{"title":"Development on AI Optimizing Technology of NC Program Using Tool Free-Cutting Temperature for Turning","authors":"I. Tanabe, H. Isobe","doi":"10.36897/jme/157374","DOIUrl":"https://doi.org/10.36897/jme/157374","url":null,"abstract":", manufacturing","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44153448","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}
Mirror-polishing processes have recently been required to add high quality, high-precision precision and multi-functionality to industrial products. However, this requires skilled manual work, making it difficult to improve productivity. Automatic polishing of these products is also very difficult. Furthermore, mirror-polishing process of stainless steel, titanium and glass, which are often used in high-value-added medical products, is also difficult. Therefore, in this study, a technology for high-precision, automatic mirror polishing of three difficult-to-polish materials - stainless steel, titanium and glass - was developed and evaluated. First, the difficult-to-polish properties of different materials were considered, and then tools were developed to improve these properties and enable suitable mirror-polishing processes. Next, a polishing slurry was developed to improve the surface roughness (Rz=0.1 µm or less). Finally, the optimum polishing conditions for the proposed mirror-polishing process were determined by design of experiment and then evaluated. It was concluded that;(1) In order to perform mirror-polishing processes of three types of workpieces, stainless steel (SUS304), titanium (pure titanium) and glass (quartz glass), new polishing tool and slurries corresponding to each material were developed and the optimum polishing processing conditions were clarified, (2) Using (1), mirror-polishing processes of each material with a surface roughness of Rz=0.1μm has been achieved.
{"title":"Development of mirror-polishing process technology for difficult-to-polish materials","authors":"I. Tanabe, H. Isobe","doi":"10.36897/jme/157211","DOIUrl":"https://doi.org/10.36897/jme/157211","url":null,"abstract":"Mirror-polishing processes have recently been required to add high quality, high-precision precision and multi-functionality to industrial products. However, this requires skilled manual work, making it difficult to improve productivity. Automatic polishing of these products is also very difficult. Furthermore, mirror-polishing process of stainless steel, titanium and glass, which are often used in high-value-added medical products, is also difficult. Therefore, in this study, a technology for high-precision, automatic mirror polishing of three difficult-to-polish materials - stainless steel, titanium and glass - was developed and evaluated. First, the difficult-to-polish properties of different materials were considered, and then tools were developed to improve these properties and enable suitable mirror-polishing processes. Next, a polishing slurry was developed to improve the surface roughness (Rz=0.1 µm or less). Finally, the optimum polishing conditions for the proposed mirror-polishing process were determined by design of experiment and then evaluated. It was concluded that;(1) In order to perform mirror-polishing processes of three types of workpieces, stainless steel (SUS304), titanium (pure titanium) and glass (quartz glass), new polishing tool and slurries corresponding to each material were developed and the optimum polishing processing conditions were clarified, (2) Using (1), mirror-polishing processes of each material with a surface roughness of Rz=0.1μm has been achieved.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42426242","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}
Utilising microphones as audible sound sensors for monitoring a single-side grinding process with lapping kinematics is presented in the paper. The audible sound generated during grinding depended on the cutting properties of electroplated tools with D107 diamond grains and different thicknesses of the nickel bond. The tool wear affected the obtained technological effects such as material removal rate and the surface roughness of Al 2 O 3 ceramic samples. The relationship between the quantities that characterise the sound signal and the surface roughness of machined surfaces was examined with the use of spectral analysis of the sound signal in the frequency domain with a focus on the Ra parameter. The decreasing amplitude indicated a better surface finish, down to Ra = 0.23 µm. The developed method and the obtained results will facilitate the practical use of the electroplated tools in the lap-grinding technology without interrupting the process before obtaining the required surface roughness.
