In this study, the extractive summarization using sentence embeddings generated by the finetuned BERT (Bidirectional Encoder Representations from Transformers) models and the K-Means clustering method has been investigated. To show how the BERT model can capture the knowledge in specific domains like engineering design and what it can produce after being finetuned based on domain-specific datasets, several BERT models are trained, and the sentence embeddings extracted from the finetuned models are used to generate summaries of a set of papers. Different evaluation methods are then applied to measure the quality of summarization results. Both the automatic evaluation method like Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and the statistical evaluation method are used for the comparison study. The results indicate that the BERT model finetuned with a larger dataset can generate summaries with more domain terminologies than the pretrained BERT model. Moreover, the summaries generated by BERT models have more contents overlapping with original documents than those obtained through other popular non-BERT-based models. It can be concluded that the contextualized representations generated by BERT-based models can capture information in text and have better performance in applications like text summarizations after being trained by domain-specific datasets.
在这项研究中，研究了由微调的BERT (Bidirectional Encoder Representations from Transformers)模型和K-Means聚类方法生成的句子嵌入的提取摘要。为了展示BERT模型如何捕获特定领域的知识，比如工程设计，以及基于特定领域的数据集进行微调后它能产生什么，我们训练了几个BERT模型，并使用从微调模型中提取的句子嵌入来生成一组论文的摘要。然后采用不同的评价方法来衡量总结结果的质量。对比研究采用了以回忆为导向的登记评价替补(ROUGE)等自动评价方法和统计评价方法。结果表明，与预训练的BERT模型相比，经更大数据集微调后的BERT模型可以生成包含更多领域术语的摘要。此外，与其他流行的非BERT模型相比，BERT模型生成的摘要与原始文档重叠的内容更多。可以得出结论，基于bert的模型生成的上下文化表示经过特定领域的数据集训练后，可以捕获文本中的信息，并且在文本摘要等应用中具有更好的性能。
{"title":"Engineering Document Summarization Using Sentence Representations Generated by Bidirectional Language Model","authors":"Y. Qiu, Yan Jin","doi":"10.1115/detc2021-70866","DOIUrl":"https://doi.org/10.1115/detc2021-70866","url":null,"abstract":"\u0000 In this study, the extractive summarization using sentence embeddings generated by the finetuned BERT (Bidirectional Encoder Representations from Transformers) models and the K-Means clustering method has been investigated. To show how the BERT model can capture the knowledge in specific domains like engineering design and what it can produce after being finetuned based on domain-specific datasets, several BERT models are trained, and the sentence embeddings extracted from the finetuned models are used to generate summaries of a set of papers. Different evaluation methods are then applied to measure the quality of summarization results. Both the automatic evaluation method like Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and the statistical evaluation method are used for the comparison study. The results indicate that the BERT model finetuned with a larger dataset can generate summaries with more domain terminologies than the pretrained BERT model. Moreover, the summaries generated by BERT models have more contents overlapping with original documents than those obtained through other popular non-BERT-based models. It can be concluded that the contextualized representations generated by BERT-based models can capture information in text and have better performance in applications like text summarizations after being trained by domain-specific datasets.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"298 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79638391","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}
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}
The emotional needs for products have increased significantly with the recent improvements in living standards. Attribute evaluation forms the core of Kansei engineering in emotion-oriented products, and is practically quite subjective in nature. Essentially, attribute evaluation is a fuzzy classification task, whose quantitative results change slightly with statistical time and statistical objects, making it difficult to accurately describe using standard mathematical models. In this paper, we propose a novel deep-learning-assisted fuzzy attribute-evaluation (DLFAE) method, which could generate quantitative evaluation results. In comparison to existing methods, the proposed method combines subjective evaluation with convolutional neural networks, which facilitates the generation of quantitative evaluation results. Additionally, this strategy has better transferability for different situations, increasing its versatility and applicability. This, in turn, reduces the computational burden of evaluation and improves operational efficiency.
{"title":"Fuzzy Evaluation of Kansei Attributes Using Convolutional Neural Networks","authors":"Jiang-Shu Wei, Kai Zhang, Wu Zhao, Xin Guo","doi":"10.1115/detc2021-69567","DOIUrl":"https://doi.org/10.1115/detc2021-69567","url":null,"abstract":"\u0000 The emotional needs for products have increased significantly with the recent improvements in living standards. Attribute evaluation forms the core of Kansei engineering in emotion-oriented products, and is practically quite subjective in nature. Essentially, attribute evaluation is a fuzzy classification task, whose quantitative results change slightly with statistical time and statistical objects, making it difficult to accurately describe using standard mathematical models. In this paper, we propose a novel deep-learning-assisted fuzzy attribute-evaluation (DLFAE) method, which could generate quantitative evaluation results. In comparison to existing methods, the proposed method combines subjective evaluation with convolutional neural networks, which facilitates the generation of quantitative evaluation results. Additionally, this strategy has better transferability for different situations, increasing its versatility and applicability. This, in turn, reduces the computational burden of evaluation and improves operational efficiency.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74536634","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}
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}
Automotive body structure design is critical to achieve lightweight and crash worthiness based on engineers’ experience. In the current design process, it frequently occurs that designers use a previous generation design to evolve the latest designs to meet certain targets. However, in this process the possibility of adapting design ideas from other models is unlikely. The uniqueness of each design and presence of non-uniform parameters further makes it difficult to compare two or more designs and extract useful feature information. There is a need for a method that will fill the missing gap in assisting designers with better design options. This paper aims to fill this gap by introducing an innovative approach to use a non-uniform parametric study with machine learning in order to make valuable suggestions to the designer. The proposed method uses data sets produced from experiment design to reduce the number of parameters, perform parameter correlation studies and run finite element analysis (FEA), for a given set of loads. The response data generated from this FEA is then used in a machine learning algorithm to make predictions on the ideal features to be used in the design. The method can be applied to any component that has a feature-based parametric design.
{"title":"Intelligent Design Prediction Aided by Non-Uniform Parametric Study and Machine Learning in Feature Based Product Development","authors":"Satchit Ramnath, Jiachen Ma, J. Shah, D. Detwiler","doi":"10.1115/detc2021-67923","DOIUrl":"https://doi.org/10.1115/detc2021-67923","url":null,"abstract":"\u0000 Automotive body structure design is critical to achieve lightweight and crash worthiness based on engineers’ experience. In the current design process, it frequently occurs that designers use a previous generation design to evolve the latest designs to meet certain targets. However, in this process the possibility of adapting design ideas from other models is unlikely. The uniqueness of each design and presence of non-uniform parameters further makes it difficult to compare two or more designs and extract useful feature information. There is a need for a method that will fill the missing gap in assisting designers with better design options. This paper aims to fill this gap by introducing an innovative approach to use a non-uniform parametric study with machine learning in order to make valuable suggestions to the designer. The proposed method uses data sets produced from experiment design to reduce the number of parameters, perform parameter correlation studies and run finite element analysis (FEA), for a given set of loads. The response data generated from this FEA is then used in a machine learning algorithm to make predictions on the ideal features to be used in the design. The method can be applied to any component that has a feature-based parametric design.","PeriodicalId":23602,"journal":{"name":"Volume 2: 41st Computers and Information in Engineering Conference (CIE)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85445946","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}
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}
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