W. Bernstein, Andrew Bowman, R. Durscher, A. Gillman, S. Donegan
Extended reality (XR) technologies have realized significant value for design, manufacturing, and sustainment processes. However, Industrial XR, or XR implemented within industrial applications, suffers from scalability and flexibility challenges due to fundamental gaps with interoperability between data, models, and platforms. Though there has been a number of recent efforts to improve the interoperability of industrial XR technologies, progress has been hindered by an innate separation between the domain-specific models (e.g., manufacturing execution data, material specifications, and product manufacturing information) with XR (often-standard) processes (e.g., multi-scale spatial representations and data formats optimized for run-time presentation). In this paper, we elaborate on promising research directions and opportunities around which the manufacturing and visualization academic community can rally. To establish such research directions, we (1) conducted a meta-review on well-established state-of-the-art review articles that have already presented in-depth surveys on application areas for industrial XR, such as maintenance, assembly and inspection and (2) mapped those findings to publicly published priorities from across the US Department of Defense. We hope that our presented research agenda will spur interdisciplinary work across academic silos, i.e., manufacturing and visualization communities, and engages either community within work groups led by the other, e.g., within standards development organizations.
{"title":"Towards Data and Model Interoperability for Industrial Extended Reality in Manufacturing","authors":"W. Bernstein, Andrew Bowman, R. Durscher, A. Gillman, S. Donegan","doi":"10.1115/1.4062328","DOIUrl":"https://doi.org/10.1115/1.4062328","url":null,"abstract":"\u0000 Extended reality (XR) technologies have realized significant value for design, manufacturing, and sustainment processes. However, Industrial XR, or XR implemented within industrial applications, suffers from scalability and flexibility challenges due to fundamental gaps with interoperability between data, models, and platforms. Though there has been a number of recent efforts to improve the interoperability of industrial XR technologies, progress has been hindered by an innate separation between the domain-specific models (e.g., manufacturing execution data, material specifications, and product manufacturing information) with XR (often-standard) processes (e.g., multi-scale spatial representations and data formats optimized for run-time presentation). In this paper, we elaborate on promising research directions and opportunities around which the manufacturing and visualization academic community can rally. To establish such research directions, we (1) conducted a meta-review on well-established state-of-the-art review articles that have already presented in-depth surveys on application areas for industrial XR, such as maintenance, assembly and inspection and (2) mapped those findings to publicly published priorities from across the US Department of Defense. We hope that our presented research agenda will spur interdisciplinary work across academic silos, i.e., manufacturing and visualization communities, and engages either community within work groups led by the other, e.g., within standards development organizations.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"5 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87669241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Megacasting is a new concept in the automotive industry. A large number of sheet metal parts will be replaced with one large aluminum casting, i.e. a megacasting. This helps to reduce weight, opens up for larger design flexibility, allows for a more circular production, and takes away a large number of assembly steps in the production process. However, there are also challenges related to the use of megacastings. This position paper outlines challenges associated with the geometrical quality of the final product. It covers robust design and tolerancing in early product development phases as well as inspection preparation during pre-production and digital twin set-up during full production to ensure the geometrical quality of a product containing a megacasting. Simulations of both part level and assembly level deviation and variation are discussed. The paper outlines a geometry assurance process for products containing megacastings in the automotive industry, and what research challenges that are the most important ones to address in this area. It is concluded that computer-aided tolerancing tools must be able to predict the dimensional effects from joining methods such as flow drill fasteners or self-pierced riveting, to use casting simulation as input, and to handle combinations of solid and surface meshes. Furthermore, there might be a need for adjustments to the joining process based on digital twins to achieve proper quality at a reasonable price. Experiences in using megacastings in the body-in-white are lacking and a fast learning curve is required.
