Neelotpal Dutta, Tianyu Zhang, Guoxin Fang, Ismail E. Yigit, Charlie C.L. Wang
Abstract This paper presents an easy-to-control volume peeling method for multi-axis machining based on the computation taken on vector fields. The current scalar field based methods are not flexible and the vector-field based methods do not guarantee the satisfaction of the constraints in the final results. We first conduct an optimization formulation to compute an initial vector field that is well aligned with those anchor vectors specified by users according to different manufacturing requirements. The vector field is further optimized to be an irrotational field so that it can be completely realized by a scalar field's gradients. Iso-surfaces of the scalar field will be employed as the layers of working surfaces for multi-axis volume peeling in the rough machining. Algorithms are also developed to remove and process singularities of the fields. Our method has been tested on a variety of models and verified by physical experimental machining.
{"title":"Vector Field Based Volume Peeling for Multi-Axis Machining","authors":"Neelotpal Dutta, Tianyu Zhang, Guoxin Fang, Ismail E. Yigit, Charlie C.L. Wang","doi":"10.1115/1.4063861","DOIUrl":"https://doi.org/10.1115/1.4063861","url":null,"abstract":"Abstract This paper presents an easy-to-control volume peeling method for multi-axis machining based on the computation taken on vector fields. The current scalar field based methods are not flexible and the vector-field based methods do not guarantee the satisfaction of the constraints in the final results. We first conduct an optimization formulation to compute an initial vector field that is well aligned with those anchor vectors specified by users according to different manufacturing requirements. The vector field is further optimized to be an irrotational field so that it can be completely realized by a scalar field's gradients. Iso-surfaces of the scalar field will be employed as the layers of working surfaces for multi-axis volume peeling in the rough machining. Algorithms are also developed to remove and process singularities of the fields. Our method has been tested on a variety of models and verified by physical experimental machining.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135569844","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}
Janet K. Allen, Ehsan T Esfahani, Satyandra K. Gupta, Balan Gurumoorthy, Bin He, Ying Liu, John Michopoulos, Jitesh H. Panchal, Anurag Purwar, Kristina Wärmefjord
Recent advances in computing and information science such as artificial intelligence (AI), machine learning (ML), edge computing, cloud computing, metacomputing, and quantum computing are creating new computing paradigms. These advances are providing new opportunities for new research and application development. For instance, the adoption of Industry 4.0 enabled by AI/ML is fundamentally changing how products are designed, manufactured, maintained, and recycled. It enables consideration of all aspects of the product life cycle and realizing sustainable designs and helps us in achieving carbon neutrality. Intelligent machines such as robots and autonomous vehicles are revolutionizing human–machine interactions and increasing digitalization in the manufacturing and transportation industries. It is important for the Journal of Computing and Information Science in Engineering (JCISE) community to identify challenges and opportunities in these emerging areas and inspire new researchers to join the field and become a part of the community. This Special Issue consists of 19 position papers that span a wide variety of topics of interest to the JCISE community. These position papers identify challenges and opportunities, outline new areas of research, and point out new applications that will be enabled by advances in this field.
{"title":"Special Issue: Challenges and Opportunities in Computing Research to Enable Next-Generation Engineering Applications","authors":"Janet K. Allen, Ehsan T Esfahani, Satyandra K. Gupta, Balan Gurumoorthy, Bin He, Ying Liu, John Michopoulos, Jitesh H. Panchal, Anurag Purwar, Kristina Wärmefjord","doi":"10.1115/1.4063437","DOIUrl":"https://doi.org/10.1115/1.4063437","url":null,"abstract":"Recent advances in computing and information science such as artificial intelligence (AI), machine learning (ML), edge computing, cloud computing, metacomputing, and quantum computing are creating new computing paradigms. These advances are providing new opportunities for new research and application development. For instance, the adoption of Industry 4.0 enabled by AI/ML is fundamentally changing how products are designed, manufactured, maintained, and recycled. It enables consideration of all aspects of the product life cycle and realizing sustainable designs and helps us in achieving carbon neutrality. Intelligent machines such as robots and autonomous vehicles are revolutionizing human–machine interactions and increasing digitalization in the manufacturing and transportation industries. It is important for the Journal of Computing and Information Science in Engineering (JCISE) community to identify challenges and opportunities in these emerging areas and inspire new researchers to join the field and become a part of the community. This Special Issue consists of 19 position papers that span a wide variety of topics of interest to the JCISE community. These position papers identify challenges and opportunities, outline new areas of research, and point out new applications that will be enabled by advances in this field.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135667429","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}
Abstract Prototyping use cases for augmented reality (AR) applications can be beneficial to elicit the functional requirements of the features early-on, to drive the subsequent development in a goal-oriented manner. Doing so would require designers to identify the goal-oriented interactions and map the associations between those interactions in a spatio-temporal context. Pertaining to the multiple scenarios that may result from the mapping, and the embodied nature of the interaction components, recent AR prototyping methods lack the support to adequately capture and communicate the intent of designers and stakeholders during this process. We present ImpersonatAR, a mobile-device-based prototyping tool that utilizes embodied demonstrations in the augmented environment to support prototyping and evaluation of multi-scenario AR use cases. The approach uses: (1) capturing events or steps in the form of embodied demonstrations using avatars and 3D animations, (2) organizing events and steps to compose multi-scenario experience, and finally (3) allowing stakeholders to explore the scenarios through interactive role-play with the prototypes. We conducted a user study with ten participants to prototype use cases using ImpersonatAR from two different AR application features. Results validated that ImpersonatAR promotes exploration and evaluation of diverse design possibilities of multi-scenario AR use cases through embodied representations of the different scenarios.
