This effort represents the continuation of percussive riveting work that has been performed by this University of Washington research group and presented at this event in the past. Percussive riveting is an assembly method that is used ubiquitously in the aerospace industry, especially in the fuselage final assembly phase. It is a dynamic assembly process that requires two entities operating simultaneously in order to form a rivet. One entity operates with a riveting gun on the exterior of the fuselage. The rivet gun provides the energy input to carry out the assembly process. The other entity operates with a bucking bar on the interior of the fuselage. This entity is responsible for forming the rivet from the shank end. In previous work undertaken by this research group, an axisymmetric thermomechanical finite element model (FEM) framework implementing actual boundary conditions was developed to understand the effect of geometric factors on the residual stress and strain distributions within the riveting stackup. A countersunk rivet and two skins are part of the riveting stackup. The countersunk rivet is widely used in the aerospace industry because of flushness requirements. Because no further joint finishing processes are required after the countersunk rivet has been formed, it is an economically viable final assembly method. Residual stress and strain distributions within the rivet stackup affect the fatigue performance of the assembled joint. The percussive riveting process is different from the conventional squeeze riveting process because of the dynamic and adiabatic nature of the percussive riveting process. Strain rate effects and thermal effects are negligible in the squeeze riveting process because of the low velocities and strain rates involved. But these two effects along with large strain magnitude play an influential role in the percussive riveting process. Because of the dynamic nature of the percussive assembly process, framework validation is important. A three-dimensional FEM (3DFEM) was constructed with asymmetric motion of the bucking bar, also known as the forming tool. The results of the 3DFEM simulation are compared with previously presented axisymmetric FEM and are presented in this study. Analysis validation of the axisymmetric FEM was performed and analysis results were compared with FEM results.
{"title":"Numerical Study of the Percussive Riveting Process: Analysis Validation","authors":"S. Krovvidi, M. Ramulu, P. Reinhall","doi":"10.1115/imece2021-71800","DOIUrl":"https://doi.org/10.1115/imece2021-71800","url":null,"abstract":"\u0000 This effort represents the continuation of percussive riveting work that has been performed by this University of Washington research group and presented at this event in the past. Percussive riveting is an assembly method that is used ubiquitously in the aerospace industry, especially in the fuselage final assembly phase. It is a dynamic assembly process that requires two entities operating simultaneously in order to form a rivet. One entity operates with a riveting gun on the exterior of the fuselage. The rivet gun provides the energy input to carry out the assembly process. The other entity operates with a bucking bar on the interior of the fuselage. This entity is responsible for forming the rivet from the shank end. In previous work undertaken by this research group, an axisymmetric thermomechanical finite element model (FEM) framework implementing actual boundary conditions was developed to understand the effect of geometric factors on the residual stress and strain distributions within the riveting stackup. A countersunk rivet and two skins are part of the riveting stackup. The countersunk rivet is widely used in the aerospace industry because of flushness requirements. Because no further joint finishing processes are required after the countersunk rivet has been formed, it is an economically viable final assembly method. Residual stress and strain distributions within the rivet stackup affect the fatigue performance of the assembled joint. The percussive riveting process is different from the conventional squeeze riveting process because of the dynamic and adiabatic nature of the percussive riveting process. Strain rate effects and thermal effects are negligible in the squeeze riveting process because of the low velocities and strain rates involved. But these two effects along with large strain magnitude play an influential role in the percussive riveting process. Because of the dynamic nature of the percussive assembly process, framework validation is important. A three-dimensional FEM (3DFEM) was constructed with asymmetric motion of the bucking bar, also known as the forming tool. The results of the 3DFEM simulation are compared with previously presented axisymmetric FEM and are presented in this study. Analysis validation of the axisymmetric FEM was performed and analysis results were compared with FEM results.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134215922","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. M. Parvez, Musarrat Farzana Rahman, S. Galib, F. Liou
Additive manufacturing (AM), fundamentally different from traditional subtractive manufacturing techniques, is a layer-by-layer deposition process to fabricate parts with complex geometries. The formation of defects within AM components is a major concern for critical structural and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. While convolutional neural network (CNN) has already been demonstrated to be an effective deep learning tool for automated detection of defects for both conventional and AM processes, a network with optimized parameters including proper data processing and sampling can improve the performance of the architecture. In this study, for the detection of good deposition quality and defects such as lack of fusion, gas porosity, and cracks in a fusion-based AM process, a CNN architecture is presented comparing the classification report and evaluation of different architectural settings and obtaining the optimized result from them. The performance of the network was also compared with the results from the previous study. The overall accuracy (98%) for both training and testing the CNN network presented in this work transcends the current state of the art (92%) for AM defect detection.