{"title":"Method of monitoring of the grinding process with lapping kinematics using audible sound analysis","authors":"M. Deja","doi":"10.36897/jme/157255","DOIUrl":"https://doi.org/10.36897/jme/157255","url":null,"abstract":"Utilising microphones as audible sound sensors for monitoring a single-side grinding process with lapping kinematics is presented in the paper. The audible sound generated during grinding depended on the cutting properties of electroplated tools with D107 diamond grains and different thicknesses of the nickel bond. The tool wear affected the obtained technological effects such as material removal rate and the surface roughness of Al 2 O 3 ceramic samples. The relationship between the quantities that characterise the sound signal and the surface roughness of machined surfaces was examined with the use of spectral analysis of the sound signal in the frequency domain with a focus on the Ra parameter. The decreasing amplitude indicated a better surface finish, down to Ra = 0.23 µm. The developed method and the obtained results will facilitate the practical use of the electroplated tools in the lap-grinding technology without interrupting the process before obtaining the required surface roughness.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41897369","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}
In this work, Cr 3 C 2 -NiCr ceramic coating with NiCr content of 30% was created on E355 (St 52-3)/1.0060 alloy steel substrate by air plasma thermal spraying (APS) method. The adhesion, tensile strength of the coating were studied in relation to spray parameters including current intensity, powder feed rate, and stand-off distance according to experimental planning. Experiments were designed based on Central Composite Design method. The adhesion and tensile strength of the coatings were measured by a tensile-compression machine. Analysis of variance (ANOVA) were used to build regression functions expressing the relationship between each property and spray parameters. The experimental results showed that those spray parameters significantly influence the properties of the coating. ANOVA results indicated that the spray parameters have different influences for each coating properties. In addition, regression model of the properties gives prediction results very close to experimental results. The optimization problem was solved in order to achieve the maximum value of adhesion and tensile strength of the coating. Values of the spray parameters for Cr 3 C 2 – NiCr coating to achieve the above criteria have been determined and proposed in this study.
{"title":"Research on Optimizing Spray Parameters for Cr3C2 - NiCr Coating Created on Alloy Steel by Plasma Spraying Technique","authors":"Thao Dang","doi":"10.36897/jme/157047","DOIUrl":"https://doi.org/10.36897/jme/157047","url":null,"abstract":"In this work, Cr 3 C 2 -NiCr ceramic coating with NiCr content of 30% was created on E355 (St 52-3)/1.0060 alloy steel substrate by air plasma thermal spraying (APS) method. The adhesion, tensile strength of the coating were studied in relation to spray parameters including current intensity, powder feed rate, and stand-off distance according to experimental planning. Experiments were designed based on Central Composite Design method. The adhesion and tensile strength of the coatings were measured by a tensile-compression machine. Analysis of variance (ANOVA) were used to build regression functions expressing the relationship between each property and spray parameters. The experimental results showed that those spray parameters significantly influence the properties of the coating. ANOVA results indicated that the spray parameters have different influences for each coating properties. In addition, regression model of the properties gives prediction results very close to experimental results. The optimization problem was solved in order to achieve the maximum value of adhesion and tensile strength of the coating. Values of the spray parameters for Cr 3 C 2 – NiCr coating to achieve the above criteria have been determined and proposed in this study.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41473224","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}
Nowadays, plastic gears are more commonly used. The Triply Periodic Minimal Surfaces (TPMS) structure can perfect the design to reduce weight but still achieve the desired workability criteria. It can also be adjusted more easily and scientifically than the empirical structure optimization based on experience. Currently, the fabrication of gears with complex internal structures such as TPMS is possible thanks to 3D printing technology. This study investigates the mechanical properties of a TPMS structure when applied to Polyetheretherketone (PEEK) plastic gears. The research content includes displacement, deformation, and Von-mises stress to evaluate the stiffness and strength of gears. The structure used to optimize the gear mass is the Gyroid structure, developed in the cylindrical cell map and studied in the paper. The goal of the research is to apply the Gyroid structure to optimize mass while still ensuring gear performance. This study not only offers new insight into the importance of the control variables for TPMS structures but also provides a mass lean process for gear designers. It uses experimental design methods to choose a suitable topology structure, and the final research result is a regression equation, which clearly shows the close relationship between the volume reduction and displacement with the specified control variables of the unit cell. From there, it is possible to determine the proper amount of material reduction while ensuring the working ability of the gear transmission.
