Long-Term Performance Analysis of PCD Tools in the Milling of Aluminium Alloy Components in a Manufacturing Context

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Many of these components demand specific surface roughness profiles achieved through milling operations. However, excess material from upstream processes—such as flash remaining after demoulding —presents challenges for the machining process. These irregularities, combined with stringent demands for specific roughness, burr-free edges, and flatness, require careful selection of tool microgeometry, tool grade, and cutting parameters. Nonetheless, research concerning the behaviour of PCD tools when machining aluminium alloys remains limited, and existing studies often fail to address the complexities of high-volume production. This study investigates polycrystalline diamond (PCD) tool performance in high-speed milling of aluminium components through an eight-month production analysis comparing PCD grades and tool microgeometries. The research reveals that medium-sized grains in PCD tools lead to superior surface quality and a 22% longer tool life. Tools with larger tip radii (0.09 mm versus 0.06 mm) demonstrated significantly higher performance, reducing burr formation by 20% while maintaining lower surface roughness values. Insert height positioning emerged as a critical factor, since reducing deviations between insert heights substantially extended tool life and ensured consistent surface roughness. The findings provide practical criteria for PCD tool selection and tool microgeometry, directly impacting cost-efficiency in high-volume automotive component manufacturing. Polycrystalline diamond (PCD) Aluminium alloy Milling Tool life Surface roughness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Aluminium alloy components are gaining traction in the automotive industry due to their lightweight properties, contributing to improved fuel efficiency and reduced emissions in line with sustainability goals [ 1 ]. Approximately 55% of the aluminium weight per vehicle is produced by die-casting [ 2 ]. These castings require machining to achieve specific roughness profiles, dimensional and geometrical tolerances, and surface quality. However, two inherent challenges must be considered in this process: (1) variations caused by upstream and operational inconsistencies, and (2) the complex microstructure of cast aluminium alloys, which combines a soft matrix with abrasive and hard phases. Addressing these challenges is essential to maintaining consistent part quality, extending tool life, and reducing cycle time, thus ensuring a profitable return on investment (ROI). Process variation in high-volume die-cast machining can negatively impact the entire cutting process. Critical variations arise due to upstream process irregularities, particularly from excess material, such as gates, runners, and flash remaining after the demoulding process [ 3 ]. These variations alter the axial depth of cut, resulting in non-uniform cutting forces that can accelerate tool wear and increase the risk of catastrophic failure [ 4 ]. Operational inconsistencies compound these challenges. Variability in insert height leads to an unbalanced distribution of cutting forces across the cutting edges of the tool, increasing localised stresses and leading to irregular wear of the inserts [ 5 ]. Similarly, tool balance issues due to improper setup affect the distribution of forces during cutting, potentially accelerating tool degradation [ 6 ]. The combined effect of these process variations and operational inconsistencies leads to premature tool wear, necessitating frequent tool changes and disrupting production. Such process challenges are further complicated by the microstructure of cast aluminium alloys. These alloys typically contain primary α-aluminium dendrites surrounded by eutectic silicon particles and intermetallic compounds [ 3 ]. This soft aluminium matrix promotes the formation of built-up edges (BUE) on the tool, affecting the surface quality of the part and causing premature tool wear. At the same time, the abrasive silicon particles (5–17%) and hard intermetallic phases lead to rapid mechanical degradation of the cutting tool through abrasive wear [ 7 ]. At the high cutting speeds required for efficient production, these competing wear mechanisms pose a significant challenge for conventional carbide tools, making it difficult to achieve both long tool life and the consistent surface quality demanded in automotive applications [ 7 ]. For this reason, manufacturers favour polycrystalline diamond (PCD) tools. Despite higher initial costs, PCD tools are cost-effective because they significantly increase cutting speeds and tool life [ 4 ]. This composite material, produced by sintering diamond particles with a cobalt catalyst and bonding them to a carbide substrate, combines exceptional hardness (~ 50 GPa Knoop hardness) and thermal conductivity (250–400 W/m/K at 500°C) with toughness (8–15 MPa·m ½ mean fracture toughness) [ 8 ]. However, achieving consistent performance in high-volume production is challenging due to the lack of standardised criteria for: (1) selecting the best PCD grade, (2) defining the best cutting parameters, and (3) determining the proper tool microgeometry. As regards PCD grade selection, the literature predominantly focuses on isolated material properties, such as grain size, demonstrating that coarser grains improve abrasion resistance but reduce toughness, while finer grains enhance toughness but are more prone to wear [ 8 ]. Despite these findings, the complexities of high-volume production—such as material inconsistencies, tool preparation variability, and operational deviations—all contribute to tool wear and performance inconsistencies. These production realities demand accurate application-specific recommendations for PCD tool selection. Current practices, often based on supplier advice or iterative trial-and-error, fail to account for the interaction between PCD grades, machining conditions, and production requirements. Consequently, these limitations result in suboptimal tool performance, elevated costs, and inconsistencies across production cycles. Similarly, the selection of cutting parameters for machining aluminium-silicon alloys with PCD tools is often guided by general manufacturer recommendations, which provide broad ranges for cutting speeds (e.g., 500–2500 m/min for hypoeutectic Al-Si alloys) and feed rates (0.1–0.4 mm/tooth) [ 8 ]. However, these guidelines fail to offer specificity, particularly in how parameters interact with different PCD grades and workpiece requirements. Few studies address these gaps. Lazkano et al. [ 9 ], for example, developed roughness maps for face milling of aluminium alloys, which generalise cutting parameter selection by considering both kinematic and stochastic components of surface generation. Nevertheless, most research has been conducted in controlled environments, limiting understanding of the long-term performance of selected cutting parameters in high-volume industrial applications. These are environments where variables such as tool wear, material inconsistencies, and operational variations have a significant impact on machining results. Another important aspect is the selection of tool microgeometry, which is critical in high-volume aluminium machining as it has a direct effect on surface roughness and tool life. Nonetheless, research concerning the microgeometry of PCD tools for machining aluminium alloys remains limited [ 9 ]. Most studies have focused on specific parameters, such as clearance angles and cutting-edge radii, under controlled laboratory conditions, which do not always reflect the complexities of industrial production [ 3 ]. In addition, while edge-preparation methods such as EDM and grinding have been evaluated for their effects on residual stresses and edge quality [ 10 ], little is known about their long-term performance in high-volume environments or their suitability for different applications. This absence of actionable data forces manufacturers to rely heavily on experience or supplier recommendations, often leading to overly conservative decisions. The absence of standardisation outlined above arises from the limited knowledge of the primary wear mechanisms affecting PCD tools during the machining of aluminium alloys. While the wear mechanisms of carbide tools, such as abrasion, diffusion, oxidation, fatigue, and adhesion, are well-documented [16], PCD tool wear remains poorly understood. Existing research has focused on PCD wear mechanisms in machining highly abrasive materials like titanium alloys [ 11 ] or aluminium metal matrix composites (MMCs) [ 12 ]. However, these findings do not adequately address the wear behaviour of PCD tools when machining common aluminium alloys, such as A356 or A380, which are widely used in die-cast automotive components. The challenges are exacerbated by the practical difficulties of studying PCD tools in real-world conditions, including their long tool life, the high cost of wasted materials, reliance on expensive equipment, and the significant cost of PCD tools themselves. Consequently, studies on PCD tools have predominantly focused on controlled laboratory conditions, isolating variables such as grain size and material properties. However, these approaches often fail to address the complexities of high-volume production. Key challenges, including variability in PCD grade quality, inconsistencies in tool microgeometry, and the impact of prolonged use on performance stability, remain underexplored. Additionally, material defects and deviations in machining conditions further complicate performance analysis. These gaps highlight the need for a comprehensive understanding of the factors influencing PCD tool behaviour under real-world production conditions, particularly over extended cycles and demanding manufacturing scenarios. This study, therefore, systematically evaluates the critical factors influencing PCD tool performance through an eight-month production analysis involving 60,000 A380-T6 alloy components. The results reveal that PCD tools with medium-sized grains and larger tip radii (0.09 mm) exhibit a 22% longer tool life and a 20% reduction in burr formation compared to finer-grained tools with smaller tip radii (0.06 mm). Insert height uniformity was identified as a key factor, with irregular heights leading to a 20% increase in microchipping and accelerated tool wear. The study comprehensively explains how tool microgeometry and process consistency affect PCD tool performance in high-volume milling operations by analysing wear mechanisms such as flank wear, microchipping, and spalling under real production conditions. 2 Material and methods This study was conducted over eight months and involved the high-speed milling of 60,000 A380-T6 aluminium alloy powertrain components. The alloy, manufactured through high-pressure die-casting, consists of α-Al dendrites and Al-Si eutectic phases. Cutting tools comprised a shell-end milling with a diameter of 105 mm and a total length of 110 mm with 14 PCD inserts. Four PCD tool configurations were tested: Grade A (medium grain size) and B (coarse grain size) with a 0.09 mm cutting edge radius, and Grade C and C' (fine grain size) with 0.06 mm. Grades C and C' differed only in their insert height uniformity, with Grade C' exhibiting greater variability. The machining operations were conducted using a 5-axis machining centre, equipped with dual HSK-A63 spindles, capable of delivering 35 kW of power and 80 Nm torque in S6 mode. A hydraulic fixture system was used to clamp the workpieces during machining. An oil emulsion coolant was applied at 70 bar pressure to reduce the risk of built-up edge (BUE) formation and improve heat dissipation at the tool-workpiece interface. Cutting parameters were selected based on previous experience machining similar powertrain components. The axial depth of cut and radial depth of cut varied along the toolpath (Fig. 1 ). Tool wear progression was assessed by measuring the cutting-edge radius at the nose and flank wear of 14-inserts across four grades. A custom fixture was designed to enable direct measurements on the end mill without removing the inserts. The cutting-edge radius was measured using the Alicona Edge Master module with a 50× lens, capturing three distinct zones per insert. Each zone was analysed with 50 profiles spaced 20° apart. Flank wear was evaluated using the Alicona Profile Form Measurement module, following ISO 8688-2:1989 guidelines. Additionally, specific failure modes such as chipping, spalling, and tip breakage were quantified using the Alicona Difference Measurement Module with a 5× lens. Surface roughness was measured using a MarSurf PS 10 portable roughness tester at a designated point on the workpiece at the start and end of each machining cycle. The measurement location was chosen based on areas exhibiting the highest roughness levels. The evaluation length was 5.6 mm. The R-motif parameter was calculated according to ISO 12085, with roughness motif limit A set at 0.50 mm and waviness motif limit B set at 2.50 mm. The R-motif parameter was used as the criterion for end-of-life roughness. Strict compliance with roughness levels in the range of 5–20 µm was required to ensure part functionality and to meet mandatory leakage tests. Burr formation was analysed using a Mitutoyo QuickScope vision system with 3x magnification. In this study, burrs were considered problematic when their dimensions exceeded the cleaning and washing capabilities of the production system or when they affected part functionality during sealing tests. This functional assessment approach was preferred over establishing absolute dimensional thresholds, as the critical factor was the impact on downstream processes and component performance rather than a specific measurement value. Tool balancing was performed using the modular balancing TD Comfort Plus machine of Haimer, achieving a balance of 6,000 rpm @ 2.5G following the recommendations of the cutting tool manufacturer. Insert positioning was measured and adjusted using Zoller Hyperion equipment. After any insert height adjustments, tool balancing was repeated to maintain dynamic stability. Power consumption data was recorded from the internal sensors of the machining centre. 3 Results and Discussion 3.1 Wear performance of the analysed grades The wear mechanisms identified in this study include microchipping, spalling, fracture, and flank wear (Fig. 2 ). Microchipping was the most frequent wear mechanism, progressively increasing throughout the machining cycles. This wear mode resulted in small-scale material detachment at the cutting edge, as depicted in Fig. 2 a,b, leading to a gradual increase in cutting-edge radius at the tip, as shown in Fig. 3 a. This effect was more pronounced in tools with 0.06 mm cutting-edge radii (Grades C and C'), where higher stress concentrations accelerated micro-crack formation and propagation. Conversely, tools with a 0.09 mm radius (Grades A and B) exhibited lower microchipping rates, as the larger radius distributed stress more effectively. Additionally, insert height variability increased microchipping by approximately 20%, further worsening cutting-edge. Nevertheless, after correction, Grade C′ showed approximately 10% less cutting-edge radius than Grade C. This highlights the importance of regular monitoring of insert positioning. Spalling developed as a later-stage wear mechanism, characterised by the detachment of larger fragments extending Fracture (or insert breakage) was observed in three instances, all occurring at the nose of the tool, as shown in Fig. 2 d. The first fracture occurred at 2,500 parts in one of the grades. It could be explained by insert height variability, which may have led to uneven cutting forces. This likely concentrated stresses on the tool nose, eventually causing failure. Then, insert heights were checked and corrected to reduce the likelihood of similar issues. Subsequent fractures occurred later in the machining loops and could have been influenced by casting defects. These might have created uneven cutting forces, leading to sudden failures at the tool nose. Zones with greater axial depth of cut