{"title":"A Convolutional Neural Network (CNN) for Defect Detection of Additively Manufactured Parts","authors":"M. M. Parvez, Musarrat Farzana Rahman, S. Galib, F. Liou","doi":"10.1115/imece2021-70500","DOIUrl":"https://doi.org/10.1115/imece2021-70500","url":null,"abstract":"\u0000 Additive manufacturing (AM), fundamentally different from traditional subtractive manufacturing techniques, is a layer-by-layer deposition process to fabricate parts with complex geometries. The formation of defects within AM components is a major concern for critical structural and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. While convolutional neural network (CNN) has already been demonstrated to be an effective deep learning tool for automated detection of defects for both conventional and AM processes, a network with optimized parameters including proper data processing and sampling can improve the performance of the architecture. In this study, for the detection of good deposition quality and defects such as lack of fusion, gas porosity, and cracks in a fusion-based AM process, a CNN architecture is presented comparing the classification report and evaluation of different architectural settings and obtaining the optimized result from them. The performance of the network was also compared with the results from the previous study. The overall accuracy (98%) for both training and testing the CNN network presented in this work transcends the current state of the art (92%) for AM defect detection.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"630 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132868803","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}
Mohammad Borumand, Sima Esfandiarpour Borujeni, S. Nannapaneni, Moriah Ausherman, Guru Madiraddy, M. Sealy, G. Hwang
Tailored wick structures are essential to develop efficient two-phase thermal management systems in various engineering applications, however, manufacturing a geometrically-complex wick is challenging using conventional manufacturing processes due to limited manufacturability and poor cost effectiveness. Additive manufacturing is an ideal alternative, however, the state-of-the-art metal three-dimensional printers have poor manufacturability when depositing pre-designed porous wicks with pore sizes below 100 μm. In this paper, a powder bed fusion 3D printer (Matsuura Lumex Avance-25) was employed to fabricate metallic wicks through partial sintering for pore sizes below 100 μm with data-driven control of process parameters. Hatch spacing and scan speed were selected as the two main AM process parameters to adjust. Due to the unavailability of process maps between the process parameters and properties of printed metallic wick structures, different surrogate-based models were employed to identify the combinations of the two process parameters that result in improved manufacturability of wick structures. Since the generation of training points for surrogate model training through experimentation is expensive and time-consuming, Bayesian optimization was used for sequential and intelligent selection of training points that provide maximum information gain regarding the relationships between the process parameters and the manufacturability of a 3D printed wick structure. The relationship between the required number of training points and model prediction accuracy was investigated. The AM parameters’ ranges were discretized using six values of hatch spacing and seven values of scan speed, which resulted in a total of 42 combinations across the two parameters. Preliminary results conclude that 80% prediction accuracy is achievable with approximately forty training points (only 10% of total combinations). This study provides insights into the selection of optimal process parameters for the desired additively-manufactured wick structure performance.
