The conventional stamping manufacturing process has certain limitations that need to be considered throughout the product design process, including the thickness of the blank, geometry of the product, and the drawing force. If the limitations are not considered during the design and manufacturing, they become defects such as wrinkles, excessive thinning, rupture, and spring back. The outcome of the defects is an increase in costs, rework, pre-processing of material (Heat Treatment), and the most important factor, time. To overcome defects, standard alternatives are changing the material composition, blank thickness, or the product design. This research aims to reduce the defects by keeping the design and the material the same as considered during the design phase. Electrically assisted manufacturing is used in the stamping process to eliminate defects. Electrically Assisted Manufacturing has been proven successful in increasing the workability of the workpiece. In this method, controlled electricity passes through the workpiece, blank holder, or the dies during the manufacturing process, which heat the blank. 5052-H32 Aluminium with a thickness of 0.5 mm was used for this study. Previous research indicates that this EAM technique can be used in forging, which is called Electrically Assisted Forging, to improve the formability of the workpiece. This research provides insights into the implementation of Electrically Assisted Forging in the stamping process. In the Electrically Assisted Stamping process, the heat produced due to electricity will temporarily change the material properties and increase its elasticity. Once the temporary elastic limit is achieved, the stamping process will begin. The current flow in pulses will continue until the stamping is completed. The method proposed in this paper considered three important parameters; the amplitude of the current, current holding time, and feed rate of the stamping machine. These parameters were used with different combinations during the testing. Using the data generated of drawing force from the Instron machine was used to plot different types of comparison graphs, which ultimately resulted in direct relation between current and drawing force.
{"title":"Electrically Assisted Stamping","authors":"Shubham Garde, Ranveer Patil, T. Grimm, L. Mears","doi":"10.1115/imece2022-96916","DOIUrl":"https://doi.org/10.1115/imece2022-96916","url":null,"abstract":"\u0000 The conventional stamping manufacturing process has certain limitations that need to be considered throughout the product design process, including the thickness of the blank, geometry of the product, and the drawing force. If the limitations are not considered during the design and manufacturing, they become defects such as wrinkles, excessive thinning, rupture, and spring back. The outcome of the defects is an increase in costs, rework, pre-processing of material (Heat Treatment), and the most important factor, time. To overcome defects, standard alternatives are changing the material composition, blank thickness, or the product design. This research aims to reduce the defects by keeping the design and the material the same as considered during the design phase. Electrically assisted manufacturing is used in the stamping process to eliminate defects. Electrically Assisted Manufacturing has been proven successful in increasing the workability of the workpiece. In this method, controlled electricity passes through the workpiece, blank holder, or the dies during the manufacturing process, which heat the blank. 5052-H32 Aluminium with a thickness of 0.5 mm was used for this study. Previous research indicates that this EAM technique can be used in forging, which is called Electrically Assisted Forging, to improve the formability of the workpiece. This research provides insights into the implementation of Electrically Assisted Forging in the stamping process. In the Electrically Assisted Stamping process, the heat produced due to electricity will temporarily change the material properties and increase its elasticity. Once the temporary elastic limit is achieved, the stamping process will begin. The current flow in pulses will continue until the stamping is completed. The method proposed in this paper considered three important parameters; the amplitude of the current, current holding time, and feed rate of the stamping machine. These parameters were used with different combinations during the testing. Using the data generated of drawing force from the Instron machine was used to plot different types of comparison graphs, which ultimately resulted in direct relation between current and drawing force.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129268119","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 requirement of finishing tungsten carbide at the nano level has drastically increased due to recent development in the field of punch and dies manufacturing industry. The surface roughness has a considerable impact on the quality of the created product. The current study’s major purpose is to investigate how well tungsten carbide can be completed using the solid rotating core magnetorheological finishing (MRF) method. Response surface approach is used to screen studies in order to find the main parameters impacting tungsten carbide surface roughness. The concentration of diamond abrasives, the current induced in the electromagnetic coil, the gap maintained between the workpiece surface, the solid rotating tool core tip surface, and the tool’s rotational speed are the process parameters used in this work. The process parameters in the magnetorheological finishing of tungsten carbide have a significant influence in lowering the considerable value of surface roughness. The minimal surface roughness value found on the tungsten carbide workpiece after 45 min of finishing by the solid rotating core magnetorheological finishing method was as low as 54 nm, down from an initial value of 248 nm. To analyze the finished surface characteristics of the tungsten carbide, the study of surface morphology test is performed. After performing the present MRF, the surface characteristics of the tungsten carbide show a substantial improvement. Thus, the fine finishing with the improved smooth surface quality of the tungsten carbide workpiece may improve its performance in the mold and dies manufacturing industry.