resulted from material variability further contributed to these failures by increasing localised forces on the tooltip. The absence of a clear progression pattern reinforces the possibility that these fractures resulted from inconsistent material properties and localised impact events, rather than accumulated stress. Flank wear, measured at 85–110 µm after 15,000 parts, had a limited impact on tool life. Figure 3 b suggests that PCD tools with medium (Grade A) and coarse grain size (Grade B), both with a 0.09 mm cutting-edge radius, exhibited similar wear levels of approximately 85 µm after 15,000 parts, while finer-grained tools (Grades C and C') with a 0.06 mm radius showed accelerated wear, reaching 100–110 µm after 10,000 parts. Since hypoeutectic aluminium machining occurs at relatively low temperatures, this accelerated wear behaviour could be linked to mechanical factors, such as higher stress concentrations in tools with a smaller cutting-edge radius and positioning inconsistencies, rather than thermal effects. Flank wear progression followed three phases: rapid initial wear (0–2,500 parts), steady-state wear (2,500–10,000 parts), and accelerated wear (> 10,000 parts). Compared to titanium machining with PCD [ 11 ], where flank wear reached 200–250 µm over shorter cutting distances, the values observed in this aluminium study (85–110 µm after 15,000 parts) are relatively low. Figure 3 b suggests that, while flank wear remained stable and moderate, it may not have been the dominant wear mechanism in this application. The presence of a built-up edge (BUE) was not detected as a critical wear mechanism in this study. The use of high-pressure coolant effectively minimises aluminium adhesion on the tool surface. While minor adhesion was visible, it did not significantly contribute to wear or failure, further emphasising the efficacy of lubricant application in maintaining tool performance. 3.2 Surface roughness evolution The surface roughness progression for each tool configuration is shown in Fig. 3 c, where key trends and notable events such as insert breakage and height corrections are highlighted. All tool configurations exhibited an increasing trend in surface roughness as tool wear progressed. The initial roughness values for all tools were above the functional limit of 5 µm, reflecting that the profile of the cutting tools and the selected feed per tooth were adequate for all the grades to start above the R-motif surface roughness required for the component. Surface roughness measurements consistently exceeded the functional limit of 20 µm at the end of tool life. Over successive machining cycles, roughness values progressively increased until surpassing the 20 µm threshold, at which point the tools were deemed to have reached the end of their useful life. The tools with larger tip radii (0.09 mm) consistently demonstrated better surface roughness performance than those with smaller tip radii (0.06 mm). This can be attributed to the ability of the larger tip radii to distribute cutting forces over a broader area, reducing stress concentrations at the tool-workpiece interface and minimising the initiation of micro-cracks and micro-chipping. For instance, tools configured with Grade A and B (both 0.09 mm radii) maintained surface roughness below 15 µm for 12,500 parts, whereas the finer-tipped Grade C and C’ tools reached this threshold after approximately 7,500 parts. For Grade C and C’ specifically, surface roughness was the definitive end-of-life criterion, with tools requiring replacement at 13,500 machined parts due to exceeding the established functional limit. The variability in insert heights further exacerbated the surface roughness degradation, particularly for grade C' tools. However, the anticipated correction for insert height mitigated the rapid reaching of the upper limit. Another important fact is the gradual increase in surface roughness, which correlates with the micro-chipping mechanism identified. Micro-chipping degrades the cutting edge, creating irregularities directly translating into higher roughness values on the machined surface. Tools with medium-grain PCD (e.g. Grade A) showed superior resistance to microchipping and, therefore, a more gradual roughness progression than finer-grain grades (e.g. Grade C). In summary, larger nose radii and medium-grain PCD grades are recommended for applications where surface finish consistency is critical, as these configurations extend tool life while keeping roughness within acceptable functional limits. In addition, ensuring a consistent insert height across the tool can significantly reduce early degradation, thereby extending tool life and reducing production downtime. 3.3 Burr formation Figure 4 shows that burr thickness was lower in tools with a 0.09 mm edge radius (Grades A and B), measuring 0.185 mm and 0.208 mm, likely due to better stress distribution and uniform load balance, which delayed burr progression. In contrast, Grades C and C' (0.06 mm edge radius) produced thicker burrs (0.224 mm and 0.239 mm), as higher stress concentration at the cutting interface increased material deformation. Insert height variability, particularly in Grade C', may have intensified localised stresses, accelerated burr formation, and potentially compromised part quality or required premature tool replacement. Excessive burr formation occurred after 16,500 parts in Grades A and B, despite their lower overall burr thickness. This burr accumulation constituted the definitive end-of-life criterion for Grades A and B tools, as the excessive burrs could no longer be effectively managed through manual deburring and directly compromised the leakage test performance of the machined components, necessitating tool replacement regardless of acceptable roughness values. 3.4 Power consumption Figure 5 reveals significant variability across machining cycles, primarily due to excess material resulting from upstream process irregularities. Whilst this variability precludes direct correlation between power consumption and tool wear progression, the data remains valuable for detecting anomalies in mould condition and cutting process inconsistencies. Despite the non-uniform axial and radial depths of cut, the comparative wear study maintains reliability as all PCD grades were subjected to identical production conditions. Our assessment methodology prioritised direct measurement of wear indicators rather than relying on power consumption, thereby ensuring valid comparisons whilst acknowledging the inherent challenges of industrial testing compared to controlled laboratory environments. 4 Conclusions and future lines This paper has analysed significant manufacturing challenges in the high-speed milling of A380-T6 components, particularly focusing on tool wear, surface roughness, and burr formation. By analysing PCD tool performance across 60,000 machined parts over eight months, this study bridges the critical gap between controlled research and industrial application, offering valuable insights into PCD tool behaviour under real manufacturing conditions. The conclusions from this work are the following: Microchipping was identified as the dominant wear mechanism in PCD tools during aluminium machining, with localised stress concentrations initiating micro-cracks that lead to progressive wear affecting surface quality and burr formation. Larger cutting-edge radii (0.09 mm) were demonstrated to significantly reduce microchipping compared to smaller radii (0.06 mm) by distributing stresses more uniformly. This highlights the importance of optimising tool geometry. Tools with larger edge radii were found to suppress burr formation by 20%, since stress concentrations were reduced at the cutting-edge. Medium-grain PCD tools demonstrated superior surface quality and a 22% longer tool life, providing a practical insight for tool selection in high-volume production. Power consumption was found not to be an indicator of wear evolution but rather was driven primarily by material inconsistencies. This data could also prove valuable for identifying machining anomalies and guiding process adjustments. The research underscores the importance of maintaining consistent insert height positioning and tool balancing. These operational factors help extend tool life, contributing directly to cost-effective and reliable production. Future work should advance modelling of tool wear and validate findings across different die-cast aluminum alloys and manufacturing conditions. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions J. David Chaux: Conceptualization, Methodology, Investigation, Validation, Formal Analysis, Visualisation, Data Curation, Writing – Original Draft, Writing – Review & Editing. Patxi Aristimuño: Methodology, Formal analysis, Investigation, Validation, Conceptualization, Supervision, Resources, Writing – review & editing. Mikel Etxanojauregi: Methodology, Investigation, Validation, Formal analysis, Conceptualization, Writing – review & editing. Iban Orbegozo: Methodology, Formal analysis, Validation, Conceptualization, Writing – review & editing. Pedro J. Arrazola: Project administration, Conceptualization, Methodology, Formal Analysis, Supervision, Resources, Writing - review & editing, Funding acquisition. Acknowledgements The authors hereby thank the DIGIVaCh (ZE-2021/00026), TAILORSURF (PID2022-139655OB-I00), and ORLEGI (KK-2024/00013) projects for their financial support. References J. Shen, Q. Zhang, and S. 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Cite Share Download PDF Status: Published Journal Publication published 21 Jun, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 16 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor assigned by journal 31 Mar, 2025 First submitted to journal 29 Mar, 2025 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. 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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-6336115","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436684111,"identity":"3bc81fa7-be23-4452-b6e6-895c027eecd0","order_by":0,"name":"Jesús David Chaux","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACxoYENhAtByIkYKISuJQjazEmXgsDA0RLYgPRWpjbk589+Nl2OH1te+/B2xU1tXLmDMwHb/Pgc1jPM3PD3ra03G1nziVbnjl23NiygS3ZGq+WGTlsErxtNrnbbuSYSTawHUvccIDHTJqQFsm/bRLpZmAt/0Ba+L8R1CINtCUBrKWxrQZkCxt+LT3PzKRlzqUZbjtzxtiyse+AscFhNmPLOXi0GAJDTPJN2WF5s+M9hjcbvtXJGRxvfnjjDT4tDaj8w8Bwx6McBOTR+HUE1I+CUTAKRsFIBADPXU4E8JBEHQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-1140-1005","institution":"Mondragon Unibertsitatea","correspondingAuthor":true,"prefix":"","firstName":"Jesús","middleName":"David","lastName":"Chaux","suffix":""},{"id":436684112,"identity":"5d423418-d24a-4c1c-b45a-565390f49bde","order_by":1,"name":"Patxi Aristimuño Osoro","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Patxi","middleName":"Aristimuño","lastName":"Osoro","suffix":""},{"id":436684113,"identity":"3399ce25-5cc4-4c13-8670-4b6081552094","order_by":2,"name":"Mikel Etxanojauregi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mikel","middleName":"","lastName":"Etxanojauregi","suffix":""},{"id":436684114,"identity":"6133ce16-d2dd-4c55-b8a4-d40453464968","order_by":3,"name":"Iban Orbegozo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Iban","middleName":"","lastName":"Orbegozo","suffix":""},{"id":436684115,"identity":"4b4f1837-3414-4bc9-a424-cbfa04c9b81f","order_by":4,"name":"Pedro J. Arrazola","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"J.","lastName":"Arrazola","suffix":""}],"badges":[],"createdAt":"2025-03-29 23:35:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6336115/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6336115/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-15956-3","type":"published","date":"2025-06-21T15:57:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81054024,"identity":"d2bb2c30-27e6-462a-bd27-f8ccae3a2848","added_by":"auto","created_at":"2025-04-21 16:56:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":551789,"visible":true,"origin":"","legend":"\u003cp\u003eCutting path across the A386-T6 powertrain automotive part.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6336115/v1/4d9e1d112578352a5c7ac281.png"},{"id":81054026,"identity":"56e0d606-8631-422a-960b-222324afeddf","added_by":"auto","created_at":"2025-04-21 16:56:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2204178,"visible":true,"origin":"","legend":"\u003cp\u003eWear mechanisms. \u003cstrong\u003e(a)\u003c/strong\u003ea combination of failure and wear mechanisms, \u003cstrong\u003e(b)\u003c/strong\u003e microchipping, \u003cstrong\u003e(c)\u003c/strong\u003espalling, and \u003cstrong\u003e(d)\u003c/strong\u003e fracture (breakage of the insert at the tip).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6336115/v1/a1e0406d8a4e030517c8f8c4.png"},{"id":81054191,"identity":"0af0faf0-02a9-4ff2-a556-b5ff306051e9","added_by":"auto","created_at":"2025-04-21 17:04:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":319904,"visible":true,"origin":"","legend":"\u003cp\u003eWear analysis by the number of machined parts (from 0 to 17500 parts) and PCD grades (A, B, C, and C’). \u003cstrong\u003e(a)\u003c/strong\u003e cutting edge radius at the nose, \u003cstrong\u003e(b) \u003c/strong\u003eflank wear, and \u003cstrong\u003e(c)\u003c/strong\u003e R-motif roughness evolution.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6336115/v1/97fa3656e11ee6455148b8ef.png"},{"id":81054840,"identity":"a26acf58-c7ee-4b1a-a5d2-02ea23c89928","added_by":"auto","created_at":"2025-04-21 17:12:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1025880,"visible":true,"origin":"","legend":"\u003cp\u003eBurr thickness across different PCD grades.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6336115/v1/3e7a3086ea2c9597b94fcce1.png"},{"id":81054039,"identity":"908bfffa-2f29-4276-a87d-e21632187d57","added_by":"auto","created_at":"2025-04-21 16:56:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":213377,"visible":true,"origin":"","legend":"\u003cp\u003ePower consumption to highlight variations due to changes in radial depth of cut (\u003cem\u003ea\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e) and axial depth of cut (\u003cem\u003ea\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e). Excess material from the upstream process can explain variations in axial depth of cut. \u0026nbsp;The number of parts machined is indicated by a P followed by the number of parts.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6336115/v1/6f62bde59c8720e0f33c3519.png"},{"id":85231411,"identity":"84e08ee2-7da1-480e-82be-cd39c747f961","added_by":"auto","created_at":"2025-06-23 16:07:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6229903,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6336115/v1/4ac5066c-4bd8-4303-b15c-e08512f86df9.pdf"}],"financialInterests":"","formattedTitle":"Long-Term Performance Analysis of PCD Tools in the Milling of Aluminium Alloy Components in a Manufacturing Context","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAluminium alloy components are gaining traction in the automotive industry due to their lightweight properties, contributing to improved fuel efficiency and reduced emissions in line with sustainability goals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Approximately 55% of the aluminium weight per vehicle is produced by die-casting [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These castings require machining to achieve specific roughness profiles, dimensional and geometrical tolerances, and surface quality. However, two inherent challenges must be considered in this process: (1) variations caused by upstream and operational inconsistencies, and (2) the complex microstructure of cast aluminium alloys, which combines a soft matrix with abrasive and hard phases. Addressing these challenges is essential to maintaining consistent part quality, extending tool life, and reducing cycle time, thus ensuring a profitable return on investment (ROI).