Accurate registration of Cartesian coordinate systems is necessary to facilitate metrology-based solutions for industrial robots in production environments. Conducting coordinate registration between industrial robots and their metrological systems requires measuring multiple points in the robot's and sensor system's coordinate frames. However, operators lack intuitive tools to interface, visualize, and characterize the quality of the selected points in the robot workspace for robot-sensor coordinate registration. This paper proposes an augmented reality system for human-in-the-loop, robot-sensor coordinate registration to efficiently record and visualize the pose-dependent quality of computing the robot-sensor transformation. Furthermore, this work establishes metrics to define the relative quality of measurement points used in robot-sensor coordinate registration, which are shown by the augmented reality application. Experiments were conducted demonstrating the augmented reality environment in addition to investigating the pose-dependency of the measurement point quality. The results indicate that the proposed metrics highlight the dependency of the poses on both robot and sensor placement and that the augmented reality system can provide a human-in-the-loop interface for robot-sensor coordinate registration.
{"title":"Augmented Reality Interface for Robot-Sensor Coordinate Registration","authors":"Vinh Nguyen, Xiaofeng Liu, J. Marvel","doi":"10.1115/1.4063131","DOIUrl":"https://doi.org/10.1115/1.4063131","url":null,"abstract":"\u0000 Accurate registration of Cartesian coordinate systems is necessary to facilitate metrology-based solutions for industrial robots in production environments. Conducting coordinate registration between industrial robots and their metrological systems requires measuring multiple points in the robot's and sensor system's coordinate frames. However, operators lack intuitive tools to interface, visualize, and characterize the quality of the selected points in the robot workspace for robot-sensor coordinate registration. This paper proposes an augmented reality system for human-in-the-loop, robot-sensor coordinate registration to efficiently record and visualize the pose-dependent quality of computing the robot-sensor transformation. Furthermore, this work establishes metrics to define the relative quality of measurement points used in robot-sensor coordinate registration, which are shown by the augmented reality application. Experiments were conducted demonstrating the augmented reality environment in addition to investigating the pose-dependency of the measurement point quality. The results indicate that the proposed metrics highlight the dependency of the poses on both robot and sensor placement and that the augmented reality system can provide a human-in-the-loop interface for robot-sensor coordinate registration.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49330088","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}
J. Michopoulos, A. Iliopoulos, J. Steuben, N. Apetre
The ever-improving advances of computational technologies have forced the user to manage higher resource complexity and motivates the modeling of more complex multiphysics systems than before. Consequently, the time for the user's iterations within the context space characterizing all choices required for a successful computation far exceeds the time required for the runtime software execution to produce acceptable results. This paper presents metacomputing as an approach to address this issue, starting with describing this high-dimensional context space. Then it highlights the abstract process of multiphysics model generation/solution and proposes performing top-down and bottom-up metacomputing. In the top-down approach, metacomputing is used for: Automating the process of generating theories; Raising the semantic dimensionality of these theories in higher dimensional algebraic systems that enable simplification of the equational representation and raising the syntactic dimensionality of equational representation from 1-D equational forms to 2-D and 3-D algebraic solution graphs that reduce solving to path-following. In the bottom-up approach, already existing legacy codes evolving over multiple decades are encapsulated at the bottom layer of a multilayer semantic framework that utilizes Category Theory based operations on specifications to enable the user to spend time only for defining the physics of the relevant problem and not have to deal with the rest of the details involved in deploying and executing the solution of the problem at hand. Consequently, these two metacomputing approaches enable the generation, composition, deployment, and execution of directly computable multiphysics models.
{"title":"Metacomputing for Directly Computable Multiphysics Models","authors":"J. Michopoulos, A. Iliopoulos, J. Steuben, N. Apetre","doi":"10.1115/1.4063103","DOIUrl":"https://doi.org/10.1115/1.4063103","url":null,"abstract":"\u0000 The ever-improving advances of computational technologies have forced the user to manage higher resource complexity and motivates the modeling of more complex multiphysics systems than before. Consequently, the time for the user's iterations within the context space characterizing all choices required for a successful computation far exceeds the time required for the runtime software execution to produce acceptable results. This paper presents metacomputing as an approach to address this issue, starