This work introduces a Virtual Reality (VR) Exergame application to prevent Work Related Musculoskeletal Disorders (WMSDs). WMSDs are an important issue that can have a direct economic impact since they can injure workers, who are then forced to take time off. Exercise and stretching is one method that can benefit workers’ muscles and help prevent WMSDs. While several applications have been developed to prevent WMSDs, most of the existing applications suffer from a lack of immersivity or just focus on education and not necessarily helping workers warm-up or stretch. Hence, this work presents an Exergame application that leverages VR and Depth-sensor technology to help provide users with an immersive first-person experience. The objective of the VR Exergame is to encourage and motivate users to perform full-body movements in order to pass through a series of obstacles. The application implements a variety of game elements to help motivate users to play the game and stretch. While in the game, users can visualize their motions by controlling the virtual avatar with their body movements. It is expected that this immersivity will motivate and encourage the users. Initial findings show the positive effects that the base exergame has on individuals’ motivation and physical activity level. The results indicate that the application was able to engage individuals in low-intensity exercises that produced significant and consistent increases in their heart rate. Lastly, this work explores the development and benefits that this VR Exergame could bring by motivating workers and preventing WMSDs.
{"title":"Virtual Reality Exergames: Promoting Physical Health Among Industry Workers","authors":"Thomas Stranick, C. López","doi":"10.1115/detc2021-67608","DOIUrl":"https://doi.org/10.1115/detc2021-67608","url":null,"abstract":"\u0000 This work introduces a Virtual Reality (VR) Exergame application to prevent Work Related Musculoskeletal Disorders (WMSDs). WMSDs are an important issue that can have a direct economic impact since they can injure workers, who are then forced to take time off. Exercise and stretching is one method that can benefit workers’ muscles and help prevent WMSDs. While several applications have been developed to prevent WMSDs, most of the existing applications suffer from a lack of immersivity or just focus on education and not necessarily helping workers warm-up or stretch. Hence, this work presents an Exergame application that leverages VR and Depth-sensor technology to help provide users with an immersive first-person experience. The objective of the VR Exergame is to encourage and motivate users to perform full-body movements in order to pass through a series of obstacles. The application implements a variety of game elements to help motivate users to play the game and stretch. While in the game, users can visualize their motions by controlling the virtual avatar with their body movements. It is expected that this immersivity will motivate and encourage the users. Initial findings show the positive effects that the base exergame has on individuals’ motivation and physical activity level. The results indicate that the application was able to engage individuals in low-intensity exercises that produced significant and consistent increases in their heart rate. Lastly, this work explores the development and benefits that this VR Exergame could bring by motivating workers and preventing WMSDs.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"82 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77127806","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}
Rakesh Suresh Kumar, S. Jujjavarapu, Lung Hao Lee, E. Esfahani
Knowledge about human cognitive and physical state is a key factor in physical Human-robot collaboration (pHRC). Such information benefits the robot in planning an adaptive control strategy to prevent or mitigate human fatigue. In this paper, we present a method to detect upper limb muscle fatigue during pHRC using a low-cost myoelectric sensor. We used Riemann geometry to extract robust features from the time-series data and designed a classifier to detect the binary state of human fatigue i.e. fatigued vs not fatigued. We evaluated the method using a fine-motor coordination task for the human to guide an industrial robot along a virtual path for sometime followed by a muscle curl exercise until it induces fatigue in the muscles, and then repeat the robot experiment. We recruited nine participants for the study and recorded muscle activity from their dominant upper limb using the myoelectric sensor and used the data to develop a classifier. We compared the accuracy and robustness of the classifier against conventional time-domain and wavelet-based features and showed that Riemann geometry-based features yield higher classification accuracy (∼ 91%) compared to conventional features and require less computational effort. Such classifier can be used in real-time to develop a human-aware adaptation strategy to prevent fatigue.
