Pablo Moreno-Garibaldi, M. Alvarez-Vera, J. Beltrán-Fernández, R. Carrera-Espinoza, H. M. Hdz-García, J. Díaz-Guillén, Rita Muñoz-Arroyo, Javier A. Ortega, Paul Molenda
The 17-4 PH stainless steel is widely used in the aerospace, petrochemical, chemical, food, and general metallurgical industries. The present study was conducted to analyze the mechanical properties of two types of 17-4 PH stainless steel—commercial cold-rolled and direct metal laser sintering (DMLS) manufactured. This study employed linear and nonlinear tensile FEM simulations, combined with various materials characterization techniques such as tensile testing and nanoindentation. Moreover, microstructural analysis was performed using metallographic techniques, optical microscopy, scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). The results on the microstructure for 17-4 PH DMLS stainless steel reveal the layers of melting due to the laser process characterized by complex directional columnar structures parallel to the DMLS build direction. The mechanical properties obtained from the simple tension test decreased by 17% for the elastic modulus, 7.8% for the yield strength, and 7% for the ultimate strength for 17-4 PH DMLS compared with rolled 17-4 PH stainless steel. The FEM simulation using the experimental tension test data revealed that the 17-4 PH DMLS stainless steel experienced a decrease in the yield strength of ~8% and in the ultimate strength of ~11%. A reduction of the yield strength of the material was obtained as the grain size increased.
{"title":"Characterization of Microstructural and Mechanical Properties of 17-4 PH Stainless Steel by Cold Rolled and Machining vs. DMLS Additive Manufacturing","authors":"Pablo Moreno-Garibaldi, M. Alvarez-Vera, J. Beltrán-Fernández, R. Carrera-Espinoza, H. M. Hdz-García, J. Díaz-Guillén, Rita Muñoz-Arroyo, Javier A. Ortega, Paul Molenda","doi":"10.3390/jmmp8020048","DOIUrl":"https://doi.org/10.3390/jmmp8020048","url":null,"abstract":"The 17-4 PH stainless steel is widely used in the aerospace, petrochemical, chemical, food, and general metallurgical industries. The present study was conducted to analyze the mechanical properties of two types of 17-4 PH stainless steel—commercial cold-rolled and direct metal laser sintering (DMLS) manufactured. This study employed linear and nonlinear tensile FEM simulations, combined with various materials characterization techniques such as tensile testing and nanoindentation. Moreover, microstructural analysis was performed using metallographic techniques, optical microscopy, scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). The results on the microstructure for 17-4 PH DMLS stainless steel reveal the layers of melting due to the laser process characterized by complex directional columnar structures parallel to the DMLS build direction. The mechanical properties obtained from the simple tension test decreased by 17% for the elastic modulus, 7.8% for the yield strength, and 7% for the ultimate strength for 17-4 PH DMLS compared with rolled 17-4 PH stainless steel. The FEM simulation using the experimental tension test data revealed that the 17-4 PH DMLS stainless steel experienced a decrease in the yield strength of ~8% and in the ultimate strength of ~11%. A reduction of the yield strength of the material was obtained as the grain size increased.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087377","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}
This experimental study probes the dynamic behaviour of a 3 mm ABS sheet during positive mould vacuum-assisted thermoforming. In this process, the sheet undergoes large and fast deformations caused by the applied vacuum and mechanical stretching by the mould. The objective is to elucidate the complexities of these large, rapid, and non-isothermal deformations. The non-isothermal conditions are caused by the radiative heating of the sheet, convective heat loss to the surrounding air, and conductive heat transfer from the sheet to the mould. By utilizing stereo digital image correlation (DIC) in tandem with thermal imaging, the present study accurately maps the occurring displacement, strain, and strain rate field in relation to real-time temperature variation in the material. The study progresses to observe the ABS material from the moment it contacts the mould until it conforms to a positive 250 mm diameter semi-sphere cast aluminum mould. The DIC methods are validated by comparing thickness values derived from DIC’s principal strain directions to ultrasonic thickness gauge readings. This knowledge not only broadens the understanding of the thermo-mechanical behaviour of the material but also aids in optimizing process parameters for improved thickness uniformity in thermoformed products.
{"title":"In Situ Stereo Digital Image Correlation with Thermal Imaging as a Process Monitoring Method in Vacuum-Assisted Thermoforming","authors":"Rasoul Varedi, B. Buffel, F. Desplentere","doi":"10.3390/jmmp8020049","DOIUrl":"https://doi.org/10.3390/jmmp8020049","url":null,"abstract":"This experimental study probes the dynamic behaviour of a 3 mm ABS sheet during positive mould vacuum-assisted thermoforming. In this process, the sheet undergoes large and fast deformations caused by the applied vacuum and mechanical stretching by the mould. The objective is to elucidate the complexities of these large, rapid, and non-isothermal deformations. The non-isothermal conditions are caused by the radiative heating of the sheet, convective heat loss to the surrounding air, and conductive heat transfer from the sheet to the mould. By utilizing stereo digital image correlation (DIC) in tandem with thermal imaging, the present study accurately maps the occurring displacement, strain, and strain rate field in relation to real-time temperature variation in the material. The study progresses to observe the ABS material from the moment it contacts the mould until it conforms to a positive 250 mm diameter semi-sphere cast aluminum mould. The DIC methods are validated by comparing thickness values derived from DIC’s principal strain directions to ultrasonic thickness gauge readings. This knowledge not only broadens the understanding of the thermo-mechanical behaviour of the material but also aids in optimizing process parameters for improved thickness uniformity in thermoformed products.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086274","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}
Karthik Ravi Krishna Murthy, F. Akyel, U. Reisgen, S. Olschok, Dhamini Mahendran
In this study, the evolution of volume fractions during laser beam welding (LBW) of stainless steel, with a specific focus on incorporating the low transformation temperature (LTT) effect using the dilatometer, has been proposed. The LTT effect refers to the phase transformations that occur at lower temperatures and lead to the formation of a martensitic microstructure, which will significantly influence the residual stresses and distortion of the welded joints. In this research, the LTT conditions are achieved by varying the Cr and Ni content in the weld seam by varying the weld parameter, including laser power, welding speed and filler wire speed. The dilatometer analysis technique is employed to simulate the thermal conditions encountered during LBW. By subjecting the stainless steel samples to controlled heating and cooling cycles, the kinetics of the volume fractions can be measured using the lever rule and empirical method (KOP and Lee). The phase transformation simulation model is computed by integrating the thermal and metallurgical effects to predict the volume fractions in LBW joints and has been validated using dilatometer results. This provides valuable insight into the relationship between welding parameters and phase transformations in stainless steel with the LTT effect during laser beam welding. Using this relationship, the weld quality can be improved by reducing the residual stresses and distortion.
{"title":"Modelling the Evolution of Phases during Laser Beam Welding of Stainless Steel with Low Transformation Temperature Combining Dilatometry Study and FEM","authors":"Karthik Ravi Krishna Murthy, F. Akyel, U. Reisgen, S. Olschok, Dhamini Mahendran","doi":"10.3390/jmmp8020050","DOIUrl":"https://doi.org/10.3390/jmmp8020050","url":null,"abstract":"In this study, the evolution of volume fractions during laser beam welding (LBW) of stainless steel, with a specific focus on incorporating the low transformation temperature (LTT) effect using the dilatometer, has been proposed. The LTT effect refers to the phase transformations that occur at lower temperatures and lead to the formation of a martensitic microstructure, which will significantly influence the residual stresses and distortion of the welded joints. In this research, the LTT conditions are achieved by varying the Cr and Ni content in the weld seam by varying the weld parameter, including laser power, welding speed and filler wire speed. The dilatometer analysis technique is employed to simulate the thermal conditions encountered during LBW. By subjecting the stainless steel samples to controlled heating and cooling cycles, the kinetics of the volume fractions can be measured using the lever rule and empirical method (KOP and Lee). The phase transformation simulation model is computed by integrating the thermal and metallurgical effects to predict the volume fractions in LBW joints and has been validated using dilatometer results. This provides valuable insight into the relationship between welding parameters and phase transformations in stainless steel with the LTT effect during laser beam welding. Using this relationship, the weld quality can be improved by reducing the residual stresses and distortion.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088005","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}
Santiago Flores-García, C. E. Martínez-Pérez, C. Rubio-González, J. A. Banderas-Hernández, C. Félix-Martínez, Salomón M. A. Jiménez
Laser cladding (LC) is a versatile additive manufacturing process where strands of metallic material are deposited and melted by a laser. However, there are some limitations associated with this process that may affect the performance of the final manufactured parts. In the present work, the influence of laser shock peening (LSP) on the fatigue life of 304 stainless steel flat specimens with a cobalt-based alloy (Stellite 6) coating applied by LC was investigated. The analysis was carried out both experimentally and numerically. In the LSP simulation, the ABAQUS/Explicit code was used to determine the residual stress distribution of specimens with double central notches with a radius of curvature of 5, 10, 15, and 20 mm. From the numerical results, an improvement was found regarding fatigue life up to 48% in samples with LSP. Experimentally, 14% in fatigue life enhancement was observed. The residual stress, determined by the contour method, showed good agreement with the LSP simulation. The SEM images revealed that the fatigue failure started at the Stellite 6 coating and propagated towards the center of the specimen. LSP has been shown to be a suitable postprocessing alternative for laser-cladded parts that will be subjected to fatigue loading since it led to fatigue improvement through the introduction of compressive residual stresses on clad coatings.
{"title":"Fatigue Life and Residual Stress of Flat Stainless Steel Specimens Laser-Cladded with a Cobalt-Based Alloy and Postprocessed with Laser Shock Peening","authors":"Santiago Flores-García, C. E. Martínez-Pérez, C. Rubio-González, J. A. Banderas-Hernández, C. Félix-Martínez, Salomón M. A. Jiménez","doi":"10.3390/jmmp8020045","DOIUrl":"https://doi.org/10.3390/jmmp8020045","url":null,"abstract":"Laser cladding (LC) is a versatile additive manufacturing process where strands of metallic material are deposited and melted by a laser. However, there are some limitations associated with this process that may affect the performance of the final manufactured parts. In the present work, the influence of laser shock peening (LSP) on the fatigue life of 304 stainless steel flat specimens with a cobalt-based alloy (Stellite 6) coating applied by LC was investigated. The analysis was carried out both experimentally and numerically. In the LSP simulation, the ABAQUS/Explicit code was used to determine the residual stress distribution of specimens with double central notches with a radius of curvature of 5, 10, 15, and 20 mm. From the numerical results, an improvement was found regarding fatigue life up to 48% in samples with LSP. Experimentally, 14% in fatigue life enhancement was observed. The residual stress, determined by the contour method, showed good agreement with the LSP simulation. The SEM images revealed that the fatigue failure started at the Stellite 6 coating and propagated towards the center of the specimen. LSP has been shown to be a suitable postprocessing alternative for laser-cladded parts that will be subjected to fatigue loading since it led to fatigue improvement through the introduction of compressive residual stresses on clad coatings.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140423786","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}
Accurately predicting the time-varying dynamic parameters of a workpiece during the milling of thin-walled parts is the foundation of adaptively selecting chatter-free machining parameters. Hence, a method for accurately and quickly predicting the time-varying dynamic parameters for milling thin-walled parts is proposed, which is based on the shell FEM and a three-layer neural network. The time-dependent dynamics of the workpiece can be calculated using the FEM by obtaining the geometrical parameters of the arc-faced junctions within the discrete cells of the initial and machined workpiece. It is unnecessary to re-divide the mesh cells of the thin-walled parts at each cutting position, which enhances the computational efficiency of the workpiece dynamics. Meanwhile, in comparison with the three-dimensional cube elements, the shell elements can reduce the number of degrees of freedom of the FEM model by 74%, which leads to the computation of the characteristic equation that is about nine times faster. The results of the modal test show that the maximum error of the shell FEM in predicting the natural frequency of the workpiece is about 4%. Furthermore, a three-layer neural network is constructed, and the results of the shell FEM are used as samples to train the model. The neural network model has a maximum prediction error of 0.409% when benchmarked against the results of the FEM. Furthermore, the three-layer neural network effectively enhances computational efficiency while guaranteeing accuracy.
{"title":"Predicting the Dynamic Parameters for Milling Thin-Walled Blades with a Neural Network","authors":"Yu Li, Feng Ding, Dazhen Wang, Weijun Tian, Jinhua Zhou","doi":"10.3390/jmmp8020043","DOIUrl":"https://doi.org/10.3390/jmmp8020043","url":null,"abstract":"Accurately predicting the time-varying dynamic parameters of a workpiece during the milling of thin-walled parts is the foundation of adaptively selecting chatter-free machining parameters. Hence, a method for accurately and quickly predicting the time-varying dynamic parameters for milling thin-walled parts is proposed, which is based on the shell FEM and a three-layer neural network. The time-dependent dynamics of the workpiece can be calculated using the FEM by obtaining the geometrical parameters of the arc-faced junctions within the discrete cells of the initial and machined workpiece. It is unnecessary to re-divide the mesh cells of the thin-walled parts at each cutting position, which enhances the computational efficiency of the workpiece dynamics. Meanwhile, in comparison with the three-dimensional cube elements, the shell elements can reduce the number of degrees of freedom of the FEM model by 74%, which leads to the computation of the characteristic equation that is about nine times faster. The results of the modal test show that the maximum error of the shell FEM in predicting the natural frequency of the workpiece is about 4%. Furthermore, a three-layer neural network is constructed, and the results of the shell FEM are used as samples to train the model. The neural network model has a maximum prediction error of 0.409% when benchmarked against the results of the FEM. Furthermore, the three-layer neural network effectively enhances computational efficiency while guaranteeing accuracy.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445069","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}
Nickolay S Lubimyi, M. Chepchurov, A. Polshin, Michael D. Gerasimov, Boris S. Chetverikov, Anastasia Chetverikova, A. Tikhonov, Ardalion Maltsev
This article describes the technology for manufacturing a metal composite structure of a metal-cutting tool body. The main problem with using metal 3D-printing is its prohibitively high cost. The initial data for carrying out finite element calculations are presented, in particular, the calculation and justification of the selected loads on the drill body arising from metal-cutting forces. The described methodology for designing a digital model of a metal-cutting tool for the purpose of its further production using SLM 3D metal printing methods facilitates the procurement of a digital model characterized by a reduced weight and volume of material. The described design technology involves the production of a thin-walled outer shell that forms the external technological surfaces necessary for the drill body, as well as internal structural elements formed as a result of topological optimization of the product shape. Much attention in this article is paid to the description of the technology for filling internal cavities with a viscous metal polymer, formed as a result of the topological optimization of the original model. Due to this design approach, it is possible to reduce the volume of 3D metal printing by 32%, which amounts to more than USD 135 in value terms.
本文介绍了金属切削工具本体的金属复合结构制造技术。使用金属三维打印技术的主要问题是成本过高。文章介绍了进行有限元计算的初始数据，特别是金属切削力对钻体产生的选定载荷的计算和论证。所述方法用于设计金属切削工具的数字模型，以便使用 SLM 3D 金属打印方法进一步生产该工具。所述设计技术包括生产薄壁外壳，形成钻头主体所需的外部技术表面，以及产品形状拓扑优化后形成的内部结构元件。本文将重点介绍用粘性金属聚合物填充内部空腔的技术，该技术是对原始模型进行拓扑优化的结果。由于采用了这种设计方法，三维金属打印的体积可以减少 32%，按价值计算超过 135 美元。
{"title":"Reducing the Cost of 3D Metal Printing Using Selective Laser Melting (SLM) Technology in the Manufacture of a Drill Body by Reinforcing Thin-Walled Shell Forms with Metal-Polymers","authors":"Nickolay S Lubimyi, M. Chepchurov, A. Polshin, Michael D. Gerasimov, Boris S. Chetverikov, Anastasia Chetverikova, A. Tikhonov, Ardalion Maltsev","doi":"10.3390/jmmp8020044","DOIUrl":"https://doi.org/10.3390/jmmp8020044","url":null,"abstract":"This article describes the technology for manufacturing a metal composite structure of a metal-cutting tool body. The main problem with using metal 3D-printing is its prohibitively high cost. The initial data for carrying out finite element calculations are presented, in particular, the calculation and justification of the selected loads on the drill body arising from metal-cutting forces. The described methodology for designing a digital model of a metal-cutting tool for the purpose of its further production using SLM 3D metal printing methods facilitates the procurement of a digital model characterized by a reduced weight and volume of material. The described design technology involves the production of a thin-walled outer shell that forms the external technological surfaces necessary for the drill body, as well as internal structural elements formed as a result of topological optimization of the product shape. Much attention in this article is paid to the description of the technology for filling internal cavities with a viscous metal polymer, formed as a result of the topological optimization of the original model. Due to this design approach, it is possible to reduce the volume of 3D metal printing by 32%, which amounts to more than USD 135 in value terms.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140444764","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}
Devashish Dubey, Anooshe Sadat Mirhakimi, M. Elbestawi
