A. Rawlings, A. Birnbaum, J. Michopoulos, J. Steuben, A. Iliopoulos, H. Ryou
The formation of sub-grain cellular structures generated during the rapid solidification associated with selective laser melting (SLM) typically yields enhanced mechanical properties in terms of yield stress without considerable loss in ductility when compared with those of wrought material. The extent to which the sub-grain structure appears under standard metallographic preparation shows dependence on multiple systematic conditions. This study identifies the effects of solidification and cooling rate on the grain and sub-grain structure in stainless steel through varying the processing parameters (laser power, scan velocity and spot size) of single tracks on both as-received, small grain and annealed, giant grain substrates. The process parameters, in conjunction with the initial substrate microstructure, are key components in understanding the resulting microstructure. Process parameters, particularly scan velocity, dictate the solidification rate and primary regrowth directions while the initial microstructure and its thermomechanical history dictate the propensity for stored strain energy density. Modeling the thermal process allows for experimental analysis within the context of predicted location within processing space as it pertains to local interface velocity and temperature gradient. Furthermore, it highlights the fact that this specific material system behaves in a manner that is inconsistent with classical solidification theory.
{"title":"Simulation Informed Effects of Solidification Rate on 316L Single Tracks Produced by Selective Laser Melting","authors":"A. Rawlings, A. Birnbaum, J. Michopoulos, J. Steuben, A. Iliopoulos, H. Ryou","doi":"10.1115/detc2020-22451","DOIUrl":"https://doi.org/10.1115/detc2020-22451","url":null,"abstract":"\u0000 The formation of sub-grain cellular structures generated during the rapid solidification associated with selective laser melting (SLM) typically yields enhanced mechanical properties in terms of yield stress without considerable loss in ductility when compared with those of wrought material. The extent to which the sub-grain structure appears under standard metallographic preparation shows dependence on multiple systematic conditions. This study identifies the effects of solidification and cooling rate on the grain and sub-grain structure in stainless steel through varying the processing parameters (laser power, scan velocity and spot size) of single tracks on both as-received, small grain and annealed, giant grain substrates. The process parameters, in conjunction with the initial substrate microstructure, are key components in understanding the resulting microstructure. Process parameters, particularly scan velocity, dictate the solidification rate and primary regrowth directions while the initial microstructure and its thermomechanical history dictate the propensity for stored strain energy density. Modeling the thermal process allows for experimental analysis within the context of predicted location within processing space as it pertains to local interface velocity and temperature gradient. Furthermore, it highlights the fact that this specific material system behaves in a manner that is inconsistent with classical solidification theory.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124204436","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 porous cooling system has been proved to have significant advantages over traditional 2D conformal cooling channels due to its rapid cooling performance during the injection molding process. Compared to conventional porous systems, the conformal porous structures (CPS) have been proven to have even more uniform cooling performance and a reduced temperature variance of the part. For the part with unevenly distributed thickness values however, the temperature variance problem remains unsolved. In addition, there is a lack of modeling and optimization efforts on developing an optimal CPS structure with varying cooling cell sizes to achieve better cooling performances. To solve this problem, a machine learning approach is applied to predict the part surface temperature based on identified CPS design parameters. With this surrogate temperature prediction model, the optimization is performed to generate a machine learning aided design of CPS. The simulation results of a swimming pedal case study indicate that the machine learning aided CPS is able to achieve a 76% reduction in temperature variance compared to conventional CPS.