{"title":"Process Mapping of Additively-Manufactured Metallic Wicks Through Surrogate Modeling","authors":"Mohammad Borumand, Sima Esfandiarpour Borujeni, S. Nannapaneni, Moriah Ausherman, Guru Madiraddy, M. Sealy, G. Hwang","doi":"10.1115/imece2021-71241","DOIUrl":"https://doi.org/10.1115/imece2021-71241","url":null,"abstract":"\u0000 Tailored wick structures are essential to develop efficient two-phase thermal management systems in various engineering applications, however, manufacturing a geometrically-complex wick is challenging using conventional manufacturing processes due to limited manufacturability and poor cost effectiveness. Additive manufacturing is an ideal alternative, however, the state-of-the-art metal three-dimensional printers have poor manufacturability when depositing pre-designed porous wicks with pore sizes below 100 μm. In this paper, a powder bed fusion 3D printer (Matsuura Lumex Avance-25) was employed to fabricate metallic wicks through partial sintering for pore sizes below 100 μm with data-driven control of process parameters. Hatch spacing and scan speed were selected as the two main AM process parameters to adjust. Due to the unavailability of process maps between the process parameters and properties of printed metallic wick structures, different surrogate-based models were employed to identify the combinations of the two process parameters that result in improved manufacturability of wick structures. Since the generation of training points for surrogate model training through experimentation is expensive and time-consuming, Bayesian optimization was used for sequential and intelligent selection of training points that provide maximum information gain regarding the relationships between the process parameters and the manufacturability of a 3D printed wick structure. The relationship between the required number of training points and model prediction accuracy was investigated. The AM parameters’ ranges were discretized using six values of hatch spacing and seven values of scan speed, which resulted in a total of 42 combinations across the two parameters. Preliminary results conclude that 80% prediction accuracy is achievable with approximately forty training points (only 10% of total combinations). This study provides insights into the selection of optimal process parameters for the desired additively-manufactured wick structure performance.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130239971","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}
There has been a steadily increasing global market for Additively Manufactured (AM) products, with a growth forecast of USD 23.75 billion by 2027. Of the various industrial sectors applying AM, the automotive/motor vehicles market takes up approximately 18% share. Saying AM is being widely used in the automotive sector with rapidly growing application avenues is not an overstatement. One such section of the automotive industry is the classic cars. Classic cars are 20 years or more older cars no longer in regular production, preserved and restored for their historical value. Classic cars face a huge problem of spare parts. The non-availability of the spare part leads to the break-down of the car, leaving them as display pieces or eventual scrapping. It is not economically viable to manufacture the spare parts in small volume due to challenges such as high cost of tooling, and indefinite storage time. Additive manufacturing offers attractive solutions to problems precisely such as these as it requires no additional tooling and can produce functional parts in small batches on-demand, provided accurate three-dimensional model data is available. This 3D model data is converted to one of the AM compatible file formats such as STL, AMF, 3MF etc. and then is processed using a Slicer Software. The slicer software converts three-dimensional (3-D) model data to two-dimensional (2-D) layer information that will be printed by the AM machine. Obtaining drawings or 3-D model information for classic car parts is a daunting challenge in itself, often deemed impossible. However, with the advances in imaging and scanning combined with computer aided design technologies, it is shown to be possible to generate the 3-D model data from even partial or broken parts. Now, producing spare parts using AM is not just feasible but has been successfully applied. Few notable examples include restoration of Elvis Presley’s BMW 507, originally released in 1957, which took two years to complete, Jaguar’s XK120 SE restored in 2017, 2019 restorations of Volkswagens iconic 1962 minivan, Bentley’s 1929 Blowers and Bugatti’s 1926 Bugatti Baby. Not just car manufacturers, but hobbyist collectors also found success in producing spare parts for their classic cars. This paper discusses various types of additive manufacturing technologies used to manufacture classic car parts and the strategic impact after implementing them using the examples of famous restored classic cars. The discussion further includes commercialization of these technologies, challenges, material selection and availability. Additionally, the economic implications and, the future are explored.