{"title":"Fatigue Detection for Human Aware Adaptation in Human-Robot Collaboration","authors":"Rakesh Suresh Kumar, S. Jujjavarapu, Lung Hao Lee, E. Esfahani","doi":"10.1115/detc2021-70975","DOIUrl":"https://doi.org/10.1115/detc2021-70975","url":null,"abstract":"\u0000 Knowledge about human cognitive and physical state is a key factor in physical Human-robot collaboration (pHRC). Such information benefits the robot in planning an adaptive control strategy to prevent or mitigate human fatigue. In this paper, we present a method to detect upper limb muscle fatigue during pHRC using a low-cost myoelectric sensor. We used Riemann geometry to extract robust features from the time-series data and designed a classifier to detect the binary state of human fatigue i.e. fatigued vs not fatigued. We evaluated the method using a fine-motor coordination task for the human to guide an industrial robot along a virtual path for sometime followed by a muscle curl exercise until it induces fatigue in the muscles, and then repeat the robot experiment. We recruited nine participants for the study and recorded muscle activity from their dominant upper limb using the myoelectric sensor and used the data to develop a classifier. We compared the accuracy and robustness of the classifier against conventional time-domain and wavelet-based features and showed that Riemann geometry-based features yield higher classification accuracy (∼ 91%) compared to conventional features and require less computational effort. Such classifier can be used in real-time to develop a human-aware adaptation strategy to prevent fatigue.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76558338","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}
Lattice structures (LS) manufactured by 3D printing are widely applied in many areas, such as aerospace and tissue engineering, due to their lightweight and adjustable mechanical properties. It is necessary to reduce costs by predicting the mechanical properties of LS at the design stage since 3D printing is exorbitant at present. However, predicting mechanical properties quickly and accurately poses a challenge. To address this problem, this study proposes a novel method that is applied to different LS and materials to predict their mechanical properties through machine learning. First, this study voxelised 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs of the machine learning model. The sample set includes 57 samples collected from previous studies. Support vector regression was used in this study to predict the mechanical properties. The results indicate that the proposed method can predict the mechanical properties of LS effectively and is suitable for different LS and materials. The significance of this work is that it provides a method with great potential to promote the design process of lattice structures by predicting their mechanical properties quickly and effectively.
{"title":"Predicting Mechanical Properties of 3D Printed Lattice Structures","authors":"Shuai Ma, Qian Tang, Ying Liu, Qixiang Feng","doi":"10.1115/detc2021-70249","DOIUrl":"https://doi.org/10.1115/detc2021-70249","url":null,"abstract":"\u0000 Lattice structures (LS) manufactured by 3D printing are widely applied in many areas, such as aerospace and tissue engineering, due to their lightweight and adjustable mechanical properties. It is necessary to reduce costs by predicting the mechanical properties of LS at the design stage since 3D printing is exorbitant at present. However, predicting mechanical properties quickly and accurately poses a challenge. To address this problem, this study proposes a novel method that is applied to different LS and materials to predict their mechanical properties through machine learning. First, this study voxelised 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs of the machine learning model. The sample set includes 57 samples collected from previous studies. Support vector regression was used in this study to predict the mechanical properties. The results indicate that the proposed method can predict the mechanical properties of LS effectively and is suitable for different LS and materials. The significance of this work is that it provides a method with great potential to promote the design process of lattice structures by predicting their mechanical properties quickly and effectively.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"35 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91439932","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}
Despite a growing application of additive manufacturing, build volume has limited the size of fabricated parts. Machines that can produce large-scale parts in whole have high costs and less commercially available. A workaround is to partition the desired part into smaller partitions which can be manufactured in parallel, with the added benefit of controlling process parameters for each partition independently and reducing manufacturing time. This paper proposes an approach that divides a part into a cube skeleton covered by shell segments where all components can be fabricated with smaller 3D printers. The proposed algorithm first hollows out the original fully dense part to a user-specified thickness, then partitions the part into 26 surrounding regions using the six faces of the maximally inscribed cube (or cuboid). Islands, i.e., small, disconnected partitions within each region, are combined with the smallest neighbor to create up to 26 connected partitions. To minimize the number of printed partitions, the connected partitions are ranked based on their volume and combined with their smallest neighbor in pairs in descending order, while ensuring each pair fits within a pre-selected build volume of available 3D printers. The final partitioned shell segments, the cube (or cuboid) center, and the secondary layer of cubes propagated from the face centers of the maximally inscribed cube are generated by the algorithm. Results of two cases are shown.
{"title":"An Algorithm for Partitioning Objects Into a Cube Skeleton and Segmented Shell Covers for Parallelized Additive Manufacturing","authors":"Wilson Li, Thomas Poozhikala, Mahmoud Dinar","doi":"10.1115/detc2021-69326","DOIUrl":"https://doi.org/10.1115/detc2021-69326","url":null,"abstract":"\u0000 Despite a growing application of additive manufacturing, build volume has limited the size of fabricated parts. Machines that can produce large-scale parts in whole have high costs and less commercially available. A workaround is to partition the desired part into smaller partitions which can be manufactured in parallel, with the added benefit of controlling process parameters for each partition independently and reducing manufacturing time. This paper proposes an approach that divides a part into a cube skeleton covered by shell segments where all components can be fabricated with smaller 3D printers. The proposed algorithm first hollows out the original fully dense part to a user-specified thickness, then partitions the part into 26 surrounding regions using the six faces of the maximally inscribed cube (or cuboid). Islands, i.e., small, disconnected partitions within each region, are combined with the smallest neighbor to create up to 26 connected partitions. To minimize the number of printed partitions, the connected partitions are ranked based on their volume and combined with their smallest neighbor in pairs in descending order, while ensuring each pair fits within a pre-selected build volume of available 3D printers. The final partitioned shell segments, the cube (or cuboid) center, and the secondary layer of cubes propagated from the face centers of the maximally inscribed cube are generated by the algorithm. Results of two cases are shown.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73638047","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 substantial part of the global energy mix depends upon fossil fuels that needed to be reduced to overcome the pollution and environment-related challenges. This has directed the world to shift the energy mix towards renewable energy technologies. Among the development in renewable energy technologies, the development of solar tower power plant is an active research topic. Over the past decade, advances in computers and simulation software systems have greatly expanded their use in design and development, which can facilitate the engineering activities of solar tower power plants. However, an important limitation is the visualization of three-dimensional geometrical design data onto two-dimensional computer screens. VR technologies are a great means in the visualization of 3D data. Therefore, this article attempts to illustrate a concept for the application of VR technologies in the development of solar tower power plant and lists down relevant support scenarios. The main focus of the paper is on analyzing the efficiency of the VR technology used in the design of solar tower power plants and learning from the experience gained in this process. A discussion about further scenarios ranging from on-site visualization of solar tower power plant infrastructure, installation and repair, cleaning and maintenance, etc. is included as well as future directions are pointed out. The demonstrator part consists of an Android Smartphone-based VR application and an HMD based VR application. Furthermore, a brief comparison of both the applications as well as of HMD and sVR is also provided.
{"title":"Virtual Reality (VR) for the Support of the Analysis and Operation of a Solar Thermal Tower Power Plant","authors":"Kamran Mahboob, Atif Mahboob, S. Husung","doi":"10.1115/detc2021-70202","DOIUrl":"https://doi.org/10.1115/detc2021-70202","url":null,"abstract":"\u0000 A substantial part of the global energy mix depends upon fossil fuels that needed to be reduced to overcome the pollution and environment-related challenges. This has directed the world to shift the energy mix towards renewable energy technologies. Among the development in renewable energy technologies, the development of solar tower power plant is an active research topic. Over the past decade, advances in computers and simulation software systems have greatly expanded their use in design and development, which can facilitate the engineering activities of solar tower power plants. However, an important limitation is the visualization of three-dimensional geometrical design data onto two-dimensional computer screens. VR technologies are a great means in the visualization of 3D data. Therefore, this article attempts to illustrate a concept for the application of VR technologies in the development of solar tower power plant and lists down relevant support scenarios. The main focus of the paper is on analyzing the efficiency of the VR technology used in the design of solar tower power plants and learning from the experience gained in this process. A discussion about further scenarios ranging from on-site visualization of solar tower power plant infrastructure, installation and repair, cleaning and maintenance, etc. is included as well as future directions are pointed out. The demonstrator part consists of an Android Smartphone-based VR application and an HMD based VR application. Furthermore, a brief comparison of both the applications as well as of HMD and sVR is also provided.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81341154","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}