{"title":"Magnetorheological Fine Finishing of Tungsten Carbide Mold Material","authors":"A. Thomas, Anant Kumar Singh, K. Arora","doi":"10.1115/imece2022-96885","DOIUrl":"https://doi.org/10.1115/imece2022-96885","url":null,"abstract":"\u0000 The requirement of finishing tungsten carbide at the nano level has drastically increased due to recent development in the field of punch and dies manufacturing industry. The surface roughness has a considerable impact on the quality of the created product. The current study’s major purpose is to investigate how well tungsten carbide can be completed using the solid rotating core magnetorheological finishing (MRF) method. Response surface approach is used to screen studies in order to find the main parameters impacting tungsten carbide surface roughness. The concentration of diamond abrasives, the current induced in the electromagnetic coil, the gap maintained between the workpiece surface, the solid rotating tool core tip surface, and the tool’s rotational speed are the process parameters used in this work. The process parameters in the magnetorheological finishing of tungsten carbide have a significant influence in lowering the considerable value of surface roughness. The minimal surface roughness value found on the tungsten carbide workpiece after 45 min of finishing by the solid rotating core magnetorheological finishing method was as low as 54 nm, down from an initial value of 248 nm. To analyze the finished surface characteristics of the tungsten carbide, the study of surface morphology test is performed. After performing the present MRF, the surface characteristics of the tungsten carbide show a substantial improvement. Thus, the fine finishing with the improved smooth surface quality of the tungsten carbide workpiece may improve its performance in the mold and dies manufacturing industry.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127772690","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}
Ihab Ragai, J. Goldstein, Cayla Meyer, Clayton Upcraft
Single point incremental forming (SPIF) is a relatively new process for forming sheet products. Typically, the sheet is clamped into a fixture and is incrementally formed by moving a hemispherical end-mill tool using a multi-axis CNC milling machine. The tool deforms the material in each pass until the desired geometry is achieved. Friction between the tool and the formed sheet can have detrimental effects on the final geometry. The increase in friction coefficient can result in a significant decrease in sheet formability due to excessive thinning and subsequent fracture. Additionally, tool rotational speed may also contribute to the surface roughness of the formed part. The interaction between the tool and workpiece materials over relatively small areas of surface asperity is typically where friction and subsequent damage take place. The purpose of this research is to study the effect tool rotational speed as well as tool-workpiece material interaction on the surface condition and formability of formed components. Three polymeric materials have been considered herein, namely polypropylene (PP), polycarbonate (PC), and polyvinyl chloride (PVC). Spindle speeds varied from 600 to 1800 rpm. Four tool materials were also investigated, namely stainless steel, copper 110, beryllium-copper, and thermoplastic syntactic foam. Full factorial design of experiments took place. The parts were allowed to form until fracture takes place. Subsequently, the height of the cone was measured and used as representation of formability. Additionally, surface roughness and asperity height distribution were analyzed using both profilometry and microscopy. The aim is to explore possible correlations between process parameters and surface condition and their effect on single point incrementally formed shapes.
