{"title":"Multi-criteria optimization method in µ - edm using TiN coated wc electrode","authors":"P. Nguyen","doi":"10.36897/jme/146874","DOIUrl":"https://doi.org/10.36897/jme/146874","url":null,"abstract":"","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46951435","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}
H. Klippel, Eduardo Gonzalez Sanchez, Margolis Isabel, M. Röthlin, M. Afrasiabi, Kuffa Michal, K. Wegener
The prediction of machining processes is a challenging task and usually requires a large experimental basis. These experiments are time-consuming and require manufacturing and testing of different tool geometries at various process conditions to find optimum machining settings. In this paper, a machine learning model of the orthogonal cutting process of Ti6Al4V is proposed to predict the cutting and feed forces for a wide range of process conditions with regards to rake angle, clearance angle, cutting edge radius, feed and cutting speed. The model uses training data generated by virtual experiments, which are conducted using physical based simulations of the orthogonal cutting process with the smoothed particle hydrodynamics (SPH). The ML training set is composed of input parameters, and output process forces from 2500 instances of GPU accelerated SPH simulations. The resulting model provides fast process force predictions and can consider the cutter geometry in comparison to classical analytical approaches.
{"title":"Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations","authors":"H. Klippel, Eduardo Gonzalez Sanchez, Margolis Isabel, M. Röthlin, M. Afrasiabi, Kuffa Michal, K. Wegener","doi":"10.36897/jme/147201","DOIUrl":"https://doi.org/10.36897/jme/147201","url":null,"abstract":"The prediction of machining processes is a challenging task and usually requires a large experimental basis. These experiments are time-consuming and require manufacturing and testing of different tool geometries at various process conditions to find optimum machining settings. In this paper, a machine learning model of the orthogonal cutting process of Ti6Al4V is proposed to predict the cutting and feed forces for a wide range of process conditions with regards to rake angle, clearance angle, cutting edge radius, feed and cutting speed. The model uses training data generated by virtual experiments, which are conducted using physical based simulations of the orthogonal cutting process with the smoothed particle hydrodynamics (SPH). The ML training set is composed of input parameters, and output process forces from 2500 instances of GPU accelerated SPH simulations. The resulting model provides fast process force predictions and can consider the cutter geometry in comparison to classical analytical approaches.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42544406","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}
D. Hoang Tien, V. Pham, N. Thien, Van Que Nguyen, T. Nguyen Duy, DuyTrinh Nguyen
To improve the screw machining accuracy in modern air compressors. This paper investigates three main issues during the development process for cycloid screw machining methods on five-axis CNC machine tools with high precision and efficiency. First, the theoretical basis of cycloid screw surface shaping and derivative of the final profile equation is established. Then, modeling the center trajectory and feed rate according to the cycloid screw profile is given. Next is the experimental setup and simulation of the cycloid screw machining process and discussions. The obtained surface quality prediction parameters are close to the actual measured value, which can be used as a reference model for five-axis CNC milling technology processes. All experimental results obtained by the proposed mathematical model show that a surface with good surface quality is created, meeting the requirements for surface quality. The main work can be used as references for engineers and technicians in practice.
{"title":"Parametric model analysis, geometrical characteristics and tool trajectories to surface roughness when machining the cycloid screw by a five-axis milling machine","authors":"D. Hoang Tien, V. Pham, N. Thien, Van Que Nguyen, T. Nguyen Duy, DuyTrinh Nguyen","doi":"10.36897/jme/147636","DOIUrl":"https://doi.org/10.36897/jme/147636","url":null,"abstract":"To improve the screw machining accuracy in modern air compressors. This paper investigates three main issues during the development process for cycloid screw machining methods on five-axis CNC machine tools with high precision and efficiency. First, the theoretical basis of cycloid screw surface shaping and derivative of the final profile equation is established. Then, modeling the center trajectory and feed rate according to the cycloid screw profile is given. Next is the experimental setup and simulation of the cycloid screw machining process and discussions. The obtained surface quality prediction parameters are close to the actual measured value, which can be used as a reference model for five-axis CNC milling technology processes. All experimental results obtained by the proposed mathematical model show that a surface with good surface quality is created, meeting the requirements for surface quality. The main work can be used as references for engineers and technicians in practice.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43486426","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}
Rapid evolution in sensing, data analysis, and industrial internet of things technologies had enabled the manufacturing of advanced smart tooling. This has been fused with effective digital inter-connectivity and integrated process control intelligence to form the industry I4.0 platform. This keynote paper presents the recent advances in smart tooling and intelligent control techniques for machining processes. Self-powered wireless sensing nodes have been utilized for non-intrusive measurement of process-born phenomena near the cutting zone, as well as tool wear and tool failure, to increase confidence in the process and tool condition monitoring accuracy. Cyber-physical adaptive control approaches have been developed to optimize the cycle time and cost while eliminating machined part defects. Novel artificial intelligence AI-based signal processing and modeling approaches were developed to guarantee the generalization and practicality of these systems. The paper concludes with the outlook for future work needed for seamless implementation of these developments in industry.