J. Michopoulos, A. Iliopoulos, J. Steuben, N. Apetre, S. Douglass, A. G. Lynn, R. Cairns
Understanding, modeling and simulating the behavior of thermally and electrically conductive materials under simultaneous high electric current pulse and mechanical preload conditions has long been a topic of interest for various applications involving electromechanical systems. To this end, the present work describes a computational framework that enables the fully coupled electromagnetic and thermoelastic analysis of such systems. The partial differential equations (PDEs) representing the electrodynamic and thermodynamic conservation laws are utilized and encapsulated in a computational environment enabling their numerical solution. A specific contribution of the framework is that it is capable of solving the non-linear forms of the relevant PDEs that are formed due to the dependence of the material properties on state variables such as temperature. The proposed framework is applied for a specific high-current testing apparatus under construction in our laboratory. A high current pulse is conducted through a mechanically pretensioned specimen and generates Joule heating activating thermo-elastic strains in conjunction with Lorentz body forces influencing the associated dynamic thermo-structural response of specimens of interest. Application of the developed framework enables the generation of field predictions for the quantities of interest. Selective simulation results are presented to demonstrate the capabilities of the proposed framework followed by discussion and conclusions.
{"title":"Coupled Electromagnetic and Thermoelastic Response of Conductive Materials Under Mechanical Loading and High Current Pulse Conditions","authors":"J. Michopoulos, A. Iliopoulos, J. Steuben, N. Apetre, S. Douglass, A. G. Lynn, R. Cairns","doi":"10.1115/detc2021-71130","DOIUrl":"https://doi.org/10.1115/detc2021-71130","url":null,"abstract":"\u0000 Understanding, modeling and simulating the behavior of thermally and electrically conductive materials under simultaneous high electric current pulse and mechanical preload conditions has long been a topic of interest for various applications involving electromechanical systems. To this end, the present work describes a computational framework that enables the fully coupled electromagnetic and thermoelastic analysis of such systems. The partial differential equations (PDEs) representing the electrodynamic and thermodynamic conservation laws are utilized and encapsulated in a computational environment enabling their numerical solution. A specific contribution of the framework is that it is capable of solving the non-linear forms of the relevant PDEs that are formed due to the dependence of the material properties on state variables such as temperature. The proposed framework is applied for a specific high-current testing apparatus under construction in our laboratory. A high current pulse is conducted through a mechanically pretensioned specimen and generates Joule heating activating thermo-elastic strains in conjunction with Lorentz body forces influencing the associated dynamic thermo-structural response of specimens of interest. Application of the developed framework enables the generation of field predictions for the quantities of interest. Selective simulation results are presented to demonstrate the capabilities of the proposed framework followed by discussion and conclusions.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82118129","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 paper, we define Model Based Systems Engineering (MBSE) as a set of different approaches which vary in scope and in purpose, as opposed to defining it as a monolithic concept. To do so, we inductively extract common themes from papers proposing new MBSE methods based on the type of Systems Engineering (SE) artifacts produced and the expected benefits of MBSE implementation. These themes are then validated against the experiences depicted in a second set of papers evaluating the deployment of MBSE methods in practice. We propose a taxonomy for MBSE which identifies three main categories: system specification repositories, system execution models, and design automation models. The proposed categories map well onto common discussions of the nature of the SE activity, in that the first is employed in the management of system development processes and the second in the understanding of system performance and emergent properties. The third category is almost exclusively discussed in an academic context and is therefore more difficult to relate to SE practice, but its features are clearly distinct from the other two. The proposed taxonomy clarifies what MBSE is and what it can be, therefore helping focus research on the issues that still prevent MBSE practice from living up to expectations.
