Procedia CIRP最新文献_第9页

Modeling Process Forces in CFRP Grinding: Influence of Cutting Materials and Coolant on Process Force Behavior

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.004

Alexander Brouschkin , Carsten Möller , Jan Hendrik Dege

{"title":"Modeling Process Forces in CFRP Grinding: Influence of Cutting Materials and Coolant on Process Force Behavior","authors":"Alexander Brouschkin , Carsten Möller , Jan Hendrik Dege","doi":"10.1016/j.procir.2025.02.004","DOIUrl":"10.1016/j.procir.2025.02.004","url":null,"abstract":"<div><div>Carbon Fibre Reinforced Polymer (CFRP) is favoured for its high strength to weight ratio, excellent directional mechanical and thermal properties, and the ability to be optimized in the direction of stress or heat flow. These properties make it ideal for power transmission applications. Meeting the high-quality requirements in this area requires a precise grinding process and a thorough understanding of cutting forces, which are influenced by different factors e.g. coolant usage, or cutting material. However, machining unidirectional CFRP is challenging due to its anisotropic behaviour, resulting in different machining forces for identical parameters with different fibre orientations.</div><div>A universal process-independent model was recently developed to describe the engagement conditions during oblique cutting of unidirectional CFRP by introducing the spatial fibre cutting angle θ<sub>0</sub> and the spatial engagement angle φ<sub>0</sub>. Using this description, an universal mechanistic machining force model for grinding of CFRP was developed.</div><div>In the paper, an extension of the model of oblique cutting for grinding is extended and experimentally verified, taking into account additional parameters e.g. coolant and cutting material. Therefore, the process forces were measured as a function of the spatial fibre cutting angles for different cutting materials, both with and without the use of coolant.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 14-19"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical chip formation simulations of AISI 304 steel with varying cutting tools

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.024

Erik Krumme , Kai Donnerbauer , Jannis Saelzer , Andreas Zabel , Frank Walther

{"title":"Numerical chip formation simulations of AISI 304 steel with varying cutting tools","authors":"Erik Krumme , Kai Donnerbauer , Jannis Saelzer , Andreas Zabel , Frank Walther","doi":"10.1016/j.procir.2025.02.024","DOIUrl":"10.1016/j.procir.2025.02.024","url":null,"abstract":"<div><div>Numerical chip formation simulations are a promising approach for determining the tool wear behavior of cemented carbide tools in a resource-efficient way. To achieve a high prediction quality of the chip formation simulations, suitable input data must be identified with regard to the friction and flow stress behavior of the material. Therefore, within this work, the flow stress and frictional behavior was first determined experimentally. Based on the investigations a numerical chip formation simulation was parameterized for future wear simulations. Beside the Split Hopkinson Pressure Bar (SHPB) test, quasi-static compression tests were conducted to characterize the flow stress behavior of the workpiece material. Furthermore, the frictional behavior was investigated using a special machine tool for fundamental chip formation analysis, taking into consideration the relative speed and measuring the contact temperatures for uncoated and TiAlN coated cutting tools. Based on the experimental data, different models for flow stress and friction were parameterized. Subsequently the models were implemented into the numerical chip formation simulation to model the thermo-mechanical load collective, whereas the results by means of the resulting forces were validated by orthogonal cutting tests. The parameterization of the friction models led to an improved prediction quality of the numerical chip formation simulation with regard to the cutting forces in comparison to a constant friction model.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 132-137"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive Maintenance of Wire Electrical Discharge Machining Using Long Short-Term Memory Networks for Improved Process Control