{"title":"Lightweight Plastic Gear Body using Gyroid Structure for Additive Manufacturing","authors":"Loc Nguyen, K. Nguyen","doi":"10.36897/jme/157077","DOIUrl":"https://doi.org/10.36897/jme/157077","url":null,"abstract":"Nowadays, plastic gears are more commonly used. The Triply Periodic Minimal Surfaces (TPMS) structure can perfect the design to reduce weight but still achieve the desired workability criteria. It can also be adjusted more easily and scientifically than the empirical structure optimization based on experience. Currently, the fabrication of gears with complex internal structures such as TPMS is possible thanks to 3D printing technology. This study investigates the mechanical properties of a TPMS structure when applied to Polyetheretherketone (PEEK) plastic gears. The research content includes displacement, deformation, and Von-mises stress to evaluate the stiffness and strength of gears. The structure used to optimize the gear mass is the Gyroid structure, developed in the cylindrical cell map and studied in the paper. The goal of the research is to apply the Gyroid structure to optimize mass while still ensuring gear performance. This study not only offers new insight into the importance of the control variables for TPMS structures but also provides a mass lean process for gear designers. It uses experimental design methods to choose a suitable topology structure, and the final research result is a regression equation, which clearly shows the close relationship between the volume reduction and displacement with the specified control variables of the unit cell. From there, it is possible to determine the proper amount of material reduction while ensuring the working ability of the gear transmission.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44300688","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":"Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network","authors":"H. Klippel, Samuel Pflaum, M. Kuffa, K. Wegener","doi":"10.36897/jme/156091","DOIUrl":"https://doi.org/10.36897/jme/156091","url":null,"abstract":"","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42326162","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}
Additive Manufacturing (AM) consist in producing parts by depositing material in successive layers. These step-by-step processes proposes new innovative directions for high value components: complex geometries are accessible without strong efforts (such as hollow or lattice structures which dramatically reduce the component weight while keeping their at least similar mechanical properties), assemblies can be simplified, spare parts can be realized at demand… Hence, AM has benefitted from large research efforts over the last decade, almost all existing industrial sectors have benefitted from them. This paper introduces some opportunities and the associated challenges attached to Additive Manufacturing, to produce large metallic components for naval aeronautics and train industries. In particular, two innovative approaches are discussed in details: hybrid manufacturing and twin manufacturing. Hybrid manufacturing consists in integrating AM together with other processes for the realization of components, with the objective to benefit from the interests of each process while avoiding its drawbacks. Hence, AM can realize complex geometries or offer low buy-to-fly ratios while high speed machining generates very good surface properties (position, roughness). Processes can be carried out sequentially or simultaneously on the features to manufacture and finding the optimal manufacturing work plan can be challenging. The paper introduces some hybrid approaches developed in the laboratory. Twin manufacturing uses models and multiphysics simulation methods to create a digital clone of the process implementation within the manufacturing environment. Manufacturing preparation and optimization can be carried in the virtual workshop where various configurations and choices can be tested before being selected. To enhance its accuracy, the digital twin can also be fed by monitoring data captured during the process. Several digital twins developed in the laboratory are provided. The paper is illustrated with several proof-of-concept parts made with SLM, LMD, WAAM and hybrid approaches in the laboratory. Among them, a hollow propellers that has the same hydrodynamics efficiency for a reduced weight for the naval industry, an aircraft structural panel that demonstrates simplified assemblies increased performance/mass ratio, a train component that shows the ability to produce structural parts at demand.