Most materials conventionally found in nature expand with an increase in temperature. In actual systems and assemblies like precision instruments, this can cause thermal distortions which can be difficult to handle. Materials with a tendency to shrink with an increase in temperature can be used alongside conventional materials to restrict the overall dimensional change of structures. Such structures, also called negative-thermal-expansion materials, could be crucial in applications like electronics, biomedicine, aerospace components, etc., which undergo high changes in temperature. This can be achieved using mechanically engineered materials, also called negative thermal expansion (NTE) mechanical metamaterials. Mechanical metamaterials are mechanically architected materials with novel properties that are rare in naturally occurring materials. NTE metamaterials utilize their artificially engineered architecture to attain the rare property of negative thermal expansion. The emergence of additive manufacturing has enabled the feasible production of their intricate architectures. Industrial processes such as laser powder bed fusion and direct energy deposition, both utilized in metal additive manufacturing, have proven successful in creating complex structures like lattice formations and multimaterial components in the industrial sector, rendering them suitable for manufacturing NTE structures. Nevertheless, this review examines a range of fabrication methods, encompassing both additive and traditional techniques, and explores the diverse materials used in the process. Despite NTE metamaterials being a prominent field of research, a comprehensive review of these architected materials is missing in the literature. This article aims to bridge this gap by providing a state-of-the-art review of these metamaterials, encompassing their design, fabrication, and cutting-edge applications.
{"title":"Negative Thermal Expansion Metamaterials: A Review of Design, Fabrication, and Applications","authors":"Devashish Dubey, Anooshe Sadat Mirhakimi, M. Elbestawi","doi":"10.3390/jmmp8010040","DOIUrl":"https://doi.org/10.3390/jmmp8010040","url":null,"abstract":"Most materials conventionally found in nature expand with an increase in temperature. In actual systems and assemblies like precision instruments, this can cause thermal distortions which can be difficult to handle. Materials with a tendency to shrink with an increase in temperature can be used alongside conventional materials to restrict the overall dimensional change of structures. Such structures, also called negative-thermal-expansion materials, could be crucial in applications like electronics, biomedicine, aerospace components, etc., which undergo high changes in temperature. This can be achieved using mechanically engineered materials, also called negative thermal expansion (NTE) mechanical metamaterials. Mechanical metamaterials are mechanically architected materials with novel properties that are rare in naturally occurring materials. NTE metamaterials utilize their artificially engineered architecture to attain the rare property of negative thermal expansion. The emergence of additive manufacturing has enabled the feasible production of their intricate architectures. Industrial processes such as laser powder bed fusion and direct energy deposition, both utilized in metal additive manufacturing, have proven successful in creating complex structures like lattice formations and multimaterial components in the industrial sector, rendering them suitable for manufacturing NTE structures. Nevertheless, this review examines a range of fabrication methods, encompassing both additive and traditional techniques, and explores the diverse materials used in the process. Despite NTE metamaterials being a prominent field of research, a comprehensive review of these architected materials is missing in the literature. This article aims to bridge this gap by providing a state-of-the-art review of these metamaterials, encompassing their design, fabrication, and cutting-edge applications.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837011","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}
M. Hadad, Samareh Attarsharghi, Mohsen Dehghanpour Abyaneh, Parviz Narimani, Javad Makarian, Alireza Saberi, Amir Alinaghizadeh
Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior in relation to various parameters. Nonetheless, this study introduces a novel set of parameters that have previously been unexplored, contributing to the advancement of surface roughness prediction for the grinding of Inconel 738 superalloy considering the effects of dressing and grinding parameters. Hence, the current study encompasses the utilization of a deep artificial neural network to forecast roughness. This implementation leverages an extensive dataset generated in a recent experimental study by the authors. The dataset comprises a multitude of process parameters across diverse conditions, including dressing techniques such as four-edge and single-edge diamond dresser, alongside cooling approaches like minimum quantity lubrication and conventional wet techniques. To evaluate a robust algorithm, a method is devised that involves different networks utilizing various activation functions and neuron sizes to distinguish and select the best architecture for this study. To gauge the accuracy of the methods, mean squared error and absolute accuracy metrics are applied, yielding predictions that fall within acceptable ranges for real-world industrial roughness standards. The model developed in this work has the potential to be integrated with the Industrial Internet of Things to further enhance automated machining.