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.030

Namadi Vinod Kumar, D. Chakradhar

{"title":"Predictive Maintenance of Wire Electrical Discharge Machining Using Long Short-Term Memory Networks for Improved Process Control","authors":"Namadi Vinod Kumar, D. Chakradhar","doi":"10.1016/j.procir.2025.02.030","DOIUrl":"10.1016/j.procir.2025.02.030","url":null,"abstract":"<div><div>Time series forecasting and anomaly detection are becoming essential in smart manufacturing for prognostics and health management of a machine, especially where traditional methods struggle with the analysis of high frequency data. This study uses Long Short-Term Memory (LSTM) networks for detecting and predicting anomalies in wire electrical discharge machining (WEDM), specifically focusing on events like no-sparking and wire breaks. In closed-loop forecasting continuous numerical predictions are challenging in high-frequency data analysis, so a centroid-based approach was chosen. This method simplifies forecasting by using representative feature values that highlight important class differences in the data. With this closed-loop LSTM and centroid approach, the model effectively forecasts machine states up to five seconds ahead; a useful time frame for detecting critical issues such as wire breakage and no sparking before they impact operations. The results show that this method, combined with LSTM ability to capture time patterns, can handle complex, shifting conditions in WEDM. This approach could improve productivity and reduce unexpected downtime in smart manufacturing, offering a practical and efficient way to monitor and predict machine conditions.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 167-172"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Well-informed neural network: an approach for the prediction of the width of flank wear land in turning processes

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.016

S. Stemmer , B. Papenberg , L. Langenhorst , J. Sölter , D. Meyer , A. Fischer , K. Tracht , B. Karpuschewski

{"title":"Well-informed neural network: an approach for the prediction of the width of flank wear land in turning processes","authors":"S. Stemmer , B. Papenberg , L. Langenhorst , J. Sölter , D. Meyer , A. Fischer , K. Tracht , B. Karpuschewski","doi":"10.1016/j.procir.2025.02.016","DOIUrl":"10.1016/j.procir.2025.02.016","url":null,"abstract":"<div><div>Precise information on the current state of the tool wear is essential in machining processes in order to produce workpieces with an adequate surface quality as well as to use the full tool capacity and save valuable resources. In this work, different artificial neural networks are developed and compared to a regression model to predict the width of flank wear land by using measured cutting force as input data. In particular, a “well-informed” neural network approach is introduced. This is inspired by physics-informed neural networks, in which differential equations are taken into account, but uses empirical knowledge instead. Turning experiments with three different feeds were conducted and tool wear was measured at several process times until tool failure. Measured data for two of the feeds were used for training and data for the third feed were used for testing. As a result in the test scenario, the well-informed neural network with pre-knowledge based on Kienzle’s cutting force equation yielded the highest accuracy in tool wear prediction, outperforming both a regression approach with no artificial neural network extension and an artificial neural network with no pre-knowledge. By changing the datasets used for training and testing, the results also reveal a better extrapolation capability compared to the artificial neural network without pre-knowledge.</div><div><span><span><span><svg><path></path></svg><span><span>Download: <span>Download Acrobat PDF file (241KB)</span></span></span></span></span></span></div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 84-89"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Meso-scale geometric modeling of cutting edges on vitrified bonded aluminum oxide grinding wheels for the multi-scale simulation of internal plunge grinding processes

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.088

Nils Schmidt , Tim Furlan , Jan Peters , Monika Kipp , Stefan Kaschnitz-Biegl , Andreas Menzel , Friedrich Bleicher , Dirk Biermann

{"title":"Meso-scale geometric modeling of cutting edges on vitrified bonded aluminum oxide grinding wheels for the multi-scale simulation of internal plunge grinding processes","authors":"Nils Schmidt , Tim Furlan , Jan Peters , Monika Kipp , Stefan Kaschnitz-Biegl , Andreas Menzel , Friedrich Bleicher , Dirk Biermann","doi":"10.1016/j.procir.2025.02.088","DOIUrl":"10.1016/j.procir.2025.02.088","url":null,"abstract":"<div><div>Vitrified bonded aluminum oxide grinding wheels are widespread in use for many applications in grinding, such as internal plunge grinding. However, there are challenges when it comes to the measurement, analysis and (geometric) modeling of their topography, which is crucial to understand and model the influence of the topography on the process behavior. Methodological advances allow for the detailed digitization of the topography using optical profilometry despite the challenging optical properties of these grinding wheels. Based on the digitized grinding wheel topography, methods are presented to process the measurement data in order to create a representative set of geometric cutting edge models. This set is subsequently used to generate a full-sized virtual grinding wheel with realistic topography. Using established methods in an efficient implementation that scales to many CPU-cores, the interaction between the workpiece model and each individual cutting edge can be calculated at meso-scale. Therefore, it is possible to analyze for example the chip thickness or the material removal rate per cutting edge. Furthermore, additional models can be applied, based on the analysis of the engagement situation, which is demonstrated using a cutting force model.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 513-518"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of tool cavity conditions on damping, chatter mitigation, and surface quality in internally cooled milling tools