{"title":"Challenges in Geometry Assurance of Megacasting in the Automotive Industry","authors":"Kristina Wärmefjord, Josefin Hansen, R. Söderberg","doi":"10.1115/1.4062269","DOIUrl":"https://doi.org/10.1115/1.4062269","url":null,"abstract":"\u0000 Megacasting is a new concept in the automotive industry. A large number of sheet metal parts will be replaced with one large aluminum casting, i.e. a megacasting. This helps to reduce weight, opens up for larger design flexibility, allows for a more circular production, and takes away a large number of assembly steps in the production process. However, there are also challenges related to the use of megacastings. This position paper outlines challenges associated with the geometrical quality of the final product. It covers robust design and tolerancing in early product development phases as well as inspection preparation during pre-production and digital twin set-up during full production to ensure the geometrical quality of a product containing a megacasting. Simulations of both part level and assembly level deviation and variation are discussed. The paper outlines a geometry assurance process for products containing megacastings in the automotive industry, and what research challenges that are the most important ones to address in this area. It is concluded that computer-aided tolerancing tools must be able to predict the dimensional effects from joining methods such as flow drill fasteners or self-pierced riveting, to use casting simulation as input, and to handle combinations of solid and surface meshes. Furthermore, there might be a need for adjustments to the joining process based on digital twins to achieve proper quality at a reasonable price. Experiences in using megacastings in the body-in-white are lacking and a fast learning curve is required.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"16 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73068823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances in the digitization of manufacturing have prompted ASME and International Organization for Standardization (ISO) standards committees to reexamine the definition of datums. Any new definition of datums considered by the standards committees should cover all datum feature types used in design and support both traditional metrological methods and new digital measurement techniques. This is a challenging task that requires some careful compromise. This paper describes and analyzes various alternatives considered by the standards committees. Among them is a new mathematical definition of datums based on constrained least-squares fitting. It seems to provide the best compromise and has the potential to support advanced manufacturing that is increasingly dependent on digital technologies.
{"title":"Toward a New Mathematical Definition of Datums in Standards to Support Advanced Manufacturing.","authors":"Craig M Shakarji, Vijay Srinivasan","doi":"10.1115/1.4054304","DOIUrl":"10.1115/1.4054304","url":null,"abstract":"<p><p>Recent advances in the digitization of manufacturing have prompted ASME and International Organization for Standardization (ISO) standards committees to reexamine the definition of datums. Any new definition of datums considered by the standards committees should cover all datum feature types used in design and support both traditional metrological methods and new digital measurement techniques. This is a challenging task that requires some careful compromise. This paper describes and analyzes various alternatives considered by the standards committees. Among them is a new mathematical definition of datums based on constrained least-squares fitting. It seems to provide the best compromise and has the potential to support advanced manufacturing that is increasingly dependent on digital technologies.</p>","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"23 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Given a part design, the task of manufacturing process classification identifies an appropriate manufacturing process to fabricate it. Our previous research proposed a large dataset for manufacturing process classification and achieved accurate classification results based on a combination of a convolutional neural network (CNN) and the heat kernel signature for triangle meshes. In this paper, we constructed a classification method based on rotation invariant shape descriptors and a neural network, and it achieved better accuracy than all previous methods. This method uses a point cloud part representation, in contrast to the triangle mesh representation used in our previous work. The first step extracted rotation invariant features consisting of a set of distances between points in the point cloud. Then, the extracted shape descriptors were fed into a CNN for the classification of manufacturing processes. In addition, we provide two visualization methods for interpreting the intermediate layers of the neural network. Last, the performance of the method was tested on some ambiguous examples and their performances were consistent with expectations. In this paper, we have considered only shape information, while non-shape information like materials and tolerances were ignored. Additionally, only parts that require one manufacturing process were considered in this research. Our work demonstrates that part shape attributes alone are adequate for discriminating between different manufacturing processes considered.