{"title":"Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining","authors":"M. Hadad, Samareh Attarsharghi, Mohsen Dehghanpour Abyaneh, Parviz Narimani, Javad Makarian, Alireza Saberi, Amir Alinaghizadeh","doi":"10.3390/jmmp8010041","DOIUrl":"https://doi.org/10.3390/jmmp8010041","url":null,"abstract":"Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior in relation to various parameters. Nonetheless, this study introduces a novel set of parameters that have previously been unexplored, contributing to the advancement of surface roughness prediction for the grinding of Inconel 738 superalloy considering the effects of dressing and grinding parameters. Hence, the current study encompasses the utilization of a deep artificial neural network to forecast roughness. This implementation leverages an extensive dataset generated in a recent experimental study by the authors. The dataset comprises a multitude of process parameters across diverse conditions, including dressing techniques such as four-edge and single-edge diamond dresser, alongside cooling approaches like minimum quantity lubrication and conventional wet techniques. To evaluate a robust algorithm, a method is devised that involves different networks utilizing various activation functions and neuron sizes to distinguish and select the best architecture for this study. To gauge the accuracy of the methods, mean squared error and absolute accuracy metrics are applied, yielding predictions that fall within acceptable ranges for real-world industrial roughness standards. The model developed in this work has the potential to be integrated with the Industrial Internet of Things to further enhance automated machining.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839252","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}
M. Hadad, Samareh Attarsharghi, Mohsen Dehghanpour Abyaneh, Parviz Narimani, Javad Makarian, Alireza Saberi, Amir Alinaghizadeh
Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior in relation to various parameters. Nonetheless, this study introduces a novel set of parameters that have previously been unexplored, contributing to the advancement of surface roughness prediction for the grinding of Inconel 738 superalloy considering the effects of dressing and grinding parameters. Hence, the current study encompasses the utilization of a deep artificial neural network to forecast roughness. This implementation leverages an extensive dataset generated in a recent experimental study by the authors. The dataset comprises a multitude of process parameters across diverse conditions, including dressing techniques such as four-edge and single-edge diamond dresser, alongside cooling approaches like minimum quantity lubrication and conventional wet techniques. To evaluate a robust algorithm, a method is devised that involves different networks utilizing various activation functions and neuron sizes to distinguish and select the best architecture for this study. To gauge the accuracy of the methods, mean squared error and absolute accuracy metrics are applied, yielding predictions that fall within acceptable ranges for real-world industrial roughness standards. The model developed in this work has the potential to be integrated with the Industrial Internet of Things to further enhance automated machining.