{"title":"Machine Learning Aided Design and Optimization of Conformal Porous Structures","authors":"Zhenyan Gao, Danièle Sossou, Y. Zhao","doi":"10.1115/detc2020-22150","DOIUrl":"https://doi.org/10.1115/detc2020-22150","url":null,"abstract":"\u0000 The porous cooling system has been proved to have significant advantages over traditional 2D conformal cooling channels due to its rapid cooling performance during the injection molding process. Compared to conventional porous systems, the conformal porous structures (CPS) have been proven to have even more uniform cooling performance and a reduced temperature variance of the part. For the part with unevenly distributed thickness values however, the temperature variance problem remains unsolved. In addition, there is a lack of modeling and optimization efforts on developing an optimal CPS structure with varying cooling cell sizes to achieve better cooling performances. To solve this problem, a machine learning approach is applied to predict the part surface temperature based on identified CPS design parameters. With this surrogate temperature prediction model, the optimization is performed to generate a machine learning aided design of CPS. The simulation results of a swimming pedal case study indicate that the machine learning aided CPS is able to achieve a 76% reduction in temperature variance compared to conventional CPS.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123754385","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. Iliopoulos, B. Graber, J. Michopoulos, J. Steuben, A. Birnbaum, E. Gorzkowski, E. Patterson, R. Fischer, L. Johnson, P. Bernhardt, J. M. Coombs
The microwave sintering of ceramics and other materials has emerged as an attractive method of manufacturing solid objects though volumetric approaches. The accurate modeling of such processes requires the knowledge of the dielectric constant, and particularly the real and imaginary parts of the permittivity, of these materials as they vary with temperature. This particular measurement becomes very challenging as the temperature rises. In this work, an experimental apparatus and an inverse approach are proposed, based on the coupling of the thermo-mechano-electromagnetic physics that can be used to measure the real and imaginary parts of the dielectric constant at high temperatures.
{"title":"A Computational Framework for the Inverse Identification of Temperature-Dependent Dielectric Permittivity of Materials at Giga-Hertz Frequencies","authors":"A. Iliopoulos, B. Graber, J. Michopoulos, J. Steuben, A. Birnbaum, E. Gorzkowski, E. Patterson, R. Fischer, L. Johnson, P. Bernhardt, J. M. Coombs","doi":"10.1115/detc2020-22500","DOIUrl":"https://doi.org/10.1115/detc2020-22500","url":null,"abstract":"\u0000 The microwave sintering of ceramics and other materials has emerged as an attractive method of manufacturing solid objects though volumetric approaches. The accurate modeling of such processes requires the knowledge of the dielectric constant, and particularly the real and imaginary parts of the permittivity, of these materials as they vary with temperature. This particular measurement becomes very challenging as the temperature rises. In this work, an experimental apparatus and an inverse approach are proposed, based on the coupling of the thermo-mechano-electromagnetic physics that can be used to measure the real and imaginary parts of the dielectric constant at high temperatures.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122737637","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}
Cycling is a widely popular exercise that is known to provide great health benefits. However, it has been questioned if cycling is responsible for genital numbness or Erectile Dysfunction (ED) due to compression of the perineum between the rider and the bicycle saddle. This study compares the perineal pressure distribution between three saddles (ISM, 3 West, and Fizik) for healthy, active male cyclists and a saddle recommendation is made. Using their own bikes, participants perform six randomized cycling trials (two per saddle) while sitting on a piezo-resistive pressure mat. Participants were asked to qualitatively rate the saddles after each trial. The quantitative results favor the ISM saddle due to its lower perineal pressure values, but the qualitative perceived comfort from the cyclists is split.
{"title":"Perineum Pressure Distribution Among Various Bicycle Saddles","authors":"Jazmin Cruz, Mario Garcia, E. Jackson, Jie Yang","doi":"10.1115/detc2020-22688","DOIUrl":"https://doi.org/10.1115/detc2020-22688","url":null,"abstract":"\u0000 Cycling is a widely popular exercise that is known to provide great health benefits. However, it has been questioned if cycling is responsible for genital numbness or Erectile Dysfunction (ED) due to compression of the perineum between the rider and the bicycle saddle. This study compares the perineal pressure distribution between three saddles (ISM, 3 West, and Fizik) for healthy, active male cyclists and a saddle recommendation is made. Using their own bikes, participants perform six randomized cycling trials (two per saddle) while sitting on a piezo-resistive pressure mat. Participants were asked to qualitatively rate the saddles after each trial. The quantitative results favor the ISM saddle due to its lower perineal pressure values, but the qualitative perceived comfort from the cyclists is split.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127087314","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}
Soban Babu Beemaraj, Rizwan Pathan, A. Salvi, Gehendra Sharma, F. Mistree, J. Allen
Composite materials are heterogeneous materials, and are hierarchical in nature consisting of multiple length scales. In the design of structures with composite materials, the micro-structure of the materials have a direct bearing on the final behavior of the structure. The deviations in the bulk material properties are caused due to uncertainties associated with the micro-structures and its propagation through different length scales. Uncertainties in the design parameters (geometry and materials properties etc.) at macro-scale also contribute to variations in the final behavior. Currently, these uncertainties are included as a large factor of safety in deterministic design, which may result in over design of the product. The robust performance of the structure can be achieved by considering these uncertainties explicitly in the design process. In this paper, a method for designing a robust composite structure subjected to different loading conditions is illustrated. Structural models are run to compute robust material properties and geometries for different load scenarios that yield most robust materials and micro-structures. Most robust combination of material and geometries is selected that results in most robust performance under all loading scenarios. These materials are designed using multiscale models in which micro-structural uncertainties are accounted. The uncertainties in the material properties and geometrical parameters at different length scales are explicitly modelled as ranges in the set of input parameters. Final performance variations are calculated using design capability index. Consolidated single material parameters and dimensions are selected using efficiency metrics. Design capability indices are formed as goals and constraints in compromise decision support problem. Robust micro-structures are designed inductively rather than deductively.
