CIRP Journal of Manufacturing Science and Technology最新文献

{"title":"Temperature field characteristics in rotary longitudinal-torsional ultrasonic machining of unidirectional carbon fiber reinforced polymer (CFRP)","authors":"Ziqiang Zhang ,&nbsp;Feng Jiao ,&nbsp;Yuanxiao Li","doi":"10.1016/j.cirpj.2025.12.019","DOIUrl":"10.1016/j.cirpj.2025.12.019","url":null,"abstract":"<div><div>Enhancing the drilling quality of carbon fiber reinforced polymer (CFRP) holds significant importance for advancing its application in aerospace and other fields. The temperature during CFRP core drilling critically impacts hole quality, and incorporating ultrasonic vibration during machining can effectively reduce this temperature. Predicting workpiece temperature is vital for selecting process parameters and enhancing hole quality of CFRP core drilling. However, current research on rotary ultrasonic machining (RUM) of CFRP predominantly focuses on experimental investigations, which relatively few reports on the temperature prediction. Utilizing the benefits of longitudinal-torsional ultrasonic vibration to reduce temperature, this paper establishes a temperature prediction model for rotary longitudinal-torsional ultrasonic machining (RLTUM) of unidirectional CFRP and analyzes the temperature field characteristics. Initially, the heat source properties in the machining process are analyzed, followed by an examination of heat transfer characteristics using the heat source method. Furthermore, the surface morphology of CFRP hole wall under different machining conditions was compared. Experimental verification confirms the model’s accuracy, demonstrating its capability to predict temperature evolution and variation trends with process parameters. The peak temperature prediction errors perpendicular and parallel to the fiber direction are 7.09–12.63 % and 5.54–14.36 %, respectively. Implementing longitudinal-torsional ultrasonic vibration reduces temperature during the core drilling process. As the fiber orientation angle increases, the corresponding peak temperature decreases, and the peak temperatures for different fiber orientation angles are symmetrical. This model serves as a valuable reference for selecting process parameters to improve CFRP drilling quality.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 145-162"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finite element modeling of indexable insert drilling processes in stainless steel","authors":"Mikel Etxebeste ,&nbsp;Gorka Ortiz-de-Zarate ,&nbsp;Homar Lopez-Hawa ,&nbsp;Pedro J. Arrazola","doi":"10.1016/j.cirpj.2025.12.018","DOIUrl":"10.1016/j.cirpj.2025.12.018","url":null,"abstract":"<div><div>Indexable insert drills play a crucial role in high-performance drilling, particularly for large-diameter holes and difficult-to-machine materials. Although FEM is a powerful tool for analyzing and optimizing drilling processes, limited research has focused on indexable insert drills, and the efficient simulation of large-diameter drills with complex cutting geometries remains a significant challenge. This study presents an optimized and computationally efficient FEM model for indexable insert drills, developed in AdvantEdge™ 3D, capable of predicting thermomechanical loads (thrust force, torque, stress, temperature) and chip morphology during drilling and redrilling of AISI 316 L stainless steel. The key innovation lies in a computational approach that significantly reduces simulation time while maintaining high predictive accuracy. The model incorporates a novel tool–workpiece configuration with a slotted workpiece that enables the drill to reach nominal feed rate immediately upon engagement, accelerating convergence toward thermomechanical steady-state. Model optimization was achieved through a systematic evaluation of the most influential input parameters, surpassing the capabilities of previous FEM approaches and providing new validated insight into drilling process modeling. A comprehensive sensitivity analysis of Johnson–Cook flow stress parameters, friction coefficients, and mesh size was performed to optimize both accuracy and computational efficiency. The model was experimentally validated through complete-drill tests (both inserts mounted) and novel single-insert tests (one insert mounted) across a wide range of cutting conditions, including redrilling with varying pilot hole diameters. The optimized model accurately predicts torque, thrust forces, and chip morphology (average error: 16 %), while providing detailed stress and temperature distributions. Thrust force underprediction remains the primary limitation, identified as originating from the central insert, where Build-Up Edge (BUE) formation was observed at low cutting speeds near the drill tip.