{"title":"Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining","authors":"M. Hadad, Samareh Attarsharghi, Mohsen Dehghanpour Abyaneh, Parviz Narimani, Javad Makarian, Alireza Saberi, Amir Alinaghizadeh","doi":"10.3390/jmmp8010041","DOIUrl":"https://doi.org/10.3390/jmmp8010041","url":null,"abstract":"Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior in relation to various parameters. Nonetheless, this study introduces a novel set of parameters that have previously been unexplored, contributing to the advancement of surface roughness prediction for the grinding of Inconel 738 superalloy considering the effects of dressing and grinding parameters. Hence, the current study encompasses the utilization of a deep artificial neural network to forecast roughness. This implementation leverages an extensive dataset generated in a recent experimental study by the authors. The dataset comprises a multitude of process parameters across diverse conditions, including dressing techniques such as four-edge and single-edge diamond dresser, alongside cooling approaches like minimum quantity lubrication and conventional wet techniques. To evaluate a robust algorithm, a method is devised that involves different networks utilizing various activation functions and neuron sizes to distinguish and select the best architecture for this study. To gauge the accuracy of the methods, mean squared error and absolute accuracy metrics are applied, yielding predictions that fall within acceptable ranges for real-world industrial roughness standards. The model developed in this work has the potential to be integrated with the Industrial Internet of Things to further enhance automated machining.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779338","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}
Devashish Dubey, Anooshe Sadat Mirhakimi, M. Elbestawi
Most materials conventionally found in nature expand with an increase in temperature. In actual systems and assemblies like precision instruments, this can cause thermal distortions which can be difficult to handle. Materials with a tendency to shrink with an increase in temperature can be used alongside conventional materials to restrict the overall dimensional change of structures. Such structures, also called negative-thermal-expansion materials, could be crucial in applications like electronics, biomedicine, aerospace components, etc., which undergo high changes in temperature. This can be achieved using mechanically engineered materials, also called negative thermal expansion (NTE) mechanical metamaterials. Mechanical metamaterials are mechanically architected materials with novel properties that are rare in naturally occurring materials. NTE metamaterials utilize their artificially engineered architecture to attain the rare property of negative thermal expansion. The emergence of additive manufacturing has enabled the feasible production of their intricate architectures. Industrial processes such as laser powder bed fusion and direct energy deposition, both utilized in metal additive manufacturing, have proven successful in creating complex structures like lattice formations and multimaterial components in the industrial sector, rendering them suitable for manufacturing NTE structures. Nevertheless, this review examines a range of fabrication methods, encompassing both additive and traditional techniques, and explores the diverse materials used in the process. Despite NTE metamaterials being a prominent field of research, a comprehensive review of these architected materials is missing in the literature. This article aims to bridge this gap by providing a state-of-the-art review of these metamaterials, encompassing their design, fabrication, and cutting-edge applications.
{"title":"Negative Thermal Expansion Metamaterials: A Review of Design, Fabrication, and Applications","authors":"Devashish Dubey, Anooshe Sadat Mirhakimi, M. Elbestawi","doi":"10.3390/jmmp8010040","DOIUrl":"https://doi.org/10.3390/jmmp8010040","url":null,"abstract":"Most materials conventionally found in nature expand with an increase in temperature. In actual systems and assemblies like precision instruments, this can cause thermal distortions which can be difficult to handle. Materials with a tendency to shrink with an increase in temperature can be used alongside conventional materials to restrict the overall dimensional change of structures. Such structures, also called negative-thermal-expansion materials, could be crucial in applications like electronics, biomedicine, aerospace components, etc., which undergo high changes in temperature. This can be achieved using mechanically engineered materials, also called negative thermal expansion (NTE) mechanical metamaterials. Mechanical metamaterials are mechanically architected materials with novel properties that are rare in naturally occurring materials. NTE metamaterials utilize their artificially engineered architecture to attain the rare property of negative thermal expansion. The emergence of additive manufacturing has enabled the feasible production of their intricate architectures. Industrial processes such as laser powder bed fusion and direct energy deposition, both utilized in metal additive manufacturing, have proven successful in creating complex structures like lattice formations and multimaterial components in the industrial sector, rendering them suitable for manufacturing NTE structures. Nevertheless, this review examines a range of fabrication methods, encompassing both additive and traditional techniques, and explores the diverse materials used in the process. Despite NTE metamaterials being a prominent field of research, a comprehensive review of these architected materials is missing in the literature. This article aims to bridge this gap by providing a state-of-the-art review of these metamaterials, encompassing their design, fabrication, and cutting-edge applications.","PeriodicalId":16319,"journal":{"name":"Journal of Manufacturing and Materials Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777197","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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