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.038

Ramazan Hakkı Namlu , Hakan Dogan , Muhammet Ozsoy

{"title":"Effect of tool cavity conditions on damping, chatter mitigation, and surface quality in internally cooled milling tools","authors":"Ramazan Hakkı Namlu , Hakan Dogan , Muhammet Ozsoy","doi":"10.1016/j.procir.2025.02.038","DOIUrl":"10.1016/j.procir.2025.02.038","url":null,"abstract":"<div><div>Chatter is a critical factor limiting productivity and efficiency in machining processes. Cutting tools significantly impact chatter stability, as they often serve as the most flexible component. The influence of cutting tools on chatter varies depending on their design and cooling mechanisms. Internally cooled cutting tools, commonly used in industrial applications, have the potential to exhibit distinct damping characteristics due to the presence of internal cavities, differentiating them from conventional solid tools. This study explores the effects of internally cooled milling cutting comparing an empty cavity cutting tool with a tool filled with viscous fluid. The primary objective is to evaluate how these conditions influence the damping of the machining system and their subsequent impact on surface quality, a key outcome sensitive to chatter. Surface topography and roughness measurements were taken after the experiments to assess changes in surface quality. The findings offer valuable insights into the role of internal cooling and fluid properties in not only chatter but also vibration suppressions in milling operations, highlighting their potential to enhance machining performance.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 215-220"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implicitly labeled Forecasting based Tool Condition Monitoring in Machining Processes

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.082

Tim Reeber , Hans-Christian Möhring

{"title":"Implicitly labeled Forecasting based Tool Condition Monitoring in Machining Processes","authors":"Tim Reeber , Hans-Christian Möhring","doi":"10.1016/j.procir.2025.02.082","DOIUrl":"10.1016/j.procir.2025.02.082","url":null,"abstract":"<div><div>Self-optimizing machining systems are cyber-physical systems that enable autonomous monitoring of CNC machining processes. The realization of such a system is achieved through a variety of efforts in the areas of CNC control digitization, sensor integration and machine learning. In terms of sensor signal usage in machining, approaches that aim to use existing data sources on the machine are an efficient way to implement this in practice. However, multiple machine learning approaches in process and tool condition monitoring rely heavily on classical supervised approaches, which demand machining-technology-specific feature engineering tasks such as process-dependent statistical values and labeling efforts. Both tasks require expert knowledge as well as a solid data infrastructure for storing labeled data. This paper aims to present an implicitly labeled time series forecasting approach to eliminate the need for labeling and feature engineering, which poses positive effects for real world applications based on CNC-internal control data. By using histogram-based outlier scores on the prediction residual after comparing the prediction with the real signal pattern, the approach offers further possibilities for building a fully functional, self-optimizing system. Different drilling operations are considered in which the cutting speed, feed rate, tool diameter and material are varied. In addition, the transfer of the models to another machine with other parameter variations is discussed and whether the net can be used to detect both anomalies and wear.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 477-482"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-based Characterization of Fused Filament Fabrication from Temporal Thermal Data