{"title":"Inverse Multi-Scale Robust Design of Composite Structures Using Design Capability Indices","authors":"Soban Babu Beemaraj, Rizwan Pathan, A. Salvi, Gehendra Sharma, F. Mistree, J. Allen","doi":"10.1115/detc2020-22259","DOIUrl":"https://doi.org/10.1115/detc2020-22259","url":null,"abstract":"\u0000 Composite materials are heterogeneous materials, and are hierarchical in nature consisting of multiple length scales. In the design of structures with composite materials, the micro-structure of the materials have a direct bearing on the final behavior of the structure. The deviations in the bulk material properties are caused due to uncertainties associated with the micro-structures and its propagation through different length scales. Uncertainties in the design parameters (geometry and materials properties etc.) at macro-scale also contribute to variations in the final behavior. Currently, these uncertainties are included as a large factor of safety in deterministic design, which may result in over design of the product. The robust performance of the structure can be achieved by considering these uncertainties explicitly in the design process. In this paper, a method for designing a robust composite structure subjected to different loading conditions is illustrated. Structural models are run to compute robust material properties and geometries for different load scenarios that yield most robust materials and micro-structures. Most robust combination of material and geometries is selected that results in most robust performance under all loading scenarios. These materials are designed using multiscale models in which micro-structural uncertainties are accounted. The uncertainties in the material properties and geometrical parameters at different length scales are explicitly modelled as ranges in the set of input parameters. Final performance variations are calculated using design capability index. Consolidated single material parameters and dimensions are selected using efficiency metrics. Design capability indices are formed as goals and constraints in compromise decision support problem. Robust micro-structures are designed inductively rather than deductively.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132244735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Eddy, S. Krishnamurty, I. Grosse, Michael White, Damon Blanchette
Defect prevention is particularly critical in operations such as aircraft assembly or service. Failure Modes and Effects Analysis (FMEA) procedures have been deployed manually for many years. However, the manual procedures fail to utilize capability to build intelligence into inspection processes that can facilitate elimination of human error. In this work, we introduce an artificial intelligence (AI)-based concept that can iteratively learn to assure zero defects from a given inspection process. This work introduces a schema that can serve as a knowledge management framework in a relational database for instantiation with inspection process information and data from a detection system. A companion algorithm is presented for the case of a wiring harness bracket installation in a fuselage. The schema and algorithm analyze and assess potential defects posed by Foreign Object Debris (FOD) in parallel to the assembly inspection. A closed loop of logic was introduced to enable anomaly detection by this algorithm to assure zero defects.