{"title":"Effect of Tool Material and Process Parameters on Surface Conditions in Single Point Incremental Forming (SPIF) of Polymeric Materials","authors":"Ihab Ragai, J. Goldstein, Cayla Meyer, Clayton Upcraft","doi":"10.1115/imece2022-95951","DOIUrl":"https://doi.org/10.1115/imece2022-95951","url":null,"abstract":"\u0000 Single point incremental forming (SPIF) is a relatively new process for forming sheet products. Typically, the sheet is clamped into a fixture and is incrementally formed by moving a hemispherical end-mill tool using a multi-axis CNC milling machine. The tool deforms the material in each pass until the desired geometry is achieved.\u0000 Friction between the tool and the formed sheet can have detrimental effects on the final geometry. The increase in friction coefficient can result in a significant decrease in sheet formability due to excessive thinning and subsequent fracture. Additionally, tool rotational speed may also contribute to the surface roughness of the formed part. The interaction between the tool and workpiece materials over relatively small areas of surface asperity is typically where friction and subsequent damage take place.\u0000 The purpose of this research is to study the effect tool rotational speed as well as tool-workpiece material interaction on the surface condition and formability of formed components. Three polymeric materials have been considered herein, namely polypropylene (PP), polycarbonate (PC), and polyvinyl chloride (PVC). Spindle speeds varied from 600 to 1800 rpm. Four tool materials were also investigated, namely stainless steel, copper 110, beryllium-copper, and thermoplastic syntactic foam. Full factorial design of experiments took place. The parts were allowed to form until fracture takes place. Subsequently, the height of the cone was measured and used as representation of formability. Additionally, surface roughness and asperity height distribution were analyzed using both profilometry and microscopy. The aim is to explore possible correlations between process parameters and surface condition and their effect on single point incrementally formed shapes.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121612299","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 two-dimensional elastic analytical model for bonded single lap joints subjected to heat and moisture diffusion is presented. The distributions of peel and shear stress in the bond area are calculated. Local moisture concentration and bondline temperature determine the properties of the adhesive layer, which vary in the space and time coordinates. Adhesive diffusivity coefficient and absolute saturated concentration are affected by the material temperature, and scenarios of individual and combined heat and moisture diffusion are analyzed. The governing partial differential equations are solved numerically, and a simplified shear stress formulation is introduced for low-modulus adhesives. Two-dimensional gradients in the adhesive properties affect the peel and shear stress in the bondline. Diffusive patterns in the direction of the loading axis of the joint can contribute to a positive stress redistribution along the overlap, while the results of this study show that softening patterns in the transverse direction may severely impact the joint performance. In the initial stages of environmental exposure, significant increases in peak shear stress are observed in the innermost portions of the bond area. Less significant gradients are observed for the peel stress distribution, under the same conditions. A 3-D Finite Elements Analysis is used to compute adhesive peel and shear stresses, and the results are in reasonable agreement with the proposed analytical model.