{"title":"Intelligent Cyber-Physical Monitoring and Control of I4.0 Machining Systems - An Overview and Future Perspectives","authors":"Mahmoud Hassan, Ahmad Sadek, M. Attia","doi":"10.36897/jme/147157","DOIUrl":"https://doi.org/10.36897/jme/147157","url":null,"abstract":"Rapid evolution in sensing, data analysis, and industrial internet of things technologies had enabled the manufacturing of advanced smart tooling. This has been fused with effective digital inter-connectivity and integrated process control intelligence to form the industry I4.0 platform. This keynote paper presents the recent advances in smart tooling and intelligent control techniques for machining processes. Self-powered wireless sensing nodes have been utilized for non-intrusive measurement of process-born phenomena near the cutting zone, as well as tool wear and tool failure, to increase confidence in the process and tool condition monitoring accuracy. Cyber-physical adaptive control approaches have been developed to optimize the cycle time and cost while eliminating machined part defects. Novel artificial intelligence AI-based signal processing and modeling approaches were developed to guarantee the generalization and practicality of these systems. The paper concludes with the outlook for future work needed for seamless implementation of these developments in industry.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47707943","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}
The industrial landscape is undergoing a series of fundamental changes, because of the advances in cutting-edge digital technologies. Under the framework of Industry 4.0 engineers have focused their effort on the development of new frameworks integrating digital technologies such as Big Data Analytics, Digital Twins, Extended Reality, and Artificial Intelligence, to upscale modern manufacturing systems, reduce uncertainties, and cope with the increased market volatility. However, in the upcoming industrial revolution, i.e., Industry 5.0, the research focus will be directed towards the new generation of human operators, the Operator 5.0. The purpose of this paper is to investigate the key technologies that will be the drivers towards the realization of the Operator 5.0 and to highlight the key challenges. Additional contribution is the proposal of a framework for the training and support of shopfloor technicians based on the utilization of Mixed Reality for manufacturing processes.
{"title":"Operator 5.0: A survey on enabling technologies and a framework for Digital Manufacturing based on Extended Reality","authors":"D. Mourtzis, J. Angelopoulos, N. Panopoulos","doi":"10.36897/jme/147160","DOIUrl":"https://doi.org/10.36897/jme/147160","url":null,"abstract":"The industrial landscape is undergoing a series of fundamental changes, because of the advances in cutting-edge digital technologies. Under the framework of Industry 4.0 engineers have focused their effort on the development of new frameworks integrating digital technologies such as Big Data Analytics, Digital Twins, Extended Reality, and Artificial Intelligence, to upscale modern manufacturing systems, reduce uncertainties, and cope with the increased market volatility. However, in the upcoming industrial revolution, i.e., Industry 5.0, the research focus will be directed towards the new generation of human operators, the Operator 5.0. The purpose of this paper is to investigate the key technologies that will be the drivers towards the realization of the Operator 5.0 and to highlight the key challenges. Additional contribution is the proposal of a framework for the training and support of shopfloor technicians based on the utilization of Mixed Reality for manufacturing processes.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47026675","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}
Temporally and spatially unstable thermal conditions lead to inhomogeneous thermoelastic changes in the workpiece geometry. Consequently, non-negligible geometric deviations are evident, especially when measuring large workpieces with narrow tolerances, which often take place in non-climatized production environments and thus make thermal monitoring indispensable. Accurate determination of the thermoelastic behaviour for complex and large geometries is a challenging task with computationally effortful or less accurate existing solutions. Thus, the development of innovative measurement and modelling approaches is subject of current research, whereat physical validation is a prerequisite. Therefore, the authors developed a method, enabling the emulation of typical process heat cycles on a turbine housing in combination with a geometric measurement system. The idea is to provide reproducible and reversible thermal conditions on a representative large workpiece and to investigate the resulting geometric deformation in an economically viable way. Throughout this study, an analogy test rig is presented, integrating different temperature sensors, two geometric measurement systems and thermal deformation models into one demonstrator. The demonstrator's first applications show insightful results, revealing accordance, but also unexpected deviations between the predicted and measured quantities. Moreover, it provides great potential for validation of more complex modelling approaches and innovative thermal condition monitoring systems for large precision workpieces.