{"title":"A Defect Prevention Concept Using Artificial Intelligence","authors":"D. Eddy, S. Krishnamurty, I. Grosse, Michael White, Damon Blanchette","doi":"10.1115/detc2020-22112","DOIUrl":"https://doi.org/10.1115/detc2020-22112","url":null,"abstract":"\u0000 Defect prevention is particularly critical in operations such as aircraft assembly or service. Failure Modes and Effects Analysis (FMEA) procedures have been deployed manually for many years. However, the manual procedures fail to utilize capability to build intelligence into inspection processes that can facilitate elimination of human error. In this work, we introduce an artificial intelligence (AI)-based concept that can iteratively learn to assure zero defects from a given inspection process. This work introduces a schema that can serve as a knowledge management framework in a relational database for instantiation with inspection process information and data from a detection system. A companion algorithm is presented for the case of a wiring harness bracket installation in a fuselage. The schema and algorithm analyze and assess potential defects posed by Foreign Object Debris (FOD) in parallel to the assembly inspection. A closed loop of logic was introduced to enable anomaly detection by this algorithm to assure zero defects.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121186765","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}
Fitting models to data is critical in many science and engineering fields. A major task in fitting models to data is to estimate the value of each parameter in a given model. Iterative methods, such as the Gauss-Newton method and the Levenberg-Marquardt method, are often employed for parameter estimation in nonlinear models. However, practitioners must guess the initial value for each parameter in order to initialize these iterative methods. A poor initial guess can contribute to non-convergence of these methods or lead these methods to converge to a wrong solution. In this paper, an initial guess free method is introduced to find the optimal parameter estimators in a nonlinear model that minimizes the squared error of the fit. The method includes three algorithms that require different level of computational power to find the optimal parameter estimators. The method constructs a solution interval for each parameter in the model. These solution intervals significantly reduce the search space for optimal parameter estimators. The method also provides an empirical probability distribution for each parameter, which is valuable for parameter uncertainty assessment. The initial guess free method is validated through a case study in which Fick’s second law is fit to an experimental data set. This case study shows that the initial guess free method can find the optimal parameter estimators efficiently. A four-step procedure for implementing the initial guess free method in practice is also outlined.
{"title":"An Initial Guess Free Method for Least Squares Parameter Estimation in Nonlinear Models","authors":"Guanglu Zhang, D. Allaire, J. Cagan","doi":"10.1115/detc2020-22047","DOIUrl":"https://doi.org/10.1115/detc2020-22047","url":null,"abstract":"\u0000 Fitting models to data is critical in many science and engineering fields. A major task in fitting models to data is to estimate the value of each parameter in a given model. Iterative methods, such as the Gauss-Newton method and the Levenberg-Marquardt method, are often employed for parameter estimation in nonlinear models. However, practitioners must guess the initial value for each parameter in order to initialize these iterative methods. A poor initial guess can contribute to non-convergence of these methods or lead these methods to converge to a wrong solution. In this paper, an initial guess free method is introduced to find the optimal parameter estimators in a nonlinear model that minimizes the squared error of the fit. The method includes three algorithms that require different level of computational power to find the optimal parameter estimators. The method constructs a solution interval for each parameter in the model. These solution intervals significantly reduce the search space for optimal parameter estimators. The method also provides an empirical probability distribution for each parameter, which is valuable for parameter uncertainty assessment. The initial guess free method is validated through a case study in which Fick’s second law is fit to an experimental data set. This case study shows that the initial guess free method can find the optimal parameter estimators efficiently. A four-step procedure for implementing the initial guess free method in practice is also outlined.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115412877","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}
Satchit Ramnath, Payam Haghighi, Jiachen Ma, J. Shah, D. Detwiler
Machine learning is opening up new ways of optimizing designs, but it requires large data sets for training and verification. The primary focus of this paper is to explain the trade-offs between generating a large data set and the level of idealization required to automate the process of generating such a data set. This paper discusses the efforts in curating a large CAD data set with the desired variety and validity of automotive body structures. A method to incorporate constraint networks to filter invalid designs, prior to the start of model generation is explained. Since the geometric configurations and characteristics need to be correlated to performance (structural integrity), the paper also demonstrates automated workflows to perform finite element analysis on 3D CAD models generated. Key simulation results can then be associated with CAD geometry and fed to the machine learning algorithms. With the increase in computing power and network speed, such datasets could assist in generating better designs, which could potentially be obtained by a combination of existing ones, or might provide insights into completely new design concepts meeting or exceeding the performance requirements. The approach is explained using the hood frame as an example, but the same can be adopted to other design components.