{"title":"ImpersonatAR: Using Embodied Authoring and Evaluation to Prototype Multi-Scenario Use cases for Augmented Reality Applications","authors":"Meng-Han Wu, Ananya Ipsita, Gaoping Huang, Karthik Ramani, Alexander J Quinn","doi":"10.1115/1.4063558","DOIUrl":"https://doi.org/10.1115/1.4063558","url":null,"abstract":"Abstract Prototyping use cases for augmented reality (AR) applications can be beneficial to elicit the functional requirements of the features early-on, to drive the subsequent development in a goal-oriented manner. Doing so would require designers to identify the goal-oriented interactions and map the associations between those interactions in a spatio-temporal context. Pertaining to the multiple scenarios that may result from the mapping, and the embodied nature of the interaction components, recent AR prototyping methods lack the support to adequately capture and communicate the intent of designers and stakeholders during this process. We present ImpersonatAR, a mobile-device-based prototyping tool that utilizes embodied demonstrations in the augmented environment to support prototyping and evaluation of multi-scenario AR use cases. The approach uses: (1) capturing events or steps in the form of embodied demonstrations using avatars and 3D animations, (2) organizing events and steps to compose multi-scenario experience, and finally (3) allowing stakeholders to explore the scenarios through interactive role-play with the prototypes. We conducted a user study with ten participants to prototype use cases using ImpersonatAR from two different AR application features. Results validated that ImpersonatAR promotes exploration and evaluation of diverse design possibilities of multi-scenario AR use cases through embodied representations of the different scenarios.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"9 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135667118","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}
Zhao Lun, Sen Lin, YunLong Pang, HaiBo Wang, Zeshan Abbas, ZiXin Guo, XiaoLe Huo, Seng Wang
Abstract The self-pierce riveting process for alloy materials has a wide range of applications in the automotive manufacturing industry. This will not only affect the operation performance, but also cause accidents in severe cases when there are defects in the riveted parts. A deep learning detection model is proposed that integrates atrous convolution and dynamic convolution to identify defects of self-piercing riveting parts efficiently to overcome the problem in quality inspection after the body self-piercing riveting process. Firstly, a backbone network for extracting riveting defect features is constructed based on the ResNet network. Secondly, the center area of each riveting defect is located preferentially by the center point detection algorithm. Finally, the bounding box of riveting defects is regressed to achieve defect detection based on this central region. Among them, atrous convolution is used in the external network to increase the receptive field of the model, which combined with an active convolution so that a dynamic atrous convolution module is designed. This module is used to enhance the correlation between feature points of individual pixel in the image, which helps to identify defects with incomplete image edges and suppress background interference. Ablation experiments show that the proposed method achieves the highest accuracy of 95.7%, which is 3.6% higher than the original method. It is found that the proposed method is less affected by the background interference from the qualitative comparison. Moreover, it can also effectively identify the riveting defects on the surface of each area.