{"title":"Model-Based, Experimental Thermoelastic Analysis of a Large Scale Turbine Housing","authors":"D. Emonts, M. Sanders, B. Montavon, R. Schmitt","doi":"10.36897/jme/146435","DOIUrl":"https://doi.org/10.36897/jme/146435","url":null,"abstract":"Temporally and spatially unstable thermal conditions lead to inhomogeneous thermoelastic changes in the workpiece geometry. Consequently, non-negligible geometric deviations are evident, especially when measuring large workpieces with narrow tolerances, which often take place in non-climatized production environments and thus make thermal monitoring indispensable. Accurate determination of the thermoelastic behaviour for complex and large geometries is a challenging task with computationally effortful or less accurate existing solutions. Thus, the development of innovative measurement and modelling approaches is subject of current research, whereat physical validation is a prerequisite. Therefore, the authors developed a method, enabling the emulation of typical process heat cycles on a turbine housing in combination with a geometric measurement system. The idea is to provide reproducible and reversible thermal conditions on a representative large workpiece and to investigate the resulting geometric deformation in an economically viable way. Throughout this study, an analogy test rig is presented, integrating different temperature sensors, two geometric measurement systems and thermal deformation models into one demonstrator. The demonstrator's first applications show insightful results, revealing accordance, but also unexpected deviations between the predicted and measured quantities. Moreover, it provides great potential for validation of more complex modelling approaches and innovative thermal condition monitoring systems for large precision workpieces.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41524062","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}
Force sensor integration into machine components is a promising approach to measure spatial process forces, especially, when regarding hexapod structures and kinematics. Rigid still-standing hexapod frameworks, such as clamping tables, are particular suitable for this approach, as no dynamic influences need to be taken into account within the measurement model and they allow a measurement in 6 degrees of freedom. On the other hand, the stiffness of rigid frameworks is reduced by force sensor integration significantly. In addition, many approaches apply joints or flexure hinges to reduced lateral forces and improve the measuring quality, which reduce the stiffness even more. In this contribution, the compliance of a clamping table with integrated force sensors and flexure hinges is determined by experimental measurements using a multiline laser interferometer, by analytic calculation, and by finite element simulation. In conclusion, the amount of stiffness reduction by force sensors and flexure hinges is quantified and different methods for compliance determination are compared.
{"title":"Spatial Compliance Measurement of a Clamping Table with Integrated Force Sensors","authors":"C. Friedrich, S. Ihlenfeldt","doi":"10.36897/jme/146533","DOIUrl":"https://doi.org/10.36897/jme/146533","url":null,"abstract":"Force sensor integration into machine components is a promising approach to measure spatial process forces, especially, when regarding hexapod structures and kinematics. Rigid still-standing hexapod frameworks, such as clamping tables, are particular suitable for this approach, as no dynamic influences need to be taken into account within the measurement model and they allow a measurement in 6 degrees of freedom. On the other hand, the stiffness of rigid frameworks is reduced by force sensor integration significantly. In addition, many approaches apply joints or flexure hinges to reduced lateral forces and improve the measuring quality, which reduce the stiffness even more. In this contribution, the compliance of a clamping table with integrated force sensors and flexure hinges is determined by experimental measurements using a multiline laser interferometer, by analytic calculation, and by finite element simulation. In conclusion, the amount of stiffness reduction by force sensors and flexure hinges is quantified and different methods for compliance determination are compared.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43011556","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}
{"title":"Application of Reliability-Centered Maintenance in Screw Tightening Machine of Hard Disk Drive Production System","authors":"N. Paoprasert, Wai Lin, Thepniramit Muneekaew","doi":"10.36897/jme/145272","DOIUrl":"https://doi.org/10.36897/jme/145272","url":null,"abstract":"","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41945715","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}
Research on optimization of technological parameters in micro-EDM is very important, and especially results in multi-objective optimization problem. It led to improve machining performance like machining accuracy, reduced electrode wear and improved surface quality. Recent studies mainly refer to the quality indicators of machining productivity and electrode wear, besides that machining accuracy and surface quality are also very important indicators but published results about them is very limited. In this study, Z Co-Ordinate (Z) and overcut (OC) in micro-EDM using tungsten carbide (WC) electrode for Ti-6Al-4V were decided simultaneously by TOPSIS. Technological parameters which include Voltage (V), Capacitance (C) and Response surface methodology (RPM) were investigated in the presented research work. The results showed that the quality parameters Z and OC at optimal conditions were significantly improved.The surface quality behind the microEDM is also analyzed and evaluated, and it is good.