\u003c/p\u003e \u003cp\u003eProcess variation in high-volume die-cast machining can negatively impact the entire cutting process. Critical variations arise due to upstream process irregularities, particularly from excess material, such as gates, runners, and flash remaining after the demoulding process [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These variations alter the axial depth of cut, resulting in non-uniform cutting forces that can accelerate tool wear and increase the risk of catastrophic failure [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Operational inconsistencies compound these challenges. Variability in insert height leads to an unbalanced distribution of cutting forces across the cutting edges of the tool, increasing localised stresses and leading to irregular wear of the inserts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Similarly, tool balance issues due to improper setup affect the distribution of forces during cutting, potentially accelerating tool degradation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The combined effect of these process variations and operational inconsistencies leads to premature tool wear, necessitating frequent tool changes and disrupting production.\u003c/p\u003e \u003cp\u003eSuch process challenges are further complicated by the microstructure of cast aluminium alloys. These alloys typically contain primary α-aluminium dendrites surrounded by eutectic silicon particles and intermetallic compounds [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This soft aluminium matrix promotes the formation of built-up edges (BUE) on the tool, affecting the surface quality of the part and causing premature tool wear. At the same time, the abrasive silicon particles (5\u0026ndash;17%) and hard intermetallic phases lead to rapid mechanical degradation of the cutting tool through abrasive wear [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the high cutting speeds required for efficient production, these competing wear mechanisms pose a significant challenge for conventional carbide tools, making it difficult to achieve both long tool life and the consistent surface quality demanded in automotive applications [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For this reason, manufacturers favour polycrystalline diamond (PCD) tools. Despite higher initial costs, PCD tools are cost-effective because they significantly increase cutting speeds and tool life [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This composite material, produced by sintering diamond particles with a cobalt catalyst and bonding them to a carbide substrate, combines exceptional hardness (~\u0026thinsp;50 GPa Knoop hardness) and thermal conductivity (250\u0026ndash;400 W/m/K at 500\u0026deg;C) with toughness (8\u0026ndash;15 MPa\u0026middot;m\u003csup\u003e\u0026frac12;\u003c/sup\u003e mean fracture toughness) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, achieving consistent performance in high-volume production is challenging due to the lack of standardised criteria for: (1) selecting the best PCD grade, (2) defining the best cutting parameters, and (3) determining the proper tool microgeometry.\u003c/p\u003e \u003cp\u003eAs regards PCD grade selection, the literature predominantly focuses on isolated material properties, such as grain size, demonstrating that coarser grains improve abrasion resistance but reduce toughness, while finer grains enhance toughness but are more prone to wear [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite these findings, the complexities of high-volume production\u0026mdash;such as material inconsistencies, tool preparation variability, and operational deviations\u0026mdash;all contribute to tool wear and performance inconsistencies. These production realities demand accurate application-specific recommendations for PCD tool selection. Current practices, often based on supplier advice or iterative trial-and-error, fail to account for the interaction between PCD grades, machining conditions, and production requirements. Consequently, these limitations result in suboptimal tool performance, elevated costs, and inconsistencies across production cycles.\u003c/p\u003e \u003cp\u003eSimilarly, the selection of cutting parameters for machining aluminium-silicon alloys with PCD tools is often guided by general manufacturer recommendations, which provide broad ranges for cutting speeds (e.g., 500\u0026ndash;2500 m/min for hypoeutectic Al-Si alloys) and feed rates (0.1\u0026ndash;0.4 mm/tooth) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, these guidelines fail to offer specificity, particularly in how parameters interact with different PCD grades and workpiece requirements. Few studies address these gaps. Lazkano et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], for example, developed roughness maps for face milling of aluminium alloys, which generalise cutting parameter selection by considering both kinematic and stochastic components of surface generation. Nevertheless, most research has been conducted in controlled environments, limiting understanding of the long-term performance of selected cutting parameters in high-volume industrial applications. These are environments where variables such as tool wear, material inconsistencies, and operational variations have a significant impact on machining results.\u003c/p\u003e \u003cp\u003eAnother important aspect is the selection of tool microgeometry, which is critical in high-volume aluminium machining as it has a direct effect on surface roughness and tool life. Nonetheless, research concerning the microgeometry of PCD tools for machining aluminium alloys remains limited [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Most studies have focused on specific parameters, such as clearance angles and cutting-edge radii, under controlled laboratory conditions, which do not always reflect the complexities of industrial production [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, while edge-preparation methods such as EDM and grinding have been evaluated for their effects on residual stresses and edge quality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], little is known about their long-term performance in high-volume environments or their suitability for different applications. This absence of actionable data forces manufacturers to rely heavily on experience or supplier recommendations, often leading to overly conservative decisions.\u003c/p\u003e \u003cp\u003eThe absence of standardisation outlined above arises from the limited knowledge of the primary wear mechanisms affecting PCD tools during the machining of aluminium alloys. While the wear mechanisms of carbide tools, such as abrasion, diffusion, oxidation, fatigue, and adhesion, are well-documented [16], PCD tool wear remains poorly understood. Existing research has focused on PCD wear mechanisms in machining highly abrasive materials like titanium alloys [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] or aluminium metal matrix composites (MMCs) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, these findings do not adequately address the wear behaviour of PCD tools when machining common aluminium alloys, such as A356 or A380, which are widely used in die-cast automotive components. The challenges are exacerbated by the practical difficulties of studying PCD tools in real-world conditions, including their long tool life, the high cost of wasted materials, reliance on expensive equipment, and the significant cost of PCD tools themselves.\u003c/p\u003e \u003cp\u003eConsequently, studies on PCD tools have predominantly focused on controlled laboratory conditions, isolating variables such as grain size and material properties. However, these approaches often fail to address the complexities of high-volume production. Key challenges, including variability in PCD grade quality, inconsistencies in tool microgeometry, and the impact of prolonged use on performance stability, remain underexplored. Additionally, material defects and deviations in machining conditions further complicate performance analysis. These gaps highlight the need for a comprehensive understanding of the factors influencing PCD tool behaviour under real-world production conditions, particularly over extended cycles and demanding manufacturing scenarios.