{"title":"2-D Analytical Model of Heat and Moisture Diffusion in Bonded Single Lap Joints","authors":"Marco Gerini-Romagnoli, S. Nassar","doi":"10.1115/imece2022-95201","DOIUrl":"https://doi.org/10.1115/imece2022-95201","url":null,"abstract":"\u0000 A two-dimensional elastic analytical model for bonded single lap joints subjected to heat and moisture diffusion is presented. The distributions of peel and shear stress in the bond area are calculated. Local moisture concentration and bondline temperature determine the properties of the adhesive layer, which vary in the space and time coordinates. Adhesive diffusivity coefficient and absolute saturated concentration are affected by the material temperature, and scenarios of individual and combined heat and moisture diffusion are analyzed. The governing partial differential equations are solved numerically, and a simplified shear stress formulation is introduced for low-modulus adhesives.\u0000 Two-dimensional gradients in the adhesive properties affect the peel and shear stress in the bondline. Diffusive patterns in the direction of the loading axis of the joint can contribute to a positive stress redistribution along the overlap, while the results of this study show that softening patterns in the transverse direction may severely impact the joint performance. In the initial stages of environmental exposure, significant increases in peak shear stress are observed in the innermost portions of the bond area. Less significant gradients are observed for the peel stress distribution, under the same conditions. A 3-D Finite Elements Analysis is used to compute adhesive peel and shear stresses, and the results are in reasonable agreement with the proposed analytical model.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127109128","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}
O. Lapuz, Hayk Vasilyan, Saleh Atatreh, Mozah Alyammahi, Ahmad Abdulla Al Mheiri, R. Susantyoko
3D printing technology is often the go-to solution for rapid-prototyping thermoplastics. Parts can be created in record time with regards to the initial investment compared to the resources required in traditional fabrication methods such as injection molding. This has started a wave of manufacturers that are looking to scale their production with the use of 3D printing technology, as the parts have similar properties to their injection-molded counterparts. As the advancement of 3D printing continues, to our knowledge, no studies have been conducted regarding the performance change of the part versus the use in inert-gas and vacuum antechamber environments. This study aims to demonstrate the effects of room, antechamber, dryer, and inert-gas environments with respect to the mechanical properties of 3D printed fused filament fabrication thermoplastics over time. From the variation of the results that have been noticed on samples that were printed, the parts should not be utilized immediately, but rather they must be stored in a stable environment until the material properties are fully optimized. This would enable the designer to consider a risk factor to be applied that would account for expected changes in mechanical properties according to the environmental conditions for the intended application.
{"title":"Effect of Ageing and Environmental Conditions on Mechanical Properties of 3D Printed Parts","authors":"O. Lapuz, Hayk Vasilyan, Saleh Atatreh, Mozah Alyammahi, Ahmad Abdulla Al Mheiri, R. Susantyoko","doi":"10.1115/imece2022-95588","DOIUrl":"https://doi.org/10.1115/imece2022-95588","url":null,"abstract":"\u0000 3D printing technology is often the go-to solution for rapid-prototyping thermoplastics. Parts can be created in record time with regards to the initial investment compared to the resources required in traditional fabrication methods such as injection molding. This has started a wave of manufacturers that are looking to scale their production with the use of 3D printing technology, as the parts have similar properties to their injection-molded counterparts.\u0000 As the advancement of 3D printing continues, to our knowledge, no studies have been conducted regarding the performance change of the part versus the use in inert-gas and vacuum antechamber environments. This study aims to demonstrate the effects of room, antechamber, dryer, and inert-gas environments with respect to the mechanical properties of 3D printed fused filament fabrication thermoplastics over time.\u0000 From the variation of the results that have been noticed on samples that were printed, the parts should not be utilized immediately, but rather they must be stored in a stable environment until the material properties are fully optimized. This would enable the designer to consider a risk factor to be applied that would account for expected changes in mechanical properties according to the environmental conditions for the intended application.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131889984","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 application of an electric current in situ with plastic deformation has been shown to produce non-thermal effects, which often present as a reduction in flow stress. This phenomenon is known as electroplasticity. Despite over 60 years of research, the mechanism(s) responsible for this behavior remains unknown. The magnetic vector potential is explored herein as a possible mechanism that may contribute towards electroplasticity. This effect is explored experimentally by isolating the potentials experienced during a common electrically-assisted tension/compression test through use of a solenoid. It was discovered that the vector potential does not affect the flow stress or elongation in several materials that were tested. This conclusions eliminates one possible mechanism of the electroplastic effect.