{"title":"Extending the Life of Classic Cars, the Additive Manufacturing Way","authors":"T. Luniya, Geetha Pravallika Chimata","doi":"10.1115/imece2021-70355","DOIUrl":"https://doi.org/10.1115/imece2021-70355","url":null,"abstract":"\u0000 There has been a steadily increasing global market for Additively Manufactured (AM) products, with a growth forecast of USD 23.75 billion by 2027. Of the various industrial sectors applying AM, the automotive/motor vehicles market takes up approximately 18% share. Saying AM is being widely used in the automotive sector with rapidly growing application avenues is not an overstatement. One such section of the automotive industry is the classic cars. Classic cars are 20 years or more older cars no longer in regular production, preserved and restored for their historical value. Classic cars face a huge problem of spare parts. The non-availability of the spare part leads to the break-down of the car, leaving them as display pieces or eventual scrapping. It is not economically viable to manufacture the spare parts in small volume due to challenges such as high cost of tooling, and indefinite storage time.\u0000 Additive manufacturing offers attractive solutions to problems precisely such as these as it requires no additional tooling and can produce functional parts in small batches on-demand, provided accurate three-dimensional model data is available. This 3D model data is converted to one of the AM compatible file formats such as STL, AMF, 3MF etc. and then is processed using a Slicer Software. The slicer software converts three-dimensional (3-D) model data to two-dimensional (2-D) layer information that will be printed by the AM machine. Obtaining drawings or 3-D model information for classic car parts is a daunting challenge in itself, often deemed impossible. However, with the advances in imaging and scanning combined with computer aided design technologies, it is shown to be possible to generate the 3-D model data from even partial or broken parts. Now, producing spare parts using AM is not just feasible but has been successfully applied. Few notable examples include restoration of Elvis Presley’s BMW 507, originally released in 1957, which took two years to complete, Jaguar’s XK120 SE restored in 2017, 2019 restorations of Volkswagens iconic 1962 minivan, Bentley’s 1929 Blowers and Bugatti’s 1926 Bugatti Baby. Not just car manufacturers, but hobbyist collectors also found success in producing spare parts for their classic cars. This paper discusses various types of additive manufacturing technologies used to manufacture classic car parts and the strategic impact after implementing them using the examples of famous restored classic cars. The discussion further includes commercialization of these technologies, challenges, material selection and availability. Additionally, the economic implications and, the future are explored.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133947073","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}
T. Grimm, Amit B. Deshpande, G. Parvathy, L. Mears
Within manufacturing, resistance spot welding (RSW) has been the traditional method of choice when joining steel-steel sheets. However, within the transportation industry, the use of lighter weight materials such as aluminum has become necessary in order to improve fuel economy. This has required the creation of new technologies and adaptations of traditional ones in order to successfully join these materials. One such adaptation, useful in joining aluminum-aluminum sheets, is friction element riveting (FER). This process is similar to the friction element welding process; however, two or more aluminum sheets are secured together between the element head and a relatively small steel sheet, which is termed lower element. Since this is a novel technology, the influence of different sized lower elements is unknown. A study is conducted which varies the diameter and thickness of the lower elements. A simulation was also created to estimate the thermal effects of these various geometries. Strength testing was used to determine the success of each parameter. It was discovered that the maximum joint strength occurs when using a lower element diameter of 25 mm and a thickness of 1.6 mm.
{"title":"Friction Element Riveting: Effects of Lower Element Geometry","authors":"T. Grimm, Amit B. Deshpande, G. Parvathy, L. Mears","doi":"10.1115/imece2021-68751","DOIUrl":"https://doi.org/10.1115/imece2021-68751","url":null,"abstract":"\u0000 Within manufacturing, resistance spot welding (RSW) has been the traditional method of choice when joining steel-steel sheets. However, within the transportation industry, the use of lighter weight materials such as aluminum has become necessary in order to improve fuel economy. This has required the creation of new technologies and adaptations of traditional ones in order to successfully join these materials.\u0000 One such adaptation, useful in joining aluminum-aluminum sheets, is friction element riveting (FER). This process is similar to the friction element welding process; however, two or more aluminum sheets are secured together between the element head and a relatively small steel sheet, which is termed lower element. Since this is a novel technology, the influence of different sized lower elements is unknown. A study is conducted which varies the diameter and thickness of the lower elements. A simulation was also created to estimate the thermal effects of these various geometries. Strength testing was used to determine the success of each parameter. It was discovered that the maximum joint strength occurs when using a lower element diameter of 25 mm and a thickness of 1.6 mm.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134295338","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, a numerically hybrid model is presented for the lattice structures to reduce the computational cost of the simulations. This approach consists of utilization of solid elements for the junctions and beam elements for the microbeams connecting the corresponding junctions to each other. To take into account the geometric defects, for each microbeam of the lattice structures, an ellipse is fitted to capture the effect of shape variation and roughness. Having the parameters of the ellipses, the lattice structures are constructed in Spaceclaim (ANSYS) using the geometrical hybrid approach. When the global response of the structure is linear, the results from the hybrid models are in good agreement with the ones from the 3D models. However, the hybrid models have difficulty to converge when the effect of large deformation and local plasticity are considerable in the BCCZ structures. For BCCZ lattice structures, the results are not affected by the junction’s size. This is also valid for BCC lattice structures as long as the ratio of the junction’s size to the diameter of the microbeams is greater than 2.