\u003c/p\u003e \u003cp\u003eThis study, therefore, systematically evaluates the critical factors influencing PCD tool performance through an eight-month production analysis involving 60,000 A380-T6 alloy components. The results reveal that PCD tools with medium-sized grains and larger tip radii (0.09 mm) exhibit a 22% longer tool life and a 20% reduction in burr formation compared to finer-grained tools with smaller tip radii (0.06 mm). Insert height uniformity was identified as a key factor, with irregular heights leading to a 20% increase in microchipping and accelerated tool wear. The study comprehensively explains how tool microgeometry and process consistency affect PCD tool performance in high-volume milling operations by analysing wear mechanisms such as flank wear, microchipping, and spalling under real production conditions.\u003c/p\u003e"},{"header":"2 Material and methods","content":"\u003cp\u003eThis study was conducted over eight months and involved the high-speed milling of 60,000 A380-T6 aluminium alloy powertrain components. The alloy, manufactured through high-pressure die-casting, consists of α-Al dendrites and Al-Si eutectic phases.\u003c/p\u003e \u003cp\u003eCutting tools comprised a shell-end milling with a diameter of 105 mm and a total length of 110 mm with 14 PCD inserts. Four PCD tool configurations were tested: Grade A (medium grain size) and B (coarse grain size) with a 0.09 mm cutting edge radius, and Grade C and C' (fine grain size) with 0.06 mm. Grades C and C' differed only in their insert height uniformity, with Grade C' exhibiting greater variability.\u003c/p\u003e \u003cp\u003eThe machining operations were conducted using a 5-axis machining centre, equipped with dual HSK-A63 spindles, capable of delivering 35 kW of power and 80 Nm torque in S6 mode. A hydraulic fixture system was used to clamp the workpieces during machining. An oil emulsion coolant was applied at 70 bar pressure to reduce the risk of built-up edge (BUE) formation and improve heat dissipation at the tool-workpiece interface. Cutting parameters were selected based on previous experience machining similar powertrain components. The axial depth of cut and radial depth of cut varied along the toolpath (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTool wear progression was assessed by measuring the cutting-edge radius at the nose and flank wear of 14-inserts across four grades. A custom fixture was designed to enable direct measurements on the end mill without removing the inserts. The cutting-edge radius was measured using the Alicona Edge Master module with a 50\u0026times; lens, capturing three distinct zones per insert. Each zone was analysed with 50 profiles spaced 20\u0026deg; apart. Flank wear was evaluated using the Alicona Profile Form Measurement module, following ISO 8688-2:1989 guidelines. Additionally, specific failure modes such as chipping, spalling, and tip breakage were quantified using the Alicona Difference Measurement Module with a 5\u0026times; lens.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSurface roughness was measured using a MarSurf PS 10 portable roughness tester at a designated point on the workpiece at the start and end of each machining cycle. The measurement location was chosen based on areas exhibiting the highest roughness levels. The evaluation length was 5.6 mm. The R-motif parameter was calculated according to ISO 12085, with roughness motif limit A set at 0.50 mm and waviness motif limit B set at 2.50 mm. The R-motif parameter was used as the criterion for end-of-life roughness. Strict compliance with roughness levels in the range of 5\u0026ndash;20 \u0026micro;m was required to ensure part functionality and to meet mandatory leakage tests.\u003c/p\u003e \u003cp\u003eBurr formation was analysed using a Mitutoyo QuickScope vision system with 3x magnification. In this study, burrs were considered problematic when their dimensions exceeded the cleaning and washing capabilities of the production system or when they affected part functionality during sealing tests. This functional assessment approach was preferred over establishing absolute dimensional thresholds, as the critical factor was the impact on downstream processes and component performance rather than a specific measurement value.\u003c/p\u003e \u003cp\u003eTool balancing was performed using the modular balancing TD Comfort Plus machine of Haimer, achieving a balance of 6,000 rpm @ 2.5G following the recommendations of the cutting tool manufacturer. Insert positioning was measured and adjusted using Zoller Hyperion equipment. After any insert height adjustments, tool balancing was repeated to maintain dynamic stability. Power consumption data was recorded from the internal sensors of the machining centre.\u003c/p\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Wear performance of the analysed grades\u003c/h2\u003e \u003cp\u003eThe wear mechanisms identified in this study include microchipping, spalling, fracture, and flank wear (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMicrochipping was the most frequent wear mechanism, progressively increasing throughout the machining cycles. This wear mode resulted in small-scale material detachment at the cutting edge, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea,b, leading to a gradual increase in cutting-edge radius at the tip, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. This effect was more pronounced in tools with 0.06 mm cutting-edge radii (Grades C and C'), where higher stress concentrations accelerated micro-crack formation and propagation. Conversely, tools with a 0.09 mm radius (Grades A and B) exhibited lower microchipping rates, as the larger radius distributed stress more effectively. Additionally, insert height variability increased microchipping by approximately 20%, further worsening cutting-edge. Nevertheless, after correction, Grade C\u0026prime; showed approximately 10% less cutting-edge radius than Grade C. This highlights the importance of regular monitoring of insert positioning.\u003c/p\u003e \u003cp\u003eSpalling developed as a later-stage wear mechanism, characterised by the detachment of larger fragments extending Fracture (or insert breakage) was observed in three instances, all occurring at the nose of the tool, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed. The first fracture occurred at 2,500 parts in one of the grades. It could be explained by insert height variability, which may have led to uneven cutting forces. This likely concentrated stresses on the tool nose, eventually causing failure. Then, insert heights were checked and corrected to reduce the likelihood of similar issues. Subsequent fractures occurred later in the machining loops and could have been influenced by casting defects. These might have created uneven cutting forces, leading to sudden failures at the tool nose. Zones with greater axial depth of cut resulted from material variability further contributed to these failures by increasing localised forces on the tooltip. The absence of a clear progression pattern reinforces the possibility that these fractures resulted from inconsistent material properties and localised impact events, rather than accumulated stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFlank wear, measured at 85\u0026ndash;110 \u0026micro;m after 15,000 parts, had a limited impact on tool life. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb suggests that PCD tools with medium (Grade A) and coarse grain size (Grade B), both with a 0.09 mm cutting-edge radius, exhibited similar wear levels of approximately 85 \u0026micro;m after 15,000 parts, while finer-grained tools (Grades C and C') with a 0.06 mm radius showed accelerated wear, reaching 100\u0026ndash;110 \u0026micro;m after 10,000 parts. Since hypoeutectic aluminium machining occurs at relatively low temperatures, this accelerated wear behaviour could be linked to mechanical factors, such as higher stress concentrations in tools with a smaller cutting-edge radius and positioning inconsistencies, rather than thermal effects. Flank wear progression followed three phases: rapid initial wear (0\u0026ndash;2,500 parts), steady-state wear (2,500\u0026ndash;10,000 parts), and accelerated wear (\u0026gt;\u0026thinsp;10,000 parts). Compared to titanium machining with PCD [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], where flank wear reached 200\u0026ndash;250 \u0026micro;m over shorter cutting distances, the values observed in this aluminium study (85\u0026ndash;110 \u0026micro;m after 15,000 parts) are relatively low. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb suggests that, while flank wear remained stable and moderate, it may not have been the dominant wear mechanism in this application.\u003c/p\u003e \u003cp\u003eThe presence of a built-up edge (BUE) was not detected as a critical wear mechanism in this study. The use of high-pressure coolant effectively minimises aluminium adhesion on the tool surface. While minor adhesion was visible, it did not significantly contribute to wear or failure, further emphasising the efficacy of lubricant application in maintaining tool performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Surface roughness evolution\u003c/h2\u003e \u003cp\u003eThe surface roughness progression for each tool configuration is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, where key trends and notable events such as insert breakage and height corrections are highlighted. All tool configurations exhibited an increasing trend in surface roughness as tool wear progressed. The initial roughness values for all tools were above the functional limit of 5 \u0026micro;m, reflecting that the profile of the cutting tools and the selected feed per tooth were adequate for all the grades to start above the R-motif surface roughness required for the component. Surface roughness measurements consistently exceeded the functional limit of 20 \u0026micro;m at the end of tool life. Over successive machining cycles, roughness values progressively increased until surpassing the 20 \u0026micro;m threshold, at which point the tools were deemed to have reached the end of their useful life.\u003c/p\u003e \u003cp\u003eThe tools with larger tip radii (0.09 mm) consistently demonstrated better surface roughness performance than those with smaller tip radii (0.06 mm). This can be attributed to the ability of the larger tip radii to distribute cutting forces over a broader area, reducing stress concentrations at the tool-workpiece interface and minimising the initiation of micro-cracks and micro-chipping. For instance, tools configured with Grade A and B (both 0.09 mm radii) maintained surface roughness below 15 \u0026micro;m for 12,500 parts, whereas the finer-tipped Grade C and C\u0026rsquo; tools reached this threshold after approximately 7,500 parts. For Grade C and C\u0026rsquo; specifically, surface roughness was the definitive end-of-life criterion, with tools requiring replacement at 13,500 machined parts due to exceeding the established functional limit.\u003c/p\u003e \u003cp\u003eThe variability in insert heights further exacerbated the surface roughness degradation, particularly for grade C' tools. However, the anticipated correction for insert height mitigated the rapid reaching of the upper limit. Another important fact is the gradual increase in surface roughness, which correlates with the micro-chipping mechanism identified. Micro-chipping degrades the cutting edge, creating irregularities directly translating into higher roughness values on the machined surface. Tools with medium-grain PCD (e.g. Grade A) showed superior resistance to microchipping and, therefore, a more gradual roughness progression than finer-grain grades (e.g. Grade C). In summary, larger nose radii and medium-grain PCD grades are recommended for applications where surface finish consistency is critical, as these configurations extend tool life while keeping roughness within acceptable functional limits. In addition, ensuring a consistent insert height across the tool can significantly reduce early degradation, thereby extending tool life and reducing production downtime.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Burr formation\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that burr thickness was lower in tools with a 0.09 mm edge radius (Grades A and B), measuring 0.185 mm and 0.208 mm, likely due to better stress distribution and uniform load balance, which delayed burr progression. In contrast, Grades C and C' (0.06 mm edge radius) produced thicker burrs (0.224 mm and 0.239 mm), as higher stress concentration at the cutting interface increased material deformation. Insert height variability, particularly in Grade C', may have intensified localised stresses, accelerated burr formation, and potentially compromised part quality or required premature tool replacement. Excessive burr formation occurred after 16,500 parts in Grades A and B, despite their lower overall burr thickness. This burr accumulation constituted the definitive end-of-life criterion for Grades A and B tools, as the excessive burrs could no longer be effectively managed through manual deburring and directly compromised the leakage test performance of the machined components, necessitating tool replacement regardless of acceptable roughness values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Power consumption\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reveals significant variability across machining cycles, primarily due to excess material resulting from upstream process irregularities. Whilst this variability precludes direct correlation between power consumption and tool wear progression, the data remains valuable for detecting anomalies in mould condition and cutting process inconsistencies. Despite the non-uniform axial and radial depths of cut, the comparative wear study maintains reliability as all PCD grades were subjected to identical production conditions. Our assessment methodology prioritised direct measurement of wear indicators rather than relying on power consumption, thereby ensuring valid comparisons whilst acknowledging the inherent challenges of industrial testing compared to controlled laboratory environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusions and future lines","content":"\u003cp\u003eThis paper has analysed significant manufacturing challenges in the high-speed milling of A380-T6 components, particularly focusing on tool wear, surface roughness, and burr formation. By analysing PCD tool performance across 60,000 machined parts over eight months, this study bridges the critical gap between controlled research and industrial application, offering valuable insights into PCD tool behaviour under real manufacturing conditions. The conclusions from this work are the following:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMicrochipping was identified as the dominant wear mechanism in PCD tools during aluminium machining, with localised stress concentrations initiating micro-cracks that lead to progressive wear affecting surface quality and burr formation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLarger cutting-edge radii (0.09 mm) were demonstrated to significantly reduce microchipping compared to smaller radii (0.06 mm) by distributing stresses more uniformly. This highlights the importance of optimising tool geometry.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTools with larger edge radii were found to suppress burr formation by 20%, since stress concentrations were reduced at the cutting-edge.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMedium-grain PCD tools demonstrated superior surface quality and a 22% longer tool life, providing a practical insight for tool selection in high-volume production.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePower consumption was found not to be an indicator of wear evolution but rather was driven primarily by material inconsistencies. This data could also prove valuable for identifying machining anomalies and guiding process adjustments.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe research underscores the importance of maintaining consistent insert height positioning and tool balancing. These operational factors help extend tool life, contributing directly to cost-effective and reliable production.