{"title":"The Influence of Magnetic Vector Potential in Electroplasticity","authors":"T. Grimm, L. Mears","doi":"10.1115/imece2022-93909","DOIUrl":"https://doi.org/10.1115/imece2022-93909","url":null,"abstract":"\u0000 The application of an electric current in situ with plastic deformation has been shown to produce non-thermal effects, which often present as a reduction in flow stress. This phenomenon is known as electroplasticity. Despite over 60 years of research, the mechanism(s) responsible for this behavior remains unknown.\u0000 The magnetic vector potential is explored herein as a possible mechanism that may contribute towards electroplasticity. This effect is explored experimentally by isolating the potentials experienced during a common electrically-assisted tension/compression test through use of a solenoid. It was discovered that the vector potential does not affect the flow stress or elongation in several materials that were tested. This conclusions eliminates one possible mechanism of the electroplastic effect.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131218402","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}
Joshua Blatt, Jacob Kirkendoll, Paavana Krishna Mandava, Zachary Preston, R. Joyce, Roozbeh Salary
The overarching goal of this research work is to fabricate biocompatible, porous bone scaffolds that are not only mechanically robust but also dimensionally accurate for the treatment of osseous fractures, defects, and musculoskeletal diseases. In pursuit of this goal, the objective of the work is to develop an image-based intelligent platform, based on convolutional neural network, for prediction of the functional properties (such as porosity, stiffness, and compressive strength) of composite bone scaffolds (composed of polyamide, polyolefin, and cellulose fibers) fabricated using fused deposition modeling (FDM) process. FDM is a material extrusion additive manufacturing process, which has been extensively utilized for the fabrication of a wide range of biological tissues and constructs for tissue engineering applications. As a high-resolution method, FDM allows for deposition of composite materials with complex formulations as well as complex porous microstructures. Despite the advantages and engendered applications, the FDM process is inherently complex; the complexity of the process is, to a great extent, the result of complex physical phenomena (such as non-Newtonian material deposition, layer fusion, and phase change) in addition to unavoidable material-process interactions (e.g., molten polymer flow deposition and subsequent layer fusion vs. translation speed). Besides, there is a wide spectrum of scaffold design, composite material, and fabrication process parameters (such as molten polymer viscosity, scaffold morphology, nozzle diameter, deposition temperature, and forced convection rate influencing solidification rate) contributing to the complexity of the FDM process. As a result, investigation of the impact of consequential design, material, and process parameters as well as their interactions would be required for optimal fabrication of mechanically strong, dimensionally accurate, and porous composite bone scaffolds. In this study, an image-based convolutional neural network (CNN) platform is presented with the aim to intelligently learn the complex dynamics of composite material deposition and ultimately predict scaffold porosity. In this study, the CNN model is trained on the basis of monochromatic images acquired from FDM-fabricated bone scaffolds via a high-resolution charge-coupled device (CCD) camera. The bone scaffolds were fabricated based on a medical-grade composite material, deposited using a converging microcapillary nozzle having a diameter of 800 μm with a deposition temperature, translation speed, and layer height of 225 °C, 15 mm/s, and 400 μm, respectively. The CNN model is utilized for in-process prediction of the morphological properties of the fabricated bone scaffolds. Overall, the outcomes of this study pave the way for smart, patient-specific fabrication of robust and porous bone scaffolds with tunable medical and functional properties.