{"title":"Multi-Objective Decision Making For Z Coordinator and Overcut in µ - EDM process using Tungsten Carbide Electrode for machining of Titanium Alloy","authors":"P. Nguyen","doi":"10.36897/jme/145490","DOIUrl":"https://doi.org/10.36897/jme/145490","url":null,"abstract":"Research on optimization of technological parameters in micro-EDM is very important, and especially results in multi-objective optimization problem. It led to improve machining performance like machining accuracy, reduced electrode wear and improved surface quality. Recent studies mainly refer to the quality indicators of machining productivity and electrode wear, besides that machining accuracy and surface quality are also very important indicators but published results about them is very limited. In this study, Z Co-Ordinate (Z) and overcut (OC) in micro-EDM using tungsten carbide (WC) electrode for Ti-6Al-4V were decided simultaneously by TOPSIS. Technological parameters which include Voltage (V), Capacitance (C) and Response surface methodology (RPM) were investigated in the presented research work. The results showed that the quality parameters Z and OC at optimal conditions were significantly improved.The surface quality behind the microEDM is also analyzed and evaluated, and it is good.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45840477","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}
The Mahalanobis-Taguchi System (MTS) is, today, widely used to define the optimal conditions for the design stage of product development especially, in the field of Artificial Intelligence (AI) considering the non-linear properties and non-digital data. In this paper, an approach to identify the several interactions in a MTS is proposed. The MTS contains four methods; Mahalanobis-Taguchi (MT) method, Mahalanobis Taguchi Adjoint (MTA) method, Recognition Taguchi (RT) method and Taguchi (T) method. The method to use for the analysis is selected based on the system’s properties. For the case of study used in this research, the unit space is created through the RT method and used to calculate the Mahalanobis-Taguchi distances ( MTD ). For the method proposed in this paper, the relationships between control factors and MTDs were firstly clarified by MTS (RT), then the same relationships were clarified using a modified design of experiments method, and the several interactions between control factors in MTS (RT) were finally identified by comparing the two relationships. Then effectiveness of the proposed method was evaluated by using a mathematical model.
{"title":"An Approach to Identify the Interactions Between the Control Factors in a Mahalanobis -Taguchi System","authors":"I. Tanabe","doi":"10.36897/jme/145273","DOIUrl":"https://doi.org/10.36897/jme/145273","url":null,"abstract":"The Mahalanobis-Taguchi System (MTS) is, today, widely used to define the optimal conditions for the design stage of product development especially, in the field of Artificial Intelligence (AI) considering the non-linear properties and non-digital data. In this paper, an approach to identify the several interactions in a MTS is proposed. The MTS contains four methods; Mahalanobis-Taguchi (MT) method, Mahalanobis Taguchi Adjoint (MTA) method, Recognition Taguchi (RT) method and Taguchi (T) method. The method to use for the analysis is selected based on the system’s properties. For the case of study used in this research, the unit space is created through the RT method and used to calculate the Mahalanobis-Taguchi distances ( MTD ). For the method proposed in this paper, the relationships between control factors and MTDs were firstly clarified by MTS (RT), then the same relationships were clarified using a modified design of experiments method, and the several interactions between control factors in MTS (RT) were finally identified by comparing the two relationships. Then effectiveness of the proposed method was evaluated by using a mathematical model.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43884184","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}
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