{"title":"A Numerical Hybrid Finite Element Model for Lattice Structures Using 3D/Beam Elements","authors":"A. Tahmasebimoradi, C. Mang, X. Lorang","doi":"10.1115/imece2021-69119","DOIUrl":"https://doi.org/10.1115/imece2021-69119","url":null,"abstract":"\u0000 In this work, a numerically hybrid model is presented for the lattice structures to reduce the computational cost of the simulations. This approach consists of utilization of solid elements for the junctions and beam elements for the microbeams connecting the corresponding junctions to each other. To take into account the geometric defects, for each microbeam of the lattice structures, an ellipse is fitted to capture the effect of shape variation and roughness. Having the parameters of the ellipses, the lattice structures are constructed in Spaceclaim (ANSYS) using the geometrical hybrid approach. When the global response of the structure is linear, the results from the hybrid models are in good agreement with the ones from the 3D models. However, the hybrid models have difficulty to converge when the effect of large deformation and local plasticity are considerable in the BCCZ structures. For BCCZ lattice structures, the results are not affected by the junction’s size. This is also valid for BCC lattice structures as long as the ratio of the junction’s size to the diameter of the microbeams is greater than 2.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132977580","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}
Tufan G Yılmaz, O. Kalay, F. Karpat, S. Ekwaro-Osire
Additive manufacturing processes (AMP) have grown and spread in the last twenty years. Additive manufacturing methods, which were first used for plastic materials, are now increasingly finding a place in metals. With these methods, more lightweight component designs which cannot be generated with traditional methods can be manufactured. With the spreading of electric drive vehicles, weight reduction is becoming more important since weight is primarily responsible for energy consumption. There is a one-stage gear system in electric vehicles in general. For this reason, the subject of reducing the mass of gears is gaining importance. The weight reduction can be achieved with holes and slots on the gear body for involute spur gears or reducing gear web thickness. Several optimization methods can be used for this aim. Another way is to use light materials for the gear body, while steel material is used in the tooth-rim region. Carbon fiber composites are preferred for this purpose. However, using adhesives to join steel and carbon fiber reinforced plastics may cause problems in different environmental conditions. On the other side, parts are generated with single material with AMP methods. In this study, involute spur gears with different designs convenient for generation by AMP are created in a 3D CAD program. The involute tooth region is defined as design space. The effects of different designs on root stress and tooth stiffness are investigated by finite element analyses. For this purpose, the mathematical modeling of involute spur gear is set to get points of a tooth based on Litvin’s approach in MATLAB. A point cloud code is obtained and imported to the 3D CAD program. After that, three teeth 3D finite element spur gear models are generated. Static analyses are conducted in ANSYS. Meshing force is implemented on the highest point single tooth contact line. Root stress value is the most important reason for tooth root fatigue, one of the most common failure modes of involute spur gears. Tooth deflection and stiffness are significant parameters for the dynamic behavior of involute spur gears. The tooth stiffness affects mesh stiffness and transmission error which are the primary source of gear whine. For these reasons, tooth root stress and tooth deflection values should be determined for different gear designs. In this study, stress analyses of additive manufactured gears are conducted with the finite element method. The effect of shell thickness, infill radius, and infill stiffener on tooth root stress and deformation is recorded. According to the results, shell thickness is the most effective parameter on the root stress and deformation. It is followed by infill orientation angle and infill radius, respectively.