{"title":"Opening New Opportunities For Aeronautic, Naval And Train Large Components Realization With Hybrid And Twin Manufacturing","authors":"M. Rauch, J. Hascoet","doi":"10.36897/jme/155812","DOIUrl":"https://doi.org/10.36897/jme/155812","url":null,"abstract":"Additive Manufacturing (AM) consist in producing parts by depositing material in successive layers. These step-by-step processes proposes new innovative directions for high value components: complex geometries are accessible without strong efforts (such as hollow or lattice structures which dramatically reduce the component weight while keeping their at least similar mechanical properties), assemblies can be simplified, spare parts can be realized at demand… Hence, AM has benefitted from large research efforts over the last decade, almost all existing industrial sectors have benefitted from them. This paper introduces some opportunities and the associated challenges attached to Additive Manufacturing, to produce large metallic components for naval aeronautics and train industries. In particular, two innovative approaches are discussed in details: hybrid manufacturing and twin manufacturing. Hybrid manufacturing consists in integrating AM together with other processes for the realization of components, with the objective to benefit from the interests of each process while avoiding its drawbacks. Hence, AM can realize complex geometries or offer low buy-to-fly ratios while high speed machining generates very good surface properties (position, roughness). Processes can be carried out sequentially or simultaneously on the features to manufacture and finding the optimal manufacturing work plan can be challenging. The paper introduces some hybrid approaches developed in the laboratory. Twin manufacturing uses models and multiphysics simulation methods to create a digital clone of the process implementation within the manufacturing environment. Manufacturing preparation and optimization can be carried in the virtual workshop where various configurations and choices can be tested before being selected. To enhance its accuracy, the digital twin can also be fed by monitoring data captured during the process. Several digital twins developed in the laboratory are provided. The paper is illustrated with several proof-of-concept parts made with SLM, LMD, WAAM and hybrid approaches in the laboratory. Among them, a hollow propellers that has the same hydrodynamics efficiency for a reduced weight for the naval industry, an aircraft structural panel that demonstrates simplified assemblies increased performance/mass ratio, a train component that shows the ability to produce structural parts at demand.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41787731","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}
A new approach is presented to monitor the average cutting forces that are used for the calculation of the average cutting coefficients through neural networks using available controller signals. The cutting forces and the relevant controller signals are measured using a dynamometer and commercially available software supplied by the controller manufacturer in the calibration stage. Then a neural network is trained, which treats these controller signals as inputs and the cutting forces as the outputs. Finally, the average cutting forces for a new milling operation are predicted using the trained neural network without using a dynamometer. The proposed approach is validated using an experimental study, where a good match between predictions and measured forces is achieved. It is also shown that cutting coefficients can be calibrated and stability lobe diagrams can be generated using this method.
{"title":"Monitoring of the Average Cutting Forces from Controller Signals using Artificial Neural Networks","authors":"Nevzat Bircan Bugdayci, K. Wegener, M. Postel","doi":"10.36897/jme/154801","DOIUrl":"https://doi.org/10.36897/jme/154801","url":null,"abstract":"A new approach is presented to monitor the average cutting forces that are used for the calculation of the average cutting coefficients through neural networks using available controller signals. The cutting forces and the relevant controller signals are measured using a dynamometer and commercially available software supplied by the controller manufacturer in the calibration stage. Then a neural network is trained, which treats these controller signals as inputs and the cutting forces as the outputs. Finally, the average cutting forces for a new milling operation are predicted using the trained neural network without using a dynamometer. The proposed approach is validated using an experimental study, where a good match between predictions and measured forces is achieved. It is also shown that cutting coefficients can be calibrated and stability lobe diagrams can be generated using this method.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44547232","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":"Novel One-Degree of Freedom Helix Architecture for Additive Manufacturing","authors":"tugdual le néel, Hascoët Jean-Yves","doi":"10.36897/jme/152244","DOIUrl":"https://doi.org/10.36897/jme/152244","url":null,"abstract":"","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43334073","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 current development of production engineering takes place through the innovative improvement of machine tools and machining processes at the constantly growing application of intelligent self-improvement functions. Machine learning opens up possibilities for machine tool self-improvement in real time. This paper discusses the state of knowledge relating to the application of machine learning for precise and cost-effective thermal error self-compensation. Data acquisition and processing, models and model learning and self-learning methods are also considered. Three highly effective error compensation systems (supported with machine learning) are analysed and conclusions and recommendations for future research are formulated.
{"title":"Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review","authors":"Robert Czwartosz, J. Jedrzejewski","doi":"10.36897/jme/152246","DOIUrl":"https://doi.org/10.36897/jme/152246","url":null,"abstract":"The current development of production engineering takes place through the innovative improvement of machine tools and machining processes at the constantly growing application of intelligent self-improvement functions. Machine learning opens up possibilities for machine tool self-improvement in real time. This paper discusses the state of knowledge relating to the application of machine learning for precise and cost-effective thermal error self-compensation. Data acquisition and processing, models and model learning and self-learning methods are also considered. Three highly effective error compensation systems (supported with machine learning) are analysed and conclusions and recommendations for future research are formulated.","PeriodicalId":37821,"journal":{"name":"Journal of Machine Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42603511","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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