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 130-144"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Friction stir lap welding of AA 2139 and AA 7075: Processing and sustainability analysis","authors":"Vitantonio Esperto ,&nbsp;Ersilia Cozzolino ,&nbsp;Antonello Astarita ,&nbsp;Pierpaolo Carlone ,&nbsp;Felice Rubino","doi":"10.1016/j.cirpj.2025.12.015","DOIUrl":"10.1016/j.cirpj.2025.12.015","url":null,"abstract":"<div><div>Friction Stir Welding (FSW) is increasingly adopted by industry to join difficult-to-weld materials thanks to its high energy efficiency and environmental sustainability. However, despite the extensive research, the studies on the sustainability of the process in correlation with the mechanical performance of the joints are still in the early stages. This study combines the analysis of energy consumption and mechanical properties in the FSW of dissimilar aluminum alloys, exploring different combinations of key process parameters. A gate-to-gate Life Cycle Assessment (LCA) was conducted to evaluate the environmental impact of the FSW process. From a sustainability standpoint, the optimal result was achieved using the highest travel speed (TS) of 270 mm/min in combination with a tool rotational speed (TRS) of 2000 rpm. Under these process conditions, reductions of up to 61 % in global warming potential (GWP), 62 % in total energy consumption, and 62 % in specific welding energy (SWE) were observed at the cost of approximately a 13 % reduction in flexural strength. As a result, power/energy, microhardness, microstructure, and flexural tests were incorporated into welding parameter maps to help in identifying minimum energy consumption and GWP points within the process constraints needed for maintaining good welding quality.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 1-17"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145814406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nozzle clogging during extrusion based additive manufacturing of polymer matrix composites—A numerical simulation insight into the process","authors":"Rajat Mishra ,&nbsp;Swasti Chakrabarty ,&nbsp;Amit Arora","doi":"10.1016/j.cirpj.2026.01.009","DOIUrl":"10.1016/j.cirpj.2026.01.009","url":null,"abstract":"<div><div>Enhancing properties of composite materials through aligned reinforcements in an extrusion-based additive manufacturing (AM) process, is a critical objective in engineering applications. The extrusion process involves study of complex multiphase flow to determine the directionality of the reinforcement. Advanced numerical techniques are to be deployed to study the interplay of various forces and process parameters in the process. In this study, we use coupled Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) numerical techniques to investigate the flow of a graphite-reinforced PVA polymer matrix through a nozzle, a process not easily achievable through experimental means. The drag force, pressure gradient force, and virtual mass force are found significant based on a comprehensive analysis of simulation and experimental data. Non-linear regression analysis is performed to quantify the impact of these forces on reinforcement alignment. The orientation angle of reinforcements is chosen as the output parameter, with input parameters comprising nozzle outlet diameter, reinforcement aspect ratio, volume flow rate, polymer viscosity, and reinforcement concentration. Additionally, the nozzle clogging during printing is studied using the developed model. Nozzle rotation is proposed as an effective method to mitigate clogging, further enhancing the efficiency of the reinforcement alignment process. This research advances our understanding of composite material printing and offers practical solution for optimizing the alignment of reinforcements in polymer matrices, paving the way for developing high-performance composite materials with tailored properties using extrusion based AM processes.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 273-294"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical strength of 3D-printed lattices under different environmental conditions","authors":"Mohammad Reza Khosravani ,&nbsp;Amirmahdi Abdollahi ,&nbsp;Hadi Sadeghian ,&nbsp;Majid R. Ayatollahi ,&nbsp;Tamara Reinicke","doi":"10.1016/j.cirpj.2026.01.004","DOIUrl":"10.1016/j.cirpj.2026.01.004","url":null,"abstract":"<div><div>In this study Additive Manufacturing (AM, i.e., 3D printing) has been used for fabrication of lattices with three different geometries: honeycomb, fourfold, and re-entrant. The specimens were fabricated based on the material extrusion technique, using Polyethylene Terephthalate Glycol (PETG) filament, which was reinforced with short carbon fiber additives. Since 3D-printed components might be subjected to various environmental conditions during their service life, here some of the test coupons were artificially aged (-5 to 40 °C) to determine effects of this thermal aging on their mechanical behavior. Based on a series of mechanical tests on the aged and unaged specimens, the deformation and energy absorption capabilities of the specimens were compared. Parallel to the experiments, two dimensional Finite Element Model (FEM) was developed to evaluate the mechanical performance and investigate the stress distribution and plastic deformation of the examined lattice structures. Moreover, Fractography analysis was conducted using Scanning Electron Microscope (SEM) images. The experimental findings indicate that energy absorption until damage initiation and energy absorption until the densification have been increased in almost all specimens due to the conducted thermal aging. In addition, SEM images indicate that during the loading process a higher amounts of energy was dissipated in the aged re-entrant lattices structure, compared to unaged test coupons. The documented results can be used for design and fabrication of thermal-stable 3D-printed composite parts.