14 8 2357 Taiwan 14 4227 1861 Spain 13 1210 1123 Netherlands 11 0 495 France 10 15 449 Japan 10 52 639 Figure 3 and Table 3 . the study identifies key disciplinary specializations and institutional adopters of AI-based engineering design. The visualization confirms the MIT Department of Mechanical Engineering as the preeminent institution in this field. Supporting data in Table 3 shows the scholarly impact through metrics such as document frequency, citation impact, and inter-institutional link strengths. Figure 3. Institutional distribution of global research output. The visualization highlights the departmental origins of the research data, where the MIT Department of Mechanical Engineering is identified as a dominant cluster. The magnitude of its index reflects the institution's significant volume of scholarly contributions to the analyzed domain. Table 3. Bibliometric performance summary: Comparative analysis of publication output, citation impact, and relational link strength. Table 3 shows the institutional distribution of research papers, highlighting the specific thematic focus of various organizations on the application of AI methodologies within the engineering design domain. Organization Documents Citations Total Link Strength Department Of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, 02139, Ma, United States 8 236 914 Department Of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, 15213, Pa, United States 5 55 587 Massachusetts Institute of Technology, Cambridge, Ma, United States 5 12 273 Department Of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pa, United States 4 30 1148 Department Of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, Ma, United States 3 27 325 Department Of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Ma, United States 3 47 343 School Of Civil Engineering, Shenyang Jianzhu University, Shenyang, China 3 22 598 School Of Civil Engineering, Southeast University, Nanjing, China 3 12 504 Singapore University of Technology And Design, Singapore 3 35 359 The University of New South Wales, Kensington, 2052, Nsw, Australia 3 51 177 Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan 2 22 1926 C-Core, 1 Morrissey Rd, St. John’s, A1b 3x5, Nl, Canada 2 191 256 Centre For Artificial Intelligence Research and Optimisation, Torrens University Australia, Australia 2 2999 1088 Colegio De Ciencias E Ingeniería, Universidad San Francisco De Quito, Quito, Ecuador 2 5 684 College Of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China 2 10 436 College Of Computer Science and Artificial Intelligence, Wenzhou University, Zhejiang, Wenzhou, 325035, China 2 46 1681 College Of Computer Science and Technology, Changchun Normal University, Jilin, Changchun, 130032, China 2 46 1681 Data-Driven Innovation Lab, Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore 2 17 523 Department Of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, 24060, Va, United States 2 99 100 Department Of Civil Engineering, Istanbul University-Cerrahpaşa, Istanbul, 34320, Turkey 2 28 411 3. Overview on AI technologies in engineering design 3.1 Machine Learning (ML) Machine learning is one of the most prominent AI technologies used in ED, and it helps with the optimization of designs and analysis of large datasets and patterns through a systematic framework to manage all engineering design activities, thereby increasing operations efficiency. 12 “Engineering design is a broad field encompassing domains of Government, Industry, technology, education, social science and practical use”. 13 The current capabilities of AI with large language models (LLM) such as Generative Pre-trained Transform Models (GPT) have demonstrated ways of sharing knowledge through conversations. For example, OpenAI’s ChatGPT, which explores a vast repository of information and interactive response format, is now enhancing traditional design processes. 5 , 14 – 16 3.2 Deep learning This branch of AI mimics the human brain and enhances its ability to handle complex design tasks through in-depth data training. 17 It also plays a crucial role in generating design solutions, predictions and performance analysis, which are achieved through computer vision from images and videos, text, and sound, which enables designers to identify and solve problems with high efficiency and quality. 18 Deep learning algorithms process and analyze a large amount of data using the neural network structure of the human brain with high accuracy and efficiency. 19 , 20 These features enable deep learning to be robust in navigating through large datasets, offering a transformative advantage to designers and engineers to improve designs through a pre-emptive approach in the design process. 13 In manufacturing processes, deep learning is essential for identifying usability issues or designing defects in manufactured products. Deep learning uses historical data on similar products, customer feedback, claims and production ledgers to identify products that are likely to cause customer dissatisfaction or increase production costs. 21 Deep learning also offers an innovative and dynamic approach to design by analyzing datasets to produce novel designs that may not be obvious, thus inspiring designers to adopt new concepts and solutions. 13 , 22 The integration of AI-driven techniques into the design process has paved the way for a collaborative and iterative approach to development where all stakeholders exploit the deep learning methodology to make data-driven decisions that optimize performance, user experience, and ease of manufacturing. Moreover, as these technologies evolve, their impact in engineering design will eventually grow, guiding the development of more intelligent, reactive and robust designs. 15 The evolution of AI into design has provided powerful tools for analyzing data, identifying hitches and providing innovative solutions to solve challenging problems. 3.3 Simulation AI-driven simulation techniques are shaping the way engineering design is performed, from manufacturing processes to product design and systems that are transforming the world. These tools have become the latest norm as they provide dynamic solutions to fluid dynamics through computational fluid dynamics (CFD), finite element analysis (FEA) and manufacturing process simulation. 20 The CFD technology can simulate the flow of gases and liquids in the design, and FEA predict the reactions of materials under forces, while manufacturing process simulators showcase the manufacturing processes, supply chain and other process models. Simulation forecasts the behaviors of systems and optimizes parameters to look for desired outcomes before embarking on real plant production and design to save costs, time, and resources. 11 , 23 , 24 4. Specific AI applications to engineering design To showcase the application domains of AI techniques to ED, the following section provides several situations backed by the research conducted: Mechanical Engineering: One of the main areas that has revolutionised traditional design is Computer-Aided Design (CAD) systems. Since its introduction in the sixties (1960) the use of computer technology has helped designers to enhance their work. 25 From traditional two-dimensional drawing tools to complex three-dimensional modelling, simulation and optimization systems. 26 CAD technology has become essential in various fields such as engineering, mechanical design, automotive, and industrial design. 27 AI has transformed CAD systems with tools for automation, decision-making support and advanced analytical capabilities through machine learning, neural networks and generic algorithms, which are integrated into CAD software to handle complex and data-driven tasks. 28 AI technology has improved CAD systems and predicted possible problems during design and proffer solutions, which subsequently reduces the uncertainties in the design processes, and increases reliability and efficiency. 27 , 29 AI algorithms have also boosted the capabilities of CAD systems to generate generative design, offering multiple design solutions that suits different situations. Through this approach, numerous design possibilities, optimizations of functionalities and visual appeals have become achievable. 7 , 30 Another challenging task is the design of mechanical mechanisms subjected to complex physical constraints. 26 Understanding the relationship between motion and mechanism design is essential because of the non-linear characteristics between the mechanism classes and boundary conditions. 26 When solved analytically, an in-depth knowledge of the mechanism is required. AI has helped designers to optimize these designs through simulations and data-driven methods to train data that will provide new designs with little calculation. 31 Several AI techniques, such as conditional adversarial networks, natural-language models and autoencoder networks are used for the synthesis of mechanisms, creation of multibody simulation codes or models for rapid prototyping, and representation of target paths in a compressed lower-dimensional feature space. 28 , 31 – 37 5. Conclusions and future directions The evolution of AI and its applications is finding ground in various fields, and is not limited to engineering design. In engineering design, AI is utilized for several purposes, such as CAD systems to automate tasks based on predefined rules, integrating advanced techniques that integrate graphical, verbal and gestural inputs into refined design concepts, predictive analytics and real-time monitoring to enhance systems. Data transformation is also an area that is critical in engineering design, because of its scarcity, high cost of acquisition and limited access during operations. AI techniques help in developing minimal datasets that can address such issues showcasing significant variations in system responses with small parameter changes making simulations practical, and to setting benchmarks to predict system performance and requirements of data-driven models. The integration of AI techniques also enhances design optimization, as it offers sophisticated tools to aid in rapid design concepts and reduced lead times to produce new products in the market. These techniques have demonstrated their versatility in all aspects of business, manufacturing, design and life, particularly in engineering design where AI is transforming computer software to make data-driven decisions, and simulate intelligent behaviors to enhance efficiency and product quality. In conclusion, the rapid evolution of digital ecosystem AI in engineering design has the potential to drive the engineering industry and provide systematic and dynamic capabilities to transform traditional engineering design processes into enhanced capabilities. Data availability AI Techniques in “Engineering Design Review Data”, Mendeley Data, V1, doi: 10.17632/849fxw2s8d.1 . 38 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). 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Publisher Full Text 32. Yim NH, Ryu J, Kim YY: Big data approach for synthesizing a spatial linkage mechanism. 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2023; pp. 7433–7439. 33. Röder B, Ebel H, Eberhard P: Towards intelligent design assistants for planar multibody mechanisms. PAMM. 2023; 23 (4): e202300060. Publisher Full Text 34. Lee S, Kim J, Kang N: Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions. arXiv preprint arXiv:2402.14882. 2024. 35. Gerstmayr J, Manzl P, Pieber M: Multibody models generated from natural language. Multibody Syst. Dyn. 2024; 62 (2): 249–271. Publisher Full Text 36. Nurizada A, Purwar A: Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation. J. Comput. Inf. Sci. Eng. 2024; 24 (1). Publisher Full Text 37. Deshpande S, Purwar A: A machine learning approach to kinematic synthesis of defect-free planar four-bar linkages. J. Comput. Inf. Sci. Eng. 2019; 19 (2): 021004. Publisher Full Text 38. Hassan NA, Sada AY: AI Techniques in Engineering Design Review Data. Mendeley Data. 2026; V1 . Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 24 Apr 2026 ADD YOUR COMMENT Comment Author details Author details 1 Mechanical Engineering, University of Abuja Faculty of Engineering, Abuja, Federal Capital Territory, Nigeria 2 Electrical and Computer Engineering, Baze University, Abuja, Federal Capital Territory, Nigeria Abdulhakeem Hassan Nurudeen Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Abdullahi Yusuf Sada Roles: Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 24 Apr 2026, 15:619 https://doi.org/10.12688/f1000research.179509.1 Copyright © 2026 Nurudeen AH and Sada AY. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Nurudeen AH and Sada AY. A Bibliometric Analysis of the Applications of Artificial Intelligence Techniques to Engineering Design [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :619 ( https://doi.org/10.12688/f1000research.179509.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 24 Apr 2026 Views 0 Cite How to cite this report: Sarfarazi S. Reviewer Report For: A Bibliometric Analysis of the Applications of Artificial Intelligence Techniques to Engineering Design [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :619 ( https://doi.org/10.5256/f1000research.198030.r482991 ) The direct URL for this report is: https://f1000research.com/articles/15-619/v1#referee-response-482991 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 12 May 2026 Sina Sarfarazi , University of Naples “Federico II”, Naples, Italy Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.198030.r482991 Comments 1. The review methodology is not sufficiently consistent. The abstract states that a PRISMA-based methodology was used, but the manuscript does not show a proper PRISMA flow diagram or a traceable screening process. The ... Continue reading READ ALL Comments 1. The review methodology is not sufficiently consistent. The abstract states that a PRISMA-based methodology was used, but the manuscript does not show a proper PRISMA flow diagram or a traceable screening process. The reported numbers are also confusing: the text mentions 1653 papers from 1970–2025, then filtering to 2010–2025, then 142 papers, 763 results by subject area, and 5962 keyword search results. Later, the methodology says the filter reduced the dataset to 1142 papers. These numbers must be reconciled. Without a clear record-selection process, the bibliometric results cannot be verified. 2. The bibliometric analysis is too shallow. VOSviewer is used to generate keyword, country, and institutional maps, but the interpretation is limited. For example, Table 1 lists keyword occurrences and citations, but the paper does not explain what the clusters mean for engineering design research. It should interpret why “engineering design,” “artificial intelligence,” “machine learning,” “optimization,” “benchmarking,” and “data-driven design” form certain clusters and how these clusters connect to design tasks. The current analysis mostly reports what is visible in the figures and tables. 3. There are serious issues in the country and institutional data. Table 2 reports the United States with 227 documents but only 1 citation, while China has 141 documents and 16 citations. These values are not credible for a Scopus-based bibliometric dataset unless the citation column is mislabeled or incorrectly extracted. The text also states that China has “141 output per annum,” which is not what the table shows. Table 3 contains duplicate institutional entries for MIT and Carnegie Mellon under slightly different names. The authors need to clean affiliation data and verify all bibliometric fields before drawing conclusions. 4. The figures are not fitting in criteria of journal standard quality for a bibliometric paper. The VOSviewer maps are crowded and difficult to read, especially the keyword and institutional maps. The figures also need clearer legends, cluster labels, minimum occurrence thresholds, and interpretation of node size and link strength. At present, the figures look like raw software outputs rather than carefully prepared scientific evidence. 5. The technical discussion of AI methods is too basic. Sections on machine learning, deep learning, and simulation mostly provide generic descriptions. They do not critically discuss engineering design tasks such as topology optimization, generative design, inverse design, CAD/CAE automation, design-space exploration, surrogate modeling, uncertainty-aware design, or human-AI co-design. This is a major weakness because the paper is supposed to review AI in engineering design, not AI in general. 6. The specific application section is underdeveloped. The manuscript briefly discusses CAD, generative design, and mechanism synthesis, but it does not provide a structured comparison across engineering fields. Mechanical, civil, aerospace, manufacturing, and product design should not be mixed without a framework. The authors should classify applications by design stage, AI method, data source, validation approach, and engineering output. This would make the paper useful to readers instead of only giving a broad overview. 7. The conclusions are too general and are not strongly supported by the bibliometric analysis. Statements such as AI transforming engineering design, reducing lead time, and improving product quality may be true, but the paper does not quantify these claims using the reviewed corpus. The conclusion should instead report evidence-based findings from the dataset: dominant keywords, growth trends, leading countries/institutions, method clusters, and underdeveloped research directions. Is the topic of the review discussed comprehensively in the context of the current literature? No Are all factual statements correct and adequately supported by citations? No Is the review written in accessible language? Partly Are the conclusions drawn appropriate in the context of the current research literature? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Intersection of computational mechanics and applied AI, finite element modelling (Abaqus), structural verification, and developing Python-based automation for simulations, post-processing, and reporting. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Sarfarazi S. Reviewer Report For: A Bibliometric Analysis of the Applications of Artificial Intelligence Techniques to Engineering Design [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :619 ( https://doi.org/10.5256/f1000research.198030.r482991 ) The direct URL for this report is: https://f1000research.com/articles/15-619/v1#referee-response-482991 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 24 Apr 2026 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 24 Apr 26 read Sina Sarfarazi , University of Naples “Federico II”, Naples, Italy Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Sarfarazi S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 May 2026 | for Version 1 Sina Sarfarazi , University of Naples “Federico II”, Naples, Italy 0 Views copyright © 2026 Sarfarazi S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Comments 1. The review methodology is not sufficiently consistent. The abstract states that a PRISMA-based methodology was used, but the manuscript does not show a proper PRISMA flow diagram or a traceable screening process. The reported numbers are also confusing: the text mentions 1653 papers from 1970–2025, then filtering to 2010–2025, then 142 papers, 763 results by subject area, and 5962 keyword search results. Later, the methodology says the filter reduced the dataset to 1142 papers. These numbers must be reconciled. Without a clear record-selection process, the bibliometric results cannot be verified. 2. The bibliometric analysis is too shallow. VOSviewer is used to generate keyword, country, and institutional maps, but the interpretation is limited. For example, Table 1 lists keyword occurrences and citations, but the paper does not explain what the clusters mean for engineering design research. It should interpret why “engineering design,” “artificial intelligence,” “machine learning,” “optimization,” “benchmarking,” and “data-driven design” form certain clusters and how these clusters connect to design tasks. The current analysis mostly reports what is visible in the figures and tables. 3. There are serious issues in the country and institutional data. Table 2 reports the United States with 227 documents but only 1 citation, while China has 141 documents and 16 citations. These values are not credible for a Scopus-based bibliometric dataset unless the citation column is mislabeled or incorrectly extracted. The text also states that China has “141 output per annum,” which is not what the table shows. Table 3 contains duplicate institutional entries for MIT and Carnegie Mellon under slightly different names. The authors need to clean affiliation data and verify all bibliometric fields before drawing conclusions. 4. The figures are not fitting in criteria of journal standard quality for a bibliometric paper. The VOSviewer maps are crowded and difficult to read, especially the keyword and institutional maps. The figures also need clearer legends, cluster labels, minimum occurrence thresholds, and interpretation of node size and link strength. At present, the figures look like raw software outputs rather than carefully prepared scientific evidence. 5. The technical discussion of AI methods is too basic. Sections on machine learning, deep learning, and simulation mostly provide generic descriptions. They do not critically discuss engineering design tasks such as topology optimization, generative design, inverse design, CAD/CAE automation, design-space exploration, surrogate modeling, uncertainty-aware design, or human-AI co-design. This is a major weakness because the paper is supposed to review AI in engineering design, not AI in general. 6. The specific application section is underdeveloped. The manuscript briefly discusses CAD, generative design, and mechanism synthesis, but it does not provide a structured comparison across engineering fields. Mechanical, civil, aerospace, manufacturing, and product design should not be mixed without a framework. The authors should classify applications by design stage, AI method, data source, validation approach, and engineering output. This would make the paper useful to readers instead of only giving a broad overview. 7. The conclusions are too general and are not strongly supported by the bibliometric analysis. Statements such as AI transforming engineering design, reducing lead time, and improving product quality may be true, but the paper does not quantify these claims using the reviewed corpus. The conclusion should instead report evidence-based findings from the dataset: dominant keywords, growth trends, leading countries/institutions, method clusters, and underdeveloped research directions. Is the topic of the review discussed comprehensively in the context of the current literature? No Are all factual statements correct and adequately supported by citations? No Is the review written in accessible language? Partly Are the conclusions drawn appropriate in the context of the current research literature? No Competing Interests No competing interests were disclosed. Reviewer Expertise Intersection of computational mechanics and applied AI, finite element modelling (Abaqus), structural verification, and developing Python-based automation for simulations, post-processing, and reporting. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Sarfarazi S. Peer Review Report For: A Bibliometric Analysis of the Applications of Artificial Intelligence Techniques to Engineering Design [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :619 ( https://doi.org/10.5256/f1000research.198030.r482991) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-619/v1#referee-response-482991 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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