{"title":"An Image-Based Convolutional Neural Network Platform for the Prediction of the Porosity of Composite Bone Scaffolds, Fabricated Using Material Extrusion Additive Manufacturing","authors":"Joshua Blatt, Jacob Kirkendoll, Paavana Krishna Mandava, Zachary Preston, R. Joyce, Roozbeh Salary","doi":"10.1115/imece2022-95044","DOIUrl":"https://doi.org/10.1115/imece2022-95044","url":null,"abstract":"\u0000 The overarching goal of this research work is to fabricate biocompatible, porous bone scaffolds that are not only mechanically robust but also dimensionally accurate for the treatment of osseous fractures, defects, and musculoskeletal diseases. In pursuit of this goal, the objective of the work is to develop an image-based intelligent platform, based on convolutional neural network, for prediction of the functional properties (such as porosity, stiffness, and compressive strength) of composite bone scaffolds (composed of polyamide, polyolefin, and cellulose fibers) fabricated using fused deposition modeling (FDM) process. FDM is a material extrusion additive manufacturing process, which has been extensively utilized for the fabrication of a wide range of biological tissues and constructs for tissue engineering applications. As a high-resolution method, FDM allows for deposition of composite materials with complex formulations as well as complex porous microstructures. Despite the advantages and engendered applications, the FDM process is inherently complex; the complexity of the process is, to a great extent, the result of complex physical phenomena (such as non-Newtonian material deposition, layer fusion, and phase change) in addition to unavoidable material-process interactions (e.g., molten polymer flow deposition and subsequent layer fusion vs. translation speed). Besides, there is a wide spectrum of scaffold design, composite material, and fabrication process parameters (such as molten polymer viscosity, scaffold morphology, nozzle diameter, deposition temperature, and forced convection rate influencing solidification rate) contributing to the complexity of the FDM process. As a result, investigation of the impact of consequential design, material, and process parameters as well as their interactions would be required for optimal fabrication of mechanically strong, dimensionally accurate, and porous composite bone scaffolds. In this study, an image-based convolutional neural network (CNN) platform is presented with the aim to intelligently learn the complex dynamics of composite material deposition and ultimately predict scaffold porosity. In this study, the CNN model is trained on the basis of monochromatic images acquired from FDM-fabricated bone scaffolds via a high-resolution charge-coupled device (CCD) camera. The bone scaffolds were fabricated based on a medical-grade composite material, deposited using a converging microcapillary nozzle having a diameter of 800 μm with a deposition temperature, translation speed, and layer height of 225 °C, 15 mm/s, and 400 μm, respectively. The CNN model is utilized for in-process prediction of the morphological properties of the fabricated bone scaffolds. Overall, the outcomes of this study pave the way for smart, patient-specific fabrication of robust and porous bone scaffolds with tunable medical and functional properties.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131110819","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}
With the invention of advanced engineering materials, the shaping of such materials became a challenging issue. Two or more reinforcements are added to the metal matrix and form hybrid metal matrix composites (HMMCs) with preferable material properties, but shaping became quite challenging. Hybrid Surface grinding Electrical Discharge Diamond Face Surface Grinding (EDDFSG) is a suitable hybrid machining process capable of machining complicated HMMCs. With the help of EDDFSG, adverse effects of both traditional and non-traditional techniques are overcome while combining their benefits for a better machining results. A hybrid composite machined with a hybrid machining process requires an advanced technique for modeling and the performance prediction of complex machining characteristics. Material removal rate (MRR) and (Ra) rely on the process parameters, their influence must be extensively studied. Machine learning, a subset of Artificial Intelligence, allows machines to learn, develop, and execute tasks like human beings based on data rather than explicitly programmed. In the present work, an attempt has been made to develop a Machine Learning (ML), K Nearest Neighbor (KNN) based model, to predict MRR and Ra for machining EDDFSG of Al/Al2O3p/B4Cp and Al/SiCp/B4Cp HMMCs. The KNN algorithm is one of the efficient ML models for regression. Our training data set is normalized using the Min-max scalar to avoid a biased algorithm towards one process parameter. The model’s accuracy is validated by average standard error metrics on the test data set. The impacts of the process parameters like pulse-on time, gap current, wheel speed, pulse-off time, grit number, table speed over the response variables of the ML model is studied and analyzed in depth. The remarkable results are found pertaining to machining characteristics of the EDDFSG process over traditional modelling techniques.