{"title":"Design Science Meets Data Science: Curating Large Design Datasets for Engineered Artifacts","authors":"Satchit Ramnath, Payam Haghighi, Jiachen Ma, J. Shah, D. Detwiler","doi":"10.1115/detc2020-22377","DOIUrl":"https://doi.org/10.1115/detc2020-22377","url":null,"abstract":"\u0000 Machine learning is opening up new ways of optimizing designs, but it requires large data sets for training and verification. The primary focus of this paper is to explain the trade-offs between generating a large data set and the level of idealization required to automate the process of generating such a data set. This paper discusses the efforts in curating a large CAD data set with the desired variety and validity of automotive body structures. A method to incorporate constraint networks to filter invalid designs, prior to the start of model generation is explained. Since the geometric configurations and characteristics need to be correlated to performance (structural integrity), the paper also demonstrates automated workflows to perform finite element analysis on 3D CAD models generated. Key simulation results can then be associated with CAD geometry and fed to the machine learning algorithms. With the increase in computing power and network speed, such datasets could assist in generating better designs, which could potentially be obtained by a combination of existing ones, or might provide insights into completely new design concepts meeting or exceeding the performance requirements. The approach is explained using the hood frame as an example, but the same can be adopted to other design components.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115782174","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 article illustrates that structural design synthesis can be achieved through a sequential decision process, whereby a sparsely connected seed configuration is sequentially altered through discrete actions to generate the best design solution, with respect to a specified objective and constraints. Specifically, the generative design synthesis is mathematically formulated as a finite Markov Decision Process. In this context, the states correspond to a specific structural configuration, the actions correspond to the available alterations that can be made to a given configuration, and the immediate rewards are constructed to be proportional to the improvement in the altered configuration’s performance. In the context of generative structural design synthesis, since the immediate rewards are not known at the onset of the process, reinforcement learning is employed to obtain an approximately optimal policy by which to alter the seed configuration to synthesize the best design solution. The approach is applied for the optimization of planar truss structures and its utility is investigated with three numerical examples, each with unique domains and constraints.
{"title":"Structural Design Synthesis Through a Sequential Decision Process","authors":"Maximilian E. Ororbia, G. Warn","doi":"10.1115/detc2020-22647","DOIUrl":"https://doi.org/10.1115/detc2020-22647","url":null,"abstract":"\u0000 This article illustrates that structural design synthesis can be achieved through a sequential decision process, whereby a sparsely connected seed configuration is sequentially altered through discrete actions to generate the best design solution, with respect to a specified objective and constraints. Specifically, the generative design synthesis is mathematically formulated as a finite Markov Decision Process. In this context, the states correspond to a specific structural configuration, the actions correspond to the available alterations that can be made to a given configuration, and the immediate rewards are constructed to be proportional to the improvement in the altered configuration’s performance. In the context of generative structural design synthesis, since the immediate rewards are not known at the onset of the process, reinforcement learning is employed to obtain an approximately optimal policy by which to alter the seed configuration to synthesize the best design solution. The approach is applied for the optimization of planar truss structures and its utility is investigated with three numerical examples, each with unique domains and constraints.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124804218","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}
Lifting heavy weight is one of the main reasons for manual material handling related injuries which can be mitigated by determining the limiting lifting weight of a person. In this study, a 40 degrees of freedom (DOFs) spatial skeletal model was employed to predict the symmetric maximum weight lifting motion. The lifting problem was formulated as a multi-objective optimization (MOO) problem to minimize the dynamic effort and maximize the box weight. An inverse-dynamics-based optimization approach was used to determine the optimal lifting motion and the maximum lifting weight considering dynamic joint strength. The predicted lifting motion, ground reaction forces (GRFs), and maximum box weight were shown to match well with the experimental results. It was found that for the three-dimensional (3D) symmetric lifting the left and right GRFs were not same.
{"title":"Three-Dimensional Symmetric Maximum Weight Lifting Prediction","authors":"Rahid Zaman, Y. Xiang, Jazmin Cruz, Jie Yang","doi":"10.1115/detc2020-22120","DOIUrl":"https://doi.org/10.1115/detc2020-22120","url":null,"abstract":"\u0000 Lifting heavy weight is one of the main reasons for manual material handling related injuries which can be mitigated by determining the limiting lifting weight of a person. In this study, a 40 degrees of freedom (DOFs) spatial skeletal model was employed to predict the symmetric maximum weight lifting motion. The lifting problem was formulated as a multi-objective optimization (MOO) problem to minimize the dynamic effort and maximize the box weight. An inverse-dynamics-based optimization approach was used to determine the optimal lifting motion and the maximum lifting weight considering dynamic joint strength. The predicted lifting motion, ground reaction forces (GRFs), and maximum box weight were shown to match well with the experimental results. It was found that for the three-dimensional (3D) symmetric lifting the left and right GRFs were not same.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124234039","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：