{"title":"A Taxonomy for Model-Based Systems Engineering","authors":"João P. Monteiro, Paulo J. S. Gil, Rui M. Rocha","doi":"10.1115/detc2021-69125","DOIUrl":"https://doi.org/10.1115/detc2021-69125","url":null,"abstract":"\u0000 In this paper, we define Model Based Systems Engineering (MBSE) as a set of different approaches which vary in scope and in purpose, as opposed to defining it as a monolithic concept. To do so, we inductively extract common themes from papers proposing new MBSE methods based on the type of Systems Engineering (SE) artifacts produced and the expected benefits of MBSE implementation. These themes are then validated against the experiences depicted in a second set of papers evaluating the deployment of MBSE methods in practice. We propose a taxonomy for MBSE which identifies three main categories: system specification repositories, system execution models, and design automation models. The proposed categories map well onto common discussions of the nature of the SE activity, in that the first is employed in the management of system development processes and the second in the understanding of system performance and emergent properties. The third category is almost exclusively discussed in an academic context and is therefore more difficult to relate to SE practice, but its features are clearly distinct from the other two. The proposed taxonomy clarifies what MBSE is and what it can be, therefore helping focus research on the issues that still prevent MBSE practice from living up to expectations.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77678346","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 effectiveness of our interaction with the computer-generated environments is subject to our physical limitations in real life such as our ability of discriminating differences in stiffness or roughness. This ability, represented by Weber fractions, is usually quantified by means of psychophysical experimentation. The experimentation process is tedious and repetitive as it requires the same task to be completed by participants until the mastery at a certain stimulus level can be ensured before moving onto the next level. Moreover, these thresholds are dependent on the tested standard stimulus level and, therefore, need to be identified by separate experiments for every possible standard stimulus level. The purpose of the current study is to reduce the amount of experimentation and predict the thresholds for stiffness discrimination of individuals after being tested at a single stimulus level. The prediction models tested provide a moderate level of prediction power, but more features, potentially physical and demographical in nature, are needed to increase their effectiveness. The procedure described herein can be extended to any modality other than stiffness and, therefore, has the potential to predict overall palpation effectiveness of an individual after a feasible amount of data is obtained through experimentation.
{"title":"An Application of Machine Learning to Predict Stiffness Discrimination Thresholds Using Haptics","authors":"Ernur Karadoğan","doi":"10.1115/detc2021-69337","DOIUrl":"https://doi.org/10.1115/detc2021-69337","url":null,"abstract":"\u0000 The effectiveness of our interaction with the computer-generated environments is subject to our physical limitations in real life such as our ability of discriminating differences in stiffness or roughness. This ability, represented by Weber fractions, is usually quantified by means of psychophysical experimentation. The experimentation process is tedious and repetitive as it requires the same task to be completed by participants until the mastery at a certain stimulus level can be ensured before moving onto the next level. Moreover, these thresholds are dependent on the tested standard stimulus level and, therefore, need to be identified by separate experiments for every possible standard stimulus level. The purpose of the current study is to reduce the amount of experimentation and predict the thresholds for stiffness discrimination of individuals after being tested at a single stimulus level. The prediction models tested provide a moderate level of prediction power, but more features, potentially physical and demographical in nature, are needed to increase their effectiveness. The procedure described herein can be extended to any modality other than stiffness and, therefore, has the potential to predict overall palpation effectiveness of an individual after a feasible amount of data is obtained through experimentation.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87870461","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 paper proposes a heat-flux based topology optimization approach to design self-supported enclosed voids for additive manufacturing. The enclosed overhangs that require supports in additive manufacturing are removed from the optimized design by constraining the maximum temperature of a pseudo heat conduction problem. In the pseudo problem, heat flux is applied on the non-self-supported open and enclosed surfaces. Since the density-based topology optimization involves no explicit boundary representation, we impose such surface slope dependent heat flux through a domain integral of a Heaviside projected density gradient. In addition, the solid materials and the void materials in the pseudo problem are assumed to be thermally insulating and conductive, respectively. As such, heat flux on the open surfaces can be successfully conducted to external heat sink through the void (or conductive) materials. However, heat flux on the non-self-supported enclosed surfaces is isolated by the solid (or insulating) materials and thus leads to locally high temperature. Hence, by limiting the maximum temperature of the pseudo problem, self-supported enclosed voids can be achieved, and the slope of the open surfaces would not be affected. Numerical examples are presented to demonstrate the validity and effectiveness of the proposed approach in the design of self-supported enclosed voids.