{"title":"Manufacturing Process Classification Based on Distance Rotationally Invariant Convolutions","authors":"Zhichao Wang, David Rosen","doi":"10.1115/1.4056806","DOIUrl":"https://doi.org/10.1115/1.4056806","url":null,"abstract":"Abstract Given a part design, the task of manufacturing process classification identifies an appropriate manufacturing process to fabricate it. Our previous research proposed a large dataset for manufacturing process classification and achieved accurate classification results based on a combination of a convolutional neural network (CNN) and the heat kernel signature for triangle meshes. In this paper, we constructed a classification method based on rotation invariant shape descriptors and a neural network, and it achieved better accuracy than all previous methods. This method uses a point cloud part representation, in contrast to the triangle mesh representation used in our previous work. The first step extracted rotation invariant features consisting of a set of distances between points in the point cloud. Then, the extracted shape descriptors were fed into a CNN for the classification of manufacturing processes. In addition, we provide two visualization methods for interpreting the intermediate layers of the neural network. Last, the performance of the method was tested on some ambiguous examples and their performances were consistent with expectations. In this paper, we have considered only shape information, while non-shape information like materials and tolerances were ignored. Additionally, only parts that require one manufacturing process were considered in this research. Our work demonstrates that part shape attributes alone are adequate for discriminating between different manufacturing processes considered.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"380 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135469256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wentai Zhang, Joe Joseph, Quan Chen, Can Koz, Liuyue Xie, Amit Regmi, Soji Yamakawa, T. Furuhata, Kenji Shimada, L. Kara
We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Our method is based on the randomization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizability of the machine learning models to unseen drawings.
{"title":"A Data Augmentation Method for Data-Driven Component Segmentation of Engineering Drawings","authors":"Wentai Zhang, Joe Joseph, Quan Chen, Can Koz, Liuyue Xie, Amit Regmi, Soji Yamakawa, T. Furuhata, Kenji Shimada, L. Kara","doi":"10.1115/1.4062233","DOIUrl":"https://doi.org/10.1115/1.4062233","url":null,"abstract":"\u0000 We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Our method is based on the randomization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizability of the machine learning models to unseen drawings.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"119 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81558278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Vyas, Ting-Ju Chen, Jay Woodward, Vinayak R. Krishnamurthy
We investigate speech-based input as a means to enable reflective thinking for younger individuals (middle - and high-school students) during design iterations. Verbalization offers a unique way to externalize ideas in early design and could therefore lead to new pathways for exploration and iteration, especially for K-12 students who possess the creative potential but are not technically trained in the design process. Interactive design systems, however, by-and-large utilize sketching, multi-touch, and gestural inputs. As a result, (1) there is little know-how regarding how to operationalize verbal inputs as a meaningful way to facilitate idea exploration and (2) there is little fundamental understanding of the underlying cognitive mechanisms for iteration through verbal communication. We take the initial steps towards these gaps by first designing and implementing the ShapOrator interface that takes verbal descriptions of geometric parameters (shape, size, instances) in a semi-natural language form and determines the appropriate transformations to a given design artifact modeled as a shape assembly. Using ShapOrator as our experimental setup we conducted an in-depth observational study on 10 middle - and high-school students tasked with designing spaceships. Our study revealed that participants were able to create a variety of designs while associating functional and topical contexts to their spaceships throughout the design iteration process.
{"title":"Reflect-Express-Transform: Investigating Speech-Based Iterative Digital Design for Young Designers","authors":"S. Vyas, Ting-Ju Chen, Jay Woodward, Vinayak R. Krishnamurthy","doi":"10.1115/1.4062230","DOIUrl":"https://doi.org/10.1115/1.4062230","url":null,"abstract":"\u0000 We investigate speech-based input as a means to enable reflective thinking for younger individuals (middle - and high-school students) during design iterations. Verbalization offers a unique way to externalize ideas in early design and could therefore lead to new pathways for exploration and iteration, especially for K-12 students who possess the creative potential but are not technically trained in the design process. Interactive design systems, however, by-and-large utilize sketching, multi-touch, and gestural inputs. As a result, (1) there is little know-how regarding how to operationalize verbal inputs as a meaningful way to facilitate idea exploration and (2) there is little fundamental understanding of the underlying cognitive mechanisms for iteration through verbal communication. We take the initial steps towards these gaps by first designing and implementing the ShapOrator interface that takes verbal descriptions of geometric parameters (shape, size, instances) in a semi-natural language form and determines the appropriate transformations to a given design artifact modeled as a shape assembly. Using ShapOrator as our experimental setup we conducted an in-depth observational study on 10 middle - and high-school students tasked with designing spaceships. Our study revealed that participants were able to create a variety of designs while associating functional and topical contexts to their spaceships throughout the design iteration process.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83104827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Creativity is a fundamental feature of human intelligence. However, achieving creativity is often considered a challenging task, particularly in design. In recent years, using computational machines to support people in creative activities in design, such as idea generation and evaluation, has become a popular research topic. Although there exist many creativity support tools, few of them could produce creative solutions in a direct manner, but produce stimuli instead. DALL·E is currently the most advanced computational model that could generate creative ideas in pictorial formats based on textual descriptions. This study conducts a Turing test, a computational test and an expert test to evaluate DALL·E's capability in achieving combinational creativity comparing with human designers. The results reveal that DALL·E could achieve combinational creativity at a similar level to novice designers and indicate the differences between computer and human creativity.
{"title":"A Comparison Study of Human and Machine-Generated Creativity","authors":"Liuqing Chen, Lingyun Sun, Ji Han","doi":"10.1115/1.4062232","DOIUrl":"https://doi.org/10.1115/1.4062232","url":null,"abstract":"\u0000 Creativity is a fundamental feature of human intelligence. However, achieving creativity is often considered a challenging task, particularly in design. In recent years, using computational machines to support people in creative activities in design, such as idea generation and evaluation, has become a popular research topic. Although there exist many creativity support tools, few of them could produce creative solutions in a direct manner, but produce stimuli instead. DALL·E is currently the most advanced computational model that could generate creative ideas in pictorial formats based on textual descriptions. This study conducts a Turing test, a computational test and an expert test to evaluate DALL·E's capability in achieving combinational creativity comparing with human designers. The results reveal that DALL·E could achieve combinational creativity at a similar level to novice designers and indicate the differences between computer and human creativity.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"16 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87783579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-22DOI: 10.48550/arXiv.2303.12261
Phong C. H. Nguyen, Joseph B. Choi, H. Udaykumar, Stephen Baek
Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space. Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or augment traditional numerical methods. Pioneering work has demonstrated that ML provides solutions to governing systems of equations with comparable accuracy to those obtained using direct numerical methods, but with significantly faster computational speed. These high-speed, high-fidelity estimations can facilitate the solving of complex multiscale systems by providing a better initial solution to traditional solvers. This paper provides a perspective on the opportunities and challenges of using ML for complex multiscale modeling and simulation. We first outline the current state-of-the-art ML approaches for simulating multiscale systems and highlight some of the landmark developments. Next, we discuss current challenges for ML in multiscale computational modeling, such as the data/discretization dependence, interpretability, data sharing and collaborative platform development. Finally, we suggest several potential research directions for the future.
{"title":"Challenges and opportunities for machine learning in multiscale computational modeling","authors":"Phong C. H. Nguyen, Joseph B. Choi, H. Udaykumar, Stephen Baek","doi":"10.48550/arXiv.2303.12261","DOIUrl":"https://doi.org/10.48550/arXiv.2303.12261","url":null,"abstract":"\u0000 Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space. Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or augment traditional numerical methods. Pioneering work has demonstrated that ML provides solutions to governing systems of equations with comparable accuracy to those obtained using direct numerical methods, but with significantly faster computational speed. These high-speed, high-fidelity estimations can facilitate the solving of complex multiscale systems by providing a better initial solution to traditional solvers. This paper provides a perspective on the opportunities and challenges of using ML for complex multiscale modeling and simulation. We first outline the current state-of-the-art ML approaches for simulating multiscale systems and highlight some of the landmark developments. Next, we discuss current challenges for ML in multiscale computational modeling, such as the data/discretization dependence, interpretability, data sharing and collaborative platform development. Finally, we suggest several potential research directions for the future.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"15 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76866580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The unstructured data available on the websites of manufacturing suppliers and contractors can provide valuable insights into their technological and organizational capabilities. However, since the capability data are often represented in an unstructured and informal fashion using natural language text, it is not easy to efficiently search and analyze the capability data and learn from it. The objective of this work is to propose framework to enable automated classification and ranking of suppliers based on their online capability descriptions in the context of a supplier search and discovery use case. The proposed text analytics methods used in this work are supported by a formal thesaurus that uses SKOS (Simple Knowledge Organization System) that provides lexical and structural semantics. Normalized Google Distance (NGD) is used as the metric for measuring the relatedness of terms when ranking suppliers based on their similarities with the queried capabilities. The proposed framework is validated experimentally using a hypothetical supplier search scenario. The results indicate that the generated ranked list is highly correlated with human judgment, especially when the search space is partitioned into multiple classes of suppliers with distinct capabilities. However, the correlation decreases when multiple overlapping classes of suppliers are merged together to form a heterogenous search space. The proposed framework can support supplier screening and discovery solutions by improving the precision, reliability, and intelligence of their underlying search engines.