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.098

Ming Wu , Jie Zhang , Robrecht Abts , Eleonora Ferraris , Mathias Verbeke

{"title":"Deep Learning-based Characterization of Fused Filament Fabrication from Temporal Thermal Data","authors":"Ming Wu , Jie Zhang , Robrecht Abts , Eleonora Ferraris , Mathias Verbeke","doi":"10.1016/j.procir.2025.02.098","DOIUrl":"10.1016/j.procir.2025.02.098","url":null,"abstract":"<div><div>The stability of fused filament fabrication (FFF), crucial for achieving high production efficiency, is considerably affected by the manner in which temperature distributes and propagates throughout the printed region. The application of infrared (IR) thermal cameras for the collection of spatiotemporal thermal data during strand deposition is investigated in this research. A classification framework utilizing the X3D deep learning model was developed to categorize process states as Normal, Transition, or Failure. The X3D model demonstrated high reliability in distinguishing Normal from Abnormal states with an accuracy of approximately 95% and a weighted F1-score of 0.95, although performance in categorizing Transition from Failure states was constrained to around 65%. The effectiveness of the model in discerning stable and unstable process conditions has been affirmed through the findings, resulting in the provision of a practical tool for real-time quality assurance in FFF. In addition, the X3D model showcased exceptional computational efficiency, processing a 1-second IR video clip (32 Hz) in 6 milliseconds while utilizing only 0.67 GB of GPU memory, making this setup suitable for in-process monitoring and control.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 573-578"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Molecular dynamics simulation and experimental study of laser-assisted machining of SiCp/Al composites

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.002

Feijie Cui , Hang Zhang , Minghui Yang , Ben Deng , Jiawei Lv , Rong Yan , Fangyu Peng

{"title":"Molecular dynamics simulation and experimental study of laser-assisted machining of SiCp/Al composites","authors":"Feijie Cui , Hang Zhang , Minghui Yang , Ben Deng , Jiawei Lv , Rong Yan , Fangyu Peng","doi":"10.1016/j.procir.2025.02.002","DOIUrl":"10.1016/j.procir.2025.02.002","url":null,"abstract":"<div><div>High-quality machining of SiCp/Al composite faces a number of challenges due to the presence of hard and brittle SiC particles, making it a typical difficult-to-machine material. Conventional machining (CM) inevitably leads to high cutting forces and poor surface quality, which significantly limit the potential applications of SiCp/Al composites. Laser-assisted machining (LAM) has been evidenced that it can remarkably improve the machinability of SiCp/Al composites, however, the microscopic removal mechanism of the material under the effect of laser remains to be investigated in depth. In this paper, the molecular dynamics (MD) simulation and Transmission Electron Microscope (TEM) experiments are employed to carry out further research. A MD model for LAM of SiCp/Al composites is established, which is applied to analyze the dynamic evolution of the cutting forces and the dislocations of the material during the machining. The results show that with the rise of laser power, the dislocation density inside the Al matrix decreases gradually. The coordinated deformation ability of SiCp/Al composites is enhanced, and the cutting force tends to decrease. The high consistency between experimental and simulation results verifies the validity of the MD model.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 2-7"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coupled CFD model of tool environment and workspace to determine the convective heat transfer in jet cooling of milling processes in machine tools

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.049

Steffen Brier , Alexander Geist , Janine Glänzel , Christian Naumann , Joachim Regel , Martin Dix , Steffen Ihlenfeldt

{"title":"Coupled CFD model of tool environment and workspace to determine the convective heat transfer in jet cooling of milling processes in machine tools","authors":"Steffen Brier , Alexander Geist , Janine Glänzel , Christian Naumann , Joachim Regel , Martin Dix , Steffen Ihlenfeldt","doi":"10.1016/j.procir.2025.02.049","DOIUrl":"10.1016/j.procir.2025.02.049","url":null,"abstract":"<div><div>The responsible use of resources is an essential part of the manufacturing of industrial products. This includes the economical use of cooling lubricant and requires precise knowledge of the cooling mechanisms and their effect on the accuracy of the machine tool and thus the thermal error. A temporal and spatial resolution of the dynamic coolant flow near the tool and in the entire workspace in a single model would require a large simulation time. Therefore, a composite model was developed, that consists of a near-tool model and a larger surrounding workspace model. The required static near-tool cooling lubricant distribution is obtained via data discretization methods from a separate static simulation that resolves the turbulence of the cooling lubricant created by the tool rotation. The identified coolant distribution is integrated into a CFD near-tool model (with simplified tool geometry) which is coupled with a surrounding CFD workspace model. The workspace model is thus able to identify the effects of the coolant wetting on the machine surface temperature and finally, using thermo-elastic FEM simulations, on the resulting thermal error. This approach allows the composite model to simulate the entire workspace with reduced simulation effort and map the coolant-influenced heat transfer coefficients on the machine surface.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 280-285"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0