{"title":"Stress Analysis of Additive Manufactured Lightweight Spur Gears","authors":"Tufan G Yılmaz, O. Kalay, F. Karpat, S. Ekwaro-Osire","doi":"10.1115/imece2021-73666","DOIUrl":"https://doi.org/10.1115/imece2021-73666","url":null,"abstract":"\u0000 Additive manufacturing processes (AMP) have grown and spread in the last twenty years. Additive manufacturing methods, which were first used for plastic materials, are now increasingly finding a place in metals. With these methods, more lightweight component designs which cannot be generated with traditional methods can be manufactured. With the spreading of electric drive vehicles, weight reduction is becoming more important since weight is primarily responsible for energy consumption. There is a one-stage gear system in electric vehicles in general. For this reason, the subject of reducing the mass of gears is gaining importance. The weight reduction can be achieved with holes and slots on the gear body for involute spur gears or reducing gear web thickness. Several optimization methods can be used for this aim. Another way is to use light materials for the gear body, while steel material is used in the tooth-rim region. Carbon fiber composites are preferred for this purpose. However, using adhesives to join steel and carbon fiber reinforced plastics may cause problems in different environmental conditions. On the other side, parts are generated with single material with AMP methods. In this study, involute spur gears with different designs convenient for generation by AMP are created in a 3D CAD program. The involute tooth region is defined as design space. The effects of different designs on root stress and tooth stiffness are investigated by finite element analyses. For this purpose, the mathematical modeling of involute spur gear is set to get points of a tooth based on Litvin’s approach in MATLAB. A point cloud code is obtained and imported to the 3D CAD program. After that, three teeth 3D finite element spur gear models are generated. Static analyses are conducted in ANSYS. Meshing force is implemented on the highest point single tooth contact line. Root stress value is the most important reason for tooth root fatigue, one of the most common failure modes of involute spur gears. Tooth deflection and stiffness are significant parameters for the dynamic behavior of involute spur gears. The tooth stiffness affects mesh stiffness and transmission error which are the primary source of gear whine. For these reasons, tooth root stress and tooth deflection values should be determined for different gear designs.\u0000 In this study, stress analyses of additive manufactured gears are conducted with the finite element method. The effect of shell thickness, infill radius, and infill stiffener on tooth root stress and deformation is recorded. According to the results, shell thickness is the most effective parameter on the root stress and deformation. It is followed by infill orientation angle and infill radius, respectively.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116777943","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}
Tianci Li, Lele Zhang, Geng Chen, Thomas Schopphoven, A. Gasser, R. Poprawe
Due to the surface scratch, wear or cracks, the performance and dimension of high-speed train wheelseat axle reduce to a level that cannot meet the design requirement after a period of service. Reasonable and efficient maintenance of those worn and valuable parts yields both economical and ecological benefit. In this study, we focused on the feasibility of using extreme high-speed laser material deposition (EHLA) for the repair of full-scale high-speed train wheelseat axle. To this end, AISI 4140 powder particles were selected as additive materials, and suitable process parameters were determined for the production of reduced test pieces. To investigate the possibility of transferring the process parameters determined through reduced piece testing to the full-scale wheelseat axle, finite element (FE) analyses were conducted. In these analyses the temperature distributions for different diameters of substrate with same process parameters were compared, and the results indicate that the process parameters obtained from the reduced sample studies can be transferred to the repair of full-scale axles. On the other hand, however, to consider the effects of thermal history on the clad properties, supplementary heating treatment should be carried out during the EHLA process.
{"title":"Process Prediction for Repair of High-Speed Train Wheelseat Axle by Extreme High-Speed Laser Material Deposition (EHLA)","authors":"Tianci Li, Lele Zhang, Geng Chen, Thomas Schopphoven, A. Gasser, R. Poprawe","doi":"10.1115/imece2021-72272","DOIUrl":"https://doi.org/10.1115/imece2021-72272","url":null,"abstract":"\u0000 Due to the surface scratch, wear or cracks, the performance and dimension of high-speed train wheelseat axle reduce to a level that cannot meet the design requirement after a period of service. Reasonable and efficient maintenance of those worn and valuable parts yields both economical and ecological benefit. In this study, we focused on the feasibility of using extreme high-speed laser material deposition (EHLA) for the repair of full-scale high-speed train wheelseat axle. To this end, AISI 4140 powder particles were selected as additive materials, and suitable process parameters were determined for the production of reduced test pieces. To investigate the possibility of transferring the process parameters determined through reduced piece testing to the full-scale wheelseat axle, finite element (FE) analyses were conducted. In these analyses the temperature distributions for different diameters of substrate with same process parameters were compared, and the results indicate that the process parameters obtained from the reduced sample studies can be transferred to the repair of full-scale axles. On the other hand, however, to consider the effects of thermal history on the clad properties, supplementary heating treatment should be carried out during the EHLA process.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126167803","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 front matter for this proceedings is available by clicking on the PDF icon.
通过点击PDF图标可获得本次会议的主题。
{"title":"IMECE2021 Front Matter","authors":"","doi":"10.1115/imece2021-fm2a","DOIUrl":"https://doi.org/10.1115/imece2021-fm2a","url":null,"abstract":"\u0000 The front matter for this proceedings is available by clicking on the PDF icon.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123541188","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}
All additive manufacturing processes have a characteristic ‘staircase’ layering effect at the boundaries, as this process family fabricates components by stacking layers upon each other. This effect is noticeable at shallow angles and where there is significant surface curvature. Measuring the surface roughness from virtually modeled beads of an additive manufactured product helps to have an initial estimation of the surface quality during process planning. In this paper, three techniques are developed to measure the profile surface roughness from an ‘as-built’ CAD model generated from theoretical bead geometries. Two CAD files are needed as inputs: a model of the ideal part geometry and the model created based on the bead geometry, percent bead overlap, and the fill strategy. Developed solutions are projection, projection normal-line distance, and elongation method. The results are verified by analytical calculations with less than one percent variation. The samples include flat faces, a curved surface, and an S shape (a double arc). Sensitivity studies for evaluation length are conducted as well. Estimation of the surface roughness values before a component is being built will help designers to evaluate how much material stock is needed to be added if subsequent machining processes are required. Therefore, it is anticipated that this research will assist process planners in developing their desired build solutions for both AM and hybrid manufacturing.
{"title":"Virtual Surface Roughness Measurements From an ‘As-Built’ Virtual CAD Model for Bead Based Deposition Additive Manufactured Components","authors":"H. Kalami, J. Urbanic","doi":"10.1115/imece2021-72044","DOIUrl":"https://doi.org/10.1115/imece2021-72044","url":null,"abstract":"\u0000 All additive manufacturing processes have a characteristic ‘staircase’ layering effect at the boundaries, as this process family fabricates components by stacking layers upon each other. This effect is noticeable at shallow angles and where there is significant surface curvature. Measuring the surface roughness from virtually modeled beads of an additive manufactured product helps to have an initial estimation of the surface quality during process planning. In this paper, three techniques are developed to measure the profile surface roughness from an ‘as-built’ CAD model generated from theoretical bead geometries. Two CAD files are needed as inputs: a model of the ideal part geometry and the model created based on the bead geometry, percent bead overlap, and the fill strategy. Developed solutions are projection, projection normal-line distance, and elongation method.\u0000 The results are verified by analytical calculations with less than one percent variation. The samples include flat faces, a curved surface, and an S shape (a double arc). Sensitivity studies for evaluation length are conducted as well.\u0000 Estimation of the surface roughness values before a component is being built will help designers to evaluate how much material stock is needed to be added if subsequent machining processes are required. Therefore, it is anticipated that this research will assist process planners in developing their desired build solutions for both AM and hybrid manufacturing.","PeriodicalId":141381,"journal":{"name":"Volume 2A: Advanced Manufacturing","volume":"348 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115676530","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：