{"title":"A Text Analytics Framework for Supplier Capability Scoring Supported by Normalized Google Distance and Semantic Similarity Measurement Methods","authors":"Kimia Zandbiglari, F. Ameri, Mohammad Javadi","doi":"10.1115/1.4062173","DOIUrl":"https://doi.org/10.1115/1.4062173","url":null,"abstract":"\u0000 The unstructured data available on the websites of manufacturing suppliers and contractors can provide valuable insights into their technological and organizational capabilities. However, since the capability data are often represented in an unstructured and informal fashion using natural language text, it is not easy to efficiently search and analyze the capability data and learn from it. The objective of this work is to propose framework to enable automated classification and ranking of suppliers based on their online capability descriptions in the context of a supplier search and discovery use case. The proposed text analytics methods used in this work are supported by a formal thesaurus that uses SKOS (Simple Knowledge Organization System) that provides lexical and structural semantics. Normalized Google Distance (NGD) is used as the metric for measuring the relatedness of terms when ranking suppliers based on their similarities with the queried capabilities. The proposed framework is validated experimentally using a hypothetical supplier search scenario. The results indicate that the generated ranked list is highly correlated with human judgment, especially when the search space is partitioned into multiple classes of suppliers with distinct capabilities. However, the correlation decreases when multiple overlapping classes of suppliers are merged together to form a heterogenous search space. The proposed framework can support supplier screening and discovery solutions by improving the precision, reliability, and intelligence of their underlying search engines.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"4 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74275834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, the particle finite element method (PFEM) is extended to simulate additive manufacturing processes in a variety of different complicated geometries. A three-dimensional α-shape approach is used to carry out the material addition procedure. It overcomes the limitation of merely employing the traditional element birth and death technique and reduces the degrees of freedom compared to this technique. Furthermore, numerical examples are used to evaluate and demonstrate the applicability of the PFEM method for additive manufacturing within the framework of a weakly coupled thermoelasticity formulation. During additive manufacturing operations, deflections, stresses, and temperature are computed using a user defined implementation in FEniCS.
{"title":"A Particle Finite Element Method for Additive Manufacturing Simulations","authors":"Daobo Zhang, J. M. Rodriguez, X. Ye, R. Müller","doi":"10.1115/1.4062143","DOIUrl":"https://doi.org/10.1115/1.4062143","url":null,"abstract":"\u0000 In this work, the particle finite element method (PFEM) is extended to simulate additive manufacturing processes in a variety of different complicated geometries. A three-dimensional α-shape approach is used to carry out the material addition procedure. It overcomes the limitation of merely employing the traditional element birth and death technique and reduces the degrees of freedom compared to this technique. Furthermore, numerical examples are used to evaluate and demonstrate the applicability of the PFEM method for additive manufacturing within the framework of a weakly coupled thermoelasticity formulation. During additive manufacturing operations, deflections, stresses, and temperature are computed using a user defined implementation in FEniCS.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"15 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90975320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","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：