{"title":"Intelligent Modelling and Machining Characteristics of Hybrid Machining for Hybrid Metal Matrix Composites","authors":"Janvita Reddy, R. Yadav","doi":"10.1115/imece2022-95543","DOIUrl":"https://doi.org/10.1115/imece2022-95543","url":null,"abstract":"\u0000 With the invention of advanced engineering materials, the shaping of such materials became a challenging issue. Two or more reinforcements are added to the metal matrix and form hybrid metal matrix composites (HMMCs) with preferable material properties, but shaping became quite challenging. Hybrid Surface grinding Electrical Discharge Diamond Face Surface Grinding (EDDFSG) is a suitable hybrid machining process capable of machining complicated HMMCs. With the help of EDDFSG, adverse effects of both traditional and non-traditional techniques are overcome while combining their benefits for a better machining results. A hybrid composite machined with a hybrid machining process requires an advanced technique for modeling and the performance prediction of complex machining characteristics. Material removal rate (MRR) and (Ra) rely on the process parameters, their influence must be extensively studied. Machine learning, a subset of Artificial Intelligence, allows machines to learn, develop, and execute tasks like human beings based on data rather than explicitly programmed. In the present work, an attempt has been made to develop a Machine Learning (ML), K Nearest Neighbor (KNN) based model, to predict MRR and Ra for machining EDDFSG of Al/Al2O3p/B4Cp and Al/SiCp/B4Cp HMMCs. The KNN algorithm is one of the efficient ML models for regression. Our training data set is normalized using the Min-max scalar to avoid a biased algorithm towards one process parameter. The model’s accuracy is validated by average standard error metrics on the test data set. The impacts of the process parameters like pulse-on time, gap current, wheel speed, pulse-off time, grit number, table speed over the response variables of the ML model is studied and analyzed in depth. The remarkable results are found pertaining to machining characteristics of the EDDFSG process over traditional modelling techniques.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125646636","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}
Tube-to-sheet joint expansion has been successfully used in HVAC industry for many years to avail better heat exchange between tube and fins (sheet). Because this system transports fluids under pressure, joining tube and sheet in heat exchangers is critical for all processing industries. The tube-sheet connection’s joining strength is critical because it directly affects plant safety. Tube-to-sheet joint strength is measured in terms of residual contact stress between the tube’s outer surface and the sheet’s hole surfaces. The joint integrity is affected by several design parameters, including the type of material and the initial radial clearance. The tube can be either deformed with or without an internal fluid pressure to create a joint. A commonly used process to deform the tube is hydroforming. However, hydroforming mostly uses the high pressure to deform the cross-section without dominantly use of axial length of the tube. In contrast, another category where the dominant use of axial length of the tube material is used to deform the section is termed as a hydroforging process. The use of a plastic deformation technique in hydroforging joining technology eliminates some of the limitations of existing joining technologies. The sheet deforms more strongly than the tube after the expansion tool is retracted. As a result, the tube and sheet come into direct contact. This method allows for the joining of dissimilar materials and is also environmentally friendly. Fastened joints, welded joints, and adhesive joints are all examples of methods of joining that are comparable to each other in terms of their advantages and disadvantages. In this paper a tube to sheet joint will be studied during the hydroforging process. While the tube is pressurized with low pressure, the axial force will be applied to buckle the tube outward and around sheet. In the second stage the buckling region was compressed to make a joint. Two setting will be studied: intermediate joint (named: mid joint) and end joint. For this work a two-dimensional (2D) axisymmetric finite element model will be developed. The axial compression to create a buckling/folding in the tube and later joining with the sheet were studied. The mechanics of the buckling/folding was analyzed during the axial compression. The stresses induced at the interface were studied and resulted.