{"title":"A deep convolutional neural network-based method for self-piercing rivet joint defect detection","authors":"Zhao Lun, Sen Lin, YunLong Pang, HaiBo Wang, Zeshan Abbas, ZiXin Guo, XiaoLe Huo, Seng Wang","doi":"10.1115/1.4063748","DOIUrl":"https://doi.org/10.1115/1.4063748","url":null,"abstract":"Abstract The self-pierce riveting process for alloy materials has a wide range of applications in the automotive manufacturing industry. This will not only affect the operation performance, but also cause accidents in severe cases when there are defects in the riveted parts. A deep learning detection model is proposed that integrates atrous convolution and dynamic convolution to identify defects of self-piercing riveting parts efficiently to overcome the problem in quality inspection after the body self-piercing riveting process. Firstly, a backbone network for extracting riveting defect features is constructed based on the ResNet network. Secondly, the center area of each riveting defect is located preferentially by the center point detection algorithm. Finally, the bounding box of riveting defects is regressed to achieve defect detection based on this central region. Among them, atrous convolution is used in the external network to increase the receptive field of the model, which combined with an active convolution so that a dynamic atrous convolution module is designed. This module is used to enhance the correlation between feature points of individual pixel in the image, which helps to identify defects with incomplete image edges and suppress background interference. Ablation experiments show that the proposed method achieves the highest accuracy of 95.7%, which is 3.6% higher than the original method. It is found that the proposed method is less affected by the background interference from the qualitative comparison. Moreover, it can also effectively identify the riveting defects on the surface of each area.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969737","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}
Abstract The rapid advance in sensing technology has expedited data-driven innovation in manufacturing by allowing the collection of large amounts of data from factories. Big data provides an unprecedented opportunity for smart decision-making in the manufacturing process. However, they also attract cyberattacks due to the value of sensitive information. A cyberattack on manufacturing big data can lead to a significant loss of profits and unprecedented business disruption. Moreover, the increasing use of artificial intelligence (AI) in smart factories means that manufacturing equipment is now vulnerable to cyberattacks, posing a critical threat to smart manufacturing systems. Therefore, there is an urgent need to develop AI models that incorporate privacy-preserving methods to protect sensitive information implicit in the models against model inversion attacks. Hence this paper presents the development of a new approach called Mosaic Neuron Perturbation (MNP) to preserve latent information in the framework of the AI model, ensuring differential privacy requirements while mitigating the risk of model inversion attacks. MNP is flexible to implement into AI models, enabling a trade-off between model performance and robustness against cyberattacks while being highly scalable for large-scale computing. Experimental results, based on real-world manufacturing data collected from the CNC turning process, demonstrate that the proposed method significantly improves the prevention of inversion attacks while maintaining high prediction performance. The MNP method shows strong potential for making manufacturing systems both smart and secure by addressing the risk of data breaches while preserving the quality of AI models.
{"title":"Privacy-preserving Neural Networks for Smart Manufacturing","authors":"Hankang Lee, Daniel Finke, Hui Yang","doi":"10.1115/1.4063728","DOIUrl":"https://doi.org/10.1115/1.4063728","url":null,"abstract":"Abstract The rapid advance in sensing technology has expedited data-driven innovation in manufacturing by allowing the collection of large amounts of data from factories. Big data provides an unprecedented opportunity for smart decision-making in the manufacturing process. However, they also attract cyberattacks due to the value of sensitive information. A cyberattack on manufacturing big data can lead to a significant loss of profits and unprecedented business disruption. Moreover, the increasing use of artificial intelligence (AI) in smart factories means that manufacturing equipment is now vulnerable to cyberattacks, posing a critical threat to smart manufacturing systems. Therefore, there is an urgent need to develop AI models that incorporate privacy-preserving methods to protect sensitive information implicit in the models against model inversion attacks. Hence this paper presents the development of a new approach called Mosaic Neuron Perturbation (MNP) to preserve latent information in the framework of the AI model, ensuring differential privacy requirements while mitigating the risk of model inversion attacks. MNP is flexible to implement into AI models, enabling a trade-off between model performance and robustness against cyberattacks while being highly scalable for large-scale computing. Experimental results, based on real-world manufacturing data collected from the CNC turning process, demonstrate that the proposed method significantly improves the prevention of inversion attacks while maintaining high prediction performance. The MNP method shows strong potential for making manufacturing systems both smart and secure by addressing the risk of data breaches while preserving the quality of AI models.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296155","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}
Haedong Kim, Tyler Hartleb, Khalid Bello, Faisal Aqlan, Richard Zhao, Hui Yang
Abstract Engineering is an inherently creative and collaborative endeavor to solve real-world problems, in which collaborative problem solving (CPS) is considered one of the most critical professional skills. Hands-on practices and assessment methods are essential to promote deeper learning and foster the development of professional skills. However, most existing approaches are based on out-of-process procedures such as surveys, tests, or interviews that measure the effectiveness of learning activity in an aggregated way. It is desirable to quantify CPS dynamics during the learning process. Advancements in virtual reality (VR) provide great opportunities to realize digital learning environments to facilitate a learning-by-doing curriculum. In addition, sensors in VR systems allow us to collect in-process user behavioral data. This paper presents a multiplayer VR manufacturing simulation game for virtual hands-on learning experiences, as well as a behavioral modeling method for monitoring the CPS skills of participants. First, we developed the Virtual Learning Factory, where users play simulation games of various manufacturing paradigms. Second, we collected action logs from a sample of participants and used the same pattern to generate more data. Third, the behavioral data are modeled as dynamic networks for each player. Last, network features are calculated, and a CPS scoring method is driven from them. Experimental results show that the proposed behavioral modeling successfully captures different patterns of CPS dynamics according to manufacturing paradigms and individuals. This detailed assessment contributes to the development of appropriate student-specific interventions to improve learning outcomes.
{"title":"Behavioral Modeling of Collaborative Problem Solving in Multiplayer Virtual Reality Manufacturing Simulation Games","authors":"Haedong Kim, Tyler Hartleb, Khalid Bello, Faisal Aqlan, Richard Zhao, Hui Yang","doi":"10.1115/1.4063089","DOIUrl":"https://doi.org/10.1115/1.4063089","url":null,"abstract":"Abstract Engineering is an inherently creative and collaborative endeavor to solve real-world problems, in which collaborative problem solving (CPS) is considered one of the most critical professional skills. Hands-on practices and assessment methods are essential to promote deeper learning and foster the development of professional skills. However, most existing approaches are based on out-of-process procedures such as surveys, tests, or interviews that measure the effectiveness of learning activity in an aggregated way. It is desirable to quantify CPS dynamics during the learning process. Advancements in virtual reality (VR) provide great opportunities to realize digital learning environments to facilitate a learning-by-doing curriculum. In addition, sensors in VR systems allow us to collect in-process user behavioral data. This paper presents a multiplayer VR manufacturing simulation game for virtual hands-on learning experiences, as well as a behavioral modeling method for monitoring the CPS skills of participants. First, we developed the Virtual Learning Factory, where users play simulation games of various manufacturing paradigms. Second, we collected action logs from a sample of participants and used the same pattern to generate more data. Third, the behavioral data are modeled as dynamic networks for each player. Last, network features are calculated, and a CPS scoring method is driven from them. Experimental results show that the proposed behavioral modeling successfully captures different patterns of CPS dynamics according to manufacturing paradigms and individuals. This detailed assessment contributes to the development of appropriate student-specific interventions to improve learning outcomes.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136254878","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}
Nicholas Wu, Brendan Whalen, Ji Ma, Prasanna V. Balachandran
Abstract In this work, we develop an efficient computational framework for process space exploration in laser powder bed fusion (LPBF) based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. We employ a Bayesian approach towards inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditions and inferred laser energy absorption is found to have good correspondence to literature measurements of powder bed energy absorption using calorimetric methods. The Bayesian approach naturally enables uncertainty quantification and we demonstrate its utility by performing efficient forward propagation of uncertainties through the modified Eagar-Tsai model to obtain estimates of melt pool geometries, which we validate using out-of-sample experimental data from the literature. These melt pool predictions are then used to compute the probability of occurrence of keyhole and lack-of-fusion based defects using geometry-based criteria. This information is summarized in a probabilistic printability map. We find that the probabilistic printability map can describe the keyhole and lack of fusion behavior in experimental data used for calibration, and is capable of generalizing to wider regions of processing space. This analysis is conducted for SS316L, IN718, IN625, and Ti6Al4V using melt pool measurement data retrieved from the literature.
{"title":"Probabilistic Printability Maps for Laser Powder Bed Fusion via Functional Calibration and Uncertainty Propagation","authors":"Nicholas Wu, Brendan Whalen, Ji Ma, Prasanna V. Balachandran","doi":"10.1115/1.4063727","DOIUrl":"https://doi.org/10.1115/1.4063727","url":null,"abstract":"Abstract In this work, we develop an efficient computational framework for process space exploration in laser powder bed fusion (LPBF) based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. We employ a Bayesian approach towards inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditions and inferred laser energy absorption is found to have good correspondence to literature measurements of powder bed energy absorption using calorimetric methods. The Bayesian approach naturally enables uncertainty quantification and we demonstrate its utility by performing efficient forward propagation of uncertainties through the modified Eagar-Tsai model to obtain estimates of melt pool geometries, which we validate using out-of-sample experimental data from the literature. These melt pool predictions are then used to compute the probability of occurrence of keyhole and lack-of-fusion based defects using geometry-based criteria. This information is summarized in a probabilistic printability map. We find that the probabilistic printability map can describe the keyhole and lack of fusion behavior in experimental data used for calibration, and is capable of generalizing to wider regions of processing space. This analysis is conducted for SS316L, IN718, IN625, and Ti6Al4V using melt pool measurement data retrieved from the literature.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296040","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}
Md Habibor Rahman, Erfan Yazdandoost Hamedani, Young-Jun Son, Mohammed Shafae
Abstract Identifying, analyzing, and evaluating cybersecurity risks is essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems is lacking. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors' techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.
{"title":"Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment","authors":"Md Habibor Rahman, Erfan Yazdandoost Hamedani, Young-Jun Son, Mohammed Shafae","doi":"10.1115/1.4063729","DOIUrl":"https://doi.org/10.1115/1.4063729","url":null,"abstract":"Abstract Identifying, analyzing, and evaluating cybersecurity risks is essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems is lacking. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors' techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295970","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}
Abstract This paper presents an improved method for optimizing the singularity structure of hexahedral meshes using various dual operations. Our approach aims at reducing element distortion by decomposing complex singular nodes into singular curves using high-quality sheet insertion at proper locations. Then, singular curves that meet the topological parallel requirements are paired to perform the semantic column operation, which eliminates the singular curves. Finally, the topological structure is further optimized by collapsing sheets, resulting in a valid hex mesh with a simpler structure. Compared to existing hexahedral mesh simplification methods, our approach can generate higher quality meshes. Experimental results demonstrate the effectiveness of the proposed method.
{"title":"Singularity structure optimization for hexahedral mesh via dual operations","authors":"Chun Shen, Rui Wang","doi":"10.1115/1.4063402","DOIUrl":"https://doi.org/10.1115/1.4063402","url":null,"abstract":"Abstract This paper presents an improved method for optimizing the singularity structure of hexahedral meshes using various dual operations. Our approach aims at reducing element distortion by decomposing complex singular nodes into singular curves using high-quality sheet insertion at proper locations. Then, singular curves that meet the topological parallel requirements are paired to perform the semantic column operation, which eliminates the singular curves. Finally, the topological structure is further optimized by collapsing sheets, resulting in a valid hex mesh with a simpler structure. Compared to existing hexahedral mesh simplification methods, our approach can generate higher quality meshes. Experimental results demonstrate the effectiveness of the proposed method.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136254743","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}
Abstract This paper focuses on the representation and synthesis of coupler curves of planar mechanisms using a deep neural network. While the path synthesis of planar mechanisms is not a new problem, the effective representation of coupler curves in the context of neural networks has not been fully explored. This study compares four commonly used features or representations of four-bar coupler curves: Fourier descriptors, wavelets, point coordinates, and images. The results demonstrate that these diverse representations can be unified using a generative AI framework called Variational Autoencoder (VAE). This study shows that a VAE can provide a standalone representation of a coupler curve, regardless of the input representation, and that the compact latent dimensions of the VAE can be used to describe coupler curves of four-bar linkages. Additionally, a new approach that utilizes a VAE in conjunction with a fully connected neural network to generate dimensional parameters of four-bar linkage mechanisms is proposed. This research presents a novel opportunity for automated conceptual design of mechanisms for robots and machines.
{"title":"An Invariant Representation of Coupler Curves using a Variational AutoEncoder: Application to Path Synthesis of Four-Bar Mechanisms","authors":"Anar Nurizada, Anurag Purwar","doi":"10.1115/1.4063726","DOIUrl":"https://doi.org/10.1115/1.4063726","url":null,"abstract":"Abstract This paper focuses on the representation and synthesis of coupler curves of planar mechanisms using a deep neural network. While the path synthesis of planar mechanisms is not a new problem, the effective representation of coupler curves in the context of neural networks has not been fully explored. This study compares four commonly used features or representations of four-bar coupler curves: Fourier descriptors, wavelets, point coordinates, and images. The results demonstrate that these diverse representations can be unified using a generative AI framework called Variational Autoencoder (VAE). This study shows that a VAE can provide a standalone representation of a coupler curve, regardless of the input representation, and that the compact latent dimensions of the VAE can be used to describe coupler curves of four-bar linkages. Additionally, a new approach that utilizes a VAE in conjunction with a fully connected neural network to generate dimensional parameters of four-bar linkage mechanisms is proposed. This research presents a novel opportunity for automated conceptual design of mechanisms for robots and machines.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135095618","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}
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