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 261-272"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-based feature enhancement method and physics constraint Transformer model for multi-step tool wear and RUL prediction","authors":"Shanglei Jiang ,&nbsp;Haoyuan Zhang ,&nbsp;Zhengmao Chen ,&nbsp;Yuwen Sun ,&nbsp;Xuexia Liu","doi":"10.1016/j.cirpj.2026.01.001","DOIUrl":"10.1016/j.cirpj.2026.01.001","url":null,"abstract":"<div><div>In intelligent manufacturing, tool wear monitoring (TWM) and remaining useful life (RUL) prediction are crucial for improving production quality and efficiency. However, achieving accurate and reliable multi-step (long-term) predictions remains a substantial challenge. This research proposes a feature enhancement method and constructs a Transformer model that embeds hard-soft physics constraints for multi-step wear and RUL prediction. Firstly, the fast adaptive Brownian bridge aggregation algorithm (fABBA) is employed to extract the features from multiscale signals during machining, alleviating the reliance on domain knowledge inherent in traditional feature engineering to some extent. On this basis, a physics-based feature enhancement method is proposed to improve the model’s generalization. Secondly, a Transformer model based on multi-head self-attention and cross-attention mechanisms is constructed for multi-step tool wear and RUL prediction. Meanwhile, a hard-soft physical constraint embedding module is designed to ensure that the model's output has a certain degree of physical interpretability. Finally, the PHM2010 and self-constructed datasets are employed to verify the effectiveness of the proposed method. Shapley Additive Explanations (SHAP) analysis method is used to quantitatively analyze the contribution of features to the model. The wear comparison experiments on the PHM2010 dataset, using C6 as the test set, show that the proposed model achieves RMSE values of 1.680672 <span><math><mo>±</mo></math></span> 0.137001, 2.220760 <span><math><mo>±</mo></math></span> 0.145516, 3.430798 <span><math><mo>±</mo></math></span> 0.485509, and 5.106184 <span><math><mo>±</mo></math></span> 0.690110 for 12, 24, 36 and 48 step predictions, respectively. Even for the ultra-long wear prediction, the R² remains at 0.981760 <span><math><mo>±</mo></math></span> 0.005227, which is better than GRU, BiGRU, BiGRU-AT, and TCN models.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 179-206"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent interpretable milling force prediction: A method based on vibration signals fusing data-driven and physical features","authors":"Wen Hou ,&nbsp;Tong Zhu ,&nbsp;Jiachang Wang ,&nbsp;Song Zhang","doi":"10.1016/j.cirpj.2025.12.020","DOIUrl":"10.1016/j.cirpj.2025.12.020","url":null,"abstract":"<div><div>To address the limitations in accuracy and interpretability of milling force prediction, this research proposes HyDCFF-Net, an interpretable model integrating physical time-frequency features with deep learning. First, vibration signals are processed using sliding windows, and a dual-channel neural network is developed to fuse these features, establishing a robust nonlinear mapping to milling force. Next, the Captum framework provides interpretability by visualizing feature contributions. Finally, extensive experiments under varied conditions validate its high prediction accuracy and strong generalization, achieving R² scores above 0.98 on primary tests with robust cross-dataset performance, demonstrating its effectiveness as a reliable milling force monitoring solution.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 95-129"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voxel-based rapid modeling of milling material removal for machining deformation prediction using finite cell method","authors":"Zixuan Wang ,&nbsp;Bingran Li ,&nbsp;Hui Zhang ,&nbsp;Peiqing Ye","doi":"10.1016/j.cirpj.2025.12.017","DOIUrl":"10.1016/j.cirpj.2025.12.017","url":null,"abstract":"<div><div>In the milling of thin-walled workpiece, deformation induced by cutting force is a critical factor affecting machining quality. Accurately and efficiently predicting the milling deformation caused by cutting force before machining is an essential method for deformation optimization in thin-walled workpiece machining. In this paper, we present a novel framework that integrates voxel-based modeling of material removal volume with the