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFuture work should advance modelling of tool wear and validate findings across different die-cast aluminum alloys and manufacturing conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eJ. David Chaux: Conceptualization, Methodology, Investigation, Validation, Formal Analysis, Visualisation, Data Curation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing. Patxi Aristimu\u0026ntilde;o: Methodology, Formal analysis, Investigation, Validation, Conceptualization, Supervision, Resources, Writing \u0026ndash; review \u0026amp; editing. Mikel Etxanojauregi: Methodology, Investigation, Validation, Formal analysis, Conceptualization, Writing \u0026ndash; review \u0026amp; editing. Iban Orbegozo: Methodology, Formal analysis, Validation, Conceptualization, Writing \u0026ndash; review \u0026amp; editing. Pedro J. Arrazola: Project administration, Conceptualization, Methodology, Formal Analysis, Supervision, Resources, Writing - review \u0026amp; editing, Funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors hereby thank the DIGIVaCh (ZE-2021/00026), TAILORSURF (PID2022-139655OB-I00), and ORLEGI (KK-2024/00013) projects for their financial support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ. Shen, Q. Zhang, and S. Tian, \u0026ldquo;Decarbonization pathways analysis and recommendations in the green steel supply chain of a typical steel end user-automotive industry,\u0026rdquo; \u003cem\u003eAppl Energy\u003c/em\u003e, vol. 377, p. 124711, Jan. 2025, doi: 10.1016/J.APENERGY.2024.124711.\u003c/li\u003e\n\u003cli\u003eDucker, \u0026ldquo;Light Vehicle Aluminum Content and Outlook Study,\u0026rdquo; Apr. 2023.\u003c/li\u003e\n\u003cli\u003eA. D. Kaye and A. Street, \u003cem\u003eDie casting metallurgy: Butterworths monographs in materials\u003c/em\u003e. Elsevier, 2016.\u003c/li\u003e\n\u003cli\u003eV. P. Astakhov and A. Stanley, \u0026ldquo;Polycrystalline Diamond (PCD) Tool Material: Emerging Applications, Problems, and Possible Solutions,\u0026rdquo; pp. 1\u0026ndash;32, 2015, doi: 10.1007/978-3-662-45088-8_1.\u003c/li\u003e\n\u003cli\u003eL. Nowakowski, J. Rolek, S. Blasiak, and M. Skrzyniarz, \u0026ldquo;The Influence of Insert Mounting Errors on the Surface Roughness of 1.0503 Steel in Face Milling,\u0026rdquo; \u003cem\u003eMaterials 2024, Vol. 17, Page 6144\u003c/em\u003e, vol. 17, no. 24, p. 6144, Dec. 2024, doi: 10.3390/MA17246144.\u003c/li\u003e\n\u003cli\u003eS. Zhang, X. Ai, W. X. Tang, and J. G. Liu, \u0026ldquo;Balancing of Tool/Toolholder Assembly for High-Speed Machining,\u0026rdquo; \u003cem\u003eMaterials Science Forum\u003c/em\u003e, vol. 471\u0026ndash;472, pp. 542\u0026ndash;546, 2004, doi: 10.4028/WWW.SCIENTIFIC.NET/MSF.471-472.542.\u003c/li\u003e\n\u003cli\u003eM. C. Santos, A. R. Machado, W. F. Sales, M. A. S. Barrozo, and E. O. Ezugwu, \u0026ldquo;Machining of aluminum alloys: a review,\u0026rdquo; \u003cem\u003eInternational Journal of Advanced Manufacturing Technology\u003c/em\u003e, vol. 86, no. 9\u0026ndash;12, pp. 3067\u0026ndash;3080, Oct. 2016, doi: 10.1007/S00170-016-8431-9/METRICS.\u003c/li\u003e\n\u003cli\u003eElement Six, \u0026ldquo;Precision machining: giving toolmakers a competitive edge.\u0026rdquo; Accessed: Jan. 20, 2025. [Online]. Available: https://www.e6.com/en/knowledge-base/brochures\u003c/li\u003e\n\u003cli\u003eX. Lazkano, P. X. Aristimu\u0026ntilde;o, O. Aizpuru, and P. J. Arrazola, \u0026ldquo;Roughness maps to determine the optimum process window parameters in face milling,\u0026rdquo; \u003cem\u003eInt J Mech Sci\u003c/em\u003e, vol. 221, p. 107191, May 2022, doi: 10.1016/J.IJMECSCI.2022.107191.\u003c/li\u003e\n\u003cli\u003eB. Breidenstein, N. Vogel, and B. Bergmann, \u0026ldquo;Influence of the preparation processes on the residual stresses in PCD and PcBN tools,\u0026rdquo; \u003cem\u003eEuropean Journal of Materials\u003c/em\u003e, vol. 4, no. 1, p. 2350936, Dec. 2024, doi: 10.1080/26889277.2024.2350936.\u003c/li\u003e\n\u003cli\u003eT. Childerhouse, R. M\u0026rsquo;Saoubi, L. F. P. Franca, P. Crawforth, and M. Jackson, \u0026ldquo;Machining performance and wear behaviour of polycrystalline diamond and coated carbide tools during milling of titanium alloy Ti-54M,\u0026rdquo; \u003cem\u003eWear\u003c/em\u003e, vol. 523, p. 204791, Jun. 2023, doi: 10.1016/J.WEAR.2023.204791.\u003c/li\u003e\n\u003cli\u003eJ. Xiang, S. Pang, L. Xie, X. Hu, S. Peng, and T. Wang, \u0026ldquo;Investigation of cutting forces, surface integrity, and tool wear when high-speed milling of high-volume fraction SiCp/Al6063 composites in PCD tooling,\u0026rdquo; \u003cem\u003eInternational Journal of Advanced Manufacturing Technology\u003c/em\u003e, vol. 98, no. 5\u0026ndash;8, pp. 1237\u0026ndash;1251, Sep. 2018, doi: 10.1007/S00170-018-2294-1/METRICS.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Polycrystalline diamond (PCD), Aluminium alloy, Milling, Tool life, Surface roughness","lastPublishedDoi":"10.21203/rs.3.rs-6336115/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6336115/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAluminium alloy components are gaining traction in the automotive industry due to their lightweight properties, contributing to improved fuel efficiency and reduced emissions. Many of these components demand specific surface roughness profiles achieved through milling operations. However, excess material from upstream processes\u0026mdash;such as flash remaining after demoulding \u0026mdash;presents challenges for the machining process. These irregularities, combined with stringent demands for specific roughness, burr-free edges, and flatness, require careful selection of tool microgeometry, tool grade, and cutting parameters. Nonetheless, research concerning the behaviour of PCD tools when machining aluminium alloys remains limited, and existing studies often fail to address the complexities of high-volume production. This study investigates polycrystalline diamond (PCD) tool performance in high-speed milling of aluminium components through an eight-month production analysis comparing PCD grades and tool microgeometries. The research reveals that medium-sized grains in PCD tools lead to superior surface quality and a 22% longer tool life. Tools with larger tip radii (0.09 mm versus 0.06 mm) demonstrated significantly higher performance, reducing burr formation by 20% while maintaining lower surface roughness values. Insert height positioning emerged as a critical factor, since reducing deviations between insert heights substantially extended tool life and ensured consistent surface roughness. The findings provide practical criteria for PCD tool selection and tool microgeometry, directly impacting cost-efficiency in high-volume automotive component manufacturing.\u003c/p\u003e","manuscriptTitle":"Long-Term Performance Analysis of PCD Tools in the Milling of Aluminium Alloy Components in a Manufacturing Context","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 16:56:28","doi":"10.21203/rs.3.rs-6336115/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-04-16T09:03:56+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-04-02T06:48:03+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T06:33:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-01T02:19:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-03-29T19:34:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"07eeb19c-f315-46e3-b6ef-32d1fdfaa664","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T16:02:48+00:00","versionOfRecord":{"articleIdentity":"rs-6336115","link":"https://doi.org/10.1007/s00170-025-15956-3","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2025-06-21 15:57:51","publishedOnDateReadable":"June 21st, 2025"},"versionCreatedAt":"2025-04-21 16:56:28","video":"","vorDoi":"10.1007/s00170-025-15956-3","vorDoiUrl":"https://doi.org/10.1007/s00170-025-15956-3","workflowStages":[]},"version":"v1","identity":"rs-6336115","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6336115","identity":"rs-6336115","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}