{"title":"Investigation of Tube Sheet Joining Through Hydroforging Process","authors":"S. Memon, C. Nikhare","doi":"10.1115/imece2022-94999","DOIUrl":"https://doi.org/10.1115/imece2022-94999","url":null,"abstract":"\u0000 Tube-to-sheet joint expansion has been successfully used in HVAC industry for many years to avail better heat exchange between tube and fins (sheet). Because this system transports fluids under pressure, joining tube and sheet in heat exchangers is critical for all processing industries. The tube-sheet connection’s joining strength is critical because it directly affects plant safety. Tube-to-sheet joint strength is measured in terms of residual contact stress between the tube’s outer surface and the sheet’s hole surfaces. The joint integrity is affected by several design parameters, including the type of material and the initial radial clearance. The tube can be either deformed with or without an internal fluid pressure to create a joint. A commonly used process to deform the tube is hydroforming. However, hydroforming mostly uses the high pressure to deform the cross-section without dominantly use of axial length of the tube. In contrast, another category where the dominant use of axial length of the tube material is used to deform the section is termed as a hydroforging process. The use of a plastic deformation technique in hydroforging joining technology eliminates some of the limitations of existing joining technologies. The sheet deforms more strongly than the tube after the expansion tool is retracted. As a result, the tube and sheet come into direct contact.\u0000 This method allows for the joining of dissimilar materials and is also environmentally friendly. Fastened joints, welded joints, and adhesive joints are all examples of methods of joining that are comparable to each other in terms of their advantages and disadvantages. In this paper a tube to sheet joint will be studied during the hydroforging process. While the tube is pressurized with low pressure, the axial force will be applied to buckle the tube outward and around sheet. In the second stage the buckling region was compressed to make a joint. Two setting will be studied: intermediate joint (named: mid joint) and end joint. For this work a two-dimensional (2D) axisymmetric finite element model will be developed. The axial compression to create a buckling/folding in the tube and later joining with the sheet were studied. The mechanics of the buckling/folding was analyzed during the axial compression. The stresses induced at the interface were studied and resulted.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131569765","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 study, we model the interplay between the process parameters and the part attributes with artificial neural networks (ANN) to predict the effect of a set of process parameters on the part attributes in extrusion-based AM process. Five process parameters including build orientation, print speed, extrusion temperature, deposition direction, and layer thickness with three levels are used in this study to fabricate parts following an orthogonal array experimental design. Three attributes including dimensional accuracy, surface roughness, and tensile strength of the fabricated parts are measured and used to train, validate, and test the proposed multilayer artificial neural network models. Four different ANN models are proposed where three of them are for the three individual part attributes and the fourth model is for the combination of all three attributes. The results indicate that the individual part attribute ANN models outperform the model for the combination of three attributes in terms of the RMSE and correlation coefficient. Comparison among the individual part attributes with respect to the process parameters is performed to analyze which parameters have a greater effect on the individual part attributes. The trained ANN models can be utilized to predict and optimize the part attributes in extrusion-based AM processes.
{"title":"Modeling the Interplay Between Process Parameters and Part Attributes in Additive Manufacturing Process With Artificial Neural Network","authors":"Jayanta Deb, N. Ahsan, Sharmin Majumder","doi":"10.1115/imece2022-95120","DOIUrl":"https://doi.org/10.1115/imece2022-95120","url":null,"abstract":"\u0000 In this study, we model the interplay between the process parameters and the part attributes with artificial neural networks (ANN) to predict the effect of a set of process parameters on the part attributes in extrusion-based AM process. Five process parameters including build orientation, print speed, extrusion temperature, deposition direction, and layer thickness with three levels are used in this study to fabricate parts following an orthogonal array experimental design. Three attributes including dimensional accuracy, surface roughness, and tensile strength of the fabricated parts are measured and used to train, validate, and test the proposed multilayer artificial neural network models. Four different ANN models are proposed where three of them are for the three individual part attributes and the fourth model is for the combination of all three attributes. The results indicate that the individual part attribute ANN models outperform the model for the combination of three attributes in terms of the RMSE and correlation coefficient. Comparison among the individual part attributes with respect to the process parameters is performed to analyze which parameters have a greater effect on the individual part attributes. The trained ANN models can be utilized to predict and optimize the part attributes in extrusion-based AM processes.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130256030","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：