{"title":"Topology Optimization of Self-Supported Enclosed Voids for Additive Manufacturing","authors":"Cunfu Wang","doi":"10.1115/detc2021-68785","DOIUrl":"https://doi.org/10.1115/detc2021-68785","url":null,"abstract":"\u0000 The paper proposes a heat-flux based topology optimization approach to design self-supported enclosed voids for additive manufacturing. The enclosed overhangs that require supports in additive manufacturing are removed from the optimized design by constraining the maximum temperature of a pseudo heat conduction problem. In the pseudo problem, heat flux is applied on the non-self-supported open and enclosed surfaces. Since the density-based topology optimization involves no explicit boundary representation, we impose such surface slope dependent heat flux through a domain integral of a Heaviside projected density gradient. In addition, the solid materials and the void materials in the pseudo problem are assumed to be thermally insulating and conductive, respectively. As such, heat flux on the open surfaces can be successfully conducted to external heat sink through the void (or conductive) materials. However, heat flux on the non-self-supported enclosed surfaces is isolated by the solid (or insulating) materials and thus leads to locally high temperature. Hence, by limiting the maximum temperature of the pseudo problem, self-supported enclosed voids can be achieved, and the slope of the open surfaces would not be affected. Numerical examples are presented to demonstrate the validity and effectiveness of the proposed approach in the design of self-supported enclosed voids.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85361230","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}
Nessrine Elloumi, A. Ben Makhlouf, B. Louhichi, D. Deneux
For several years, research has been brought to reconstruct a Computer Aided Design (CAD) model from a 3D mesh or point cloud produced by scanning techniques or CAD software. This process, which recreates the geometry of a real part is called Revere Engineering (RE). In the industry, RE enables designers and engineers to virtually simulate, test and validate products before the manufacturing process. Therefore, it is common to use a reconstruction algorithm to rebuild a CAD model of a real object with high quality. This technique solves several problems of exchanging geometric models in engineering, computer vision, computer graphics, 3D animation, medical, mechanic, virtual/ augmented reality, etc. Therefore, CAD model reconstruction promotes the integration of 3D data without recopying or manual transformations and facilitates the visualization and simulation of the deformed model behavior. The aim of this work is how to rebuild a CAD model starting from a deformed mesh. The complexity of this problem requires to be split into several complementary parts. 3D surface reconstruction is considered as the most difficult step to obtain this geometric model. This paper consists in presenting an original method to reconstruct a CAD model surface (NURBS surface) starting from a triangulation (meshed surface) as a main step of the geometric model reconstruction.
{"title":"Towards a Building Techniques of a BREP Model Starting From a Meshed Surface","authors":"Nessrine Elloumi, A. Ben Makhlouf, B. Louhichi, D. Deneux","doi":"10.1115/detc2021-71724","DOIUrl":"https://doi.org/10.1115/detc2021-71724","url":null,"abstract":"\u0000 For several years, research has been brought to reconstruct a Computer Aided Design (CAD) model from a 3D mesh or point cloud produced by scanning techniques or CAD software. This process, which recreates the geometry of a real part is called Revere Engineering (RE). In the industry, RE enables designers and engineers to virtually simulate, test and validate products before the manufacturing process. Therefore, it is common to use a reconstruction algorithm to rebuild a CAD model of a real object with high quality. This technique solves several problems of exchanging geometric models in engineering, computer vision, computer graphics, 3D animation, medical, mechanic, virtual/ augmented reality, etc. Therefore, CAD model reconstruction promotes the integration of 3D data without recopying or manual transformations and facilitates the visualization and simulation of the deformed model behavior.\u0000 The aim of this work is how to rebuild a CAD model starting from a deformed mesh. The complexity of this problem requires to be split into several complementary parts. 3D surface reconstruction is considered as the most difficult step to obtain this geometric model. This paper consists in presenting an original method to reconstruct a CAD model surface (NURBS surface) starting from a triangulation (meshed surface) as a main step of the geometric model reconstruction.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"1098 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76735433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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