finite cell method (FCM) for the efficient prediction of machining deformation. This method leverages the voxel model's ordered data storage characteristics for efficient calculation of cutter-workpiece engagement (CWE) and instantaneous cutting forces, while enabling fast stiffness matrix updates without re-meshing. This approach significantly enhances the computational efficiency and accuracy of deformation prediction based on FCM and the voxel model, while simultaneously overcoming the mesh generation challenges inherent in traditional finite element method. Finally, both simulation and physical milling experiments on thin-walled parts were conducted to verify the significant improvement in computational efficiency over traditional algorithms and the accuracy in predicting cutting-force-induced deformation, demonstrating great potential for engineering applications.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 295-309"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of composition, recycling and processing on deep drawing performance of automotive 6016 aluminium sheets","authors":"Angela Thum ,&nbsp;Emir Hodžić ,&nbsp;Josef Domitner ,&nbsp;Stefan Pogatscher","doi":"10.1016/j.cirpj.2025.12.012","DOIUrl":"10.1016/j.cirpj.2025.12.012","url":null,"abstract":"<div><div>The ability to undergo complex forming operations without failure due to necking or cracking is an essential feature for automotive sheet material. The present study examines the effects of chemical composition and processing parameters on the mechanical properties of industrial 6016 aluminium sheets. The homogenization process and the Mn content were used to investigate the influence of dispersoids. Moreover, the solution treatment, the Si content and the use of recycling-friendly compositions were studied. A (semi-)industrial scale deep drawing tool with a fixed drawing depth and varying blank holder force was used to characterize the formability of the sheets at different drawing speeds. An analysis of the strain hardening behaviour via Kocks-Mecking plots revealed remarkable predictive power, enabling the estimation of the behaviour under complex sheet forming conditions from tensile testing. Microstructural investigations demonstrated that dispersoids or constituent particles exerted a minimal influence on strain hardening at high degrees of deformation, whereas dissolved Si exerted a significant influence, resulting in markedly enhanced forming performance. This is linked to the suppression of dynamic recovery, which in turn leads to the interesting results that an alloy produced with higher recycled content performed very well.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 70-80"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A grey-box approach based on Johnson-Cook constitutive model to improve predictions of mechanical loads of cutting simulations for normalized AISI 1045","authors":"Jan Wolf ,&nbsp;Erik Krumme ,&nbsp;Nithin Kumar Bandaru ,&nbsp;Martin Dienwiebel ,&nbsp;Andreas Zabel ,&nbsp;Hans-Christian Möhring","doi":"10.1016/j.cirpj.2025.12.014","DOIUrl":"10.1016/j.cirpj.2025.12.014","url":null,"abstract":"<div><div>In machining, high temperatures and strain rates impact the flow stress of the workpiece material, making it essential to understand the materials behaviour in these process conditions for meaningful finite element analysis (FEA) of the cutting process. The Johnson-Cook constitutive model, despite being the most widely applied, is reported to struggle in capturing the material behaviour outside of the reference conditions it was calibrated on. However determining these parameters in conventional material tests is challenging. To solve this issue, this study proposes a grey-box approach which aims to increase the accuracy of process force prediction of FEA, employing a Johnson-Cook model determined by experiments conducted on a Split-Hopkins Pressure Bar and compression tests at elevated temperatures on a Gleeble 3800c for AISI 1045, over a variety of cutting parameters. In total 110 cutting experiments and their corresponding simulations were carried out in a fully factorial experimental design with eleven cutting speeds and ten uncut chip thicknesses. Succeeding the white-box model, a black box model is trained to capture the non-linear behaviour between the simulation and the cutting experiments. Among the tested algorithms, XGBoost and Support Vector Regression outperformed Random Forests and Neural Network for predicting cutting force and feed force. The proposed grey-box approach showed an improved capability of predicting cutting force and feed force, reducing the mean absolute error and mean squared error compared to the white-box model by 97.9 % and 99.9 % for cutting force and by 94.9 % and 99.7 % for feed force, respectively. The grey-box model achieved a mean error of 1.3 % with a standard deviation of 0.1 in process force prediction.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 163-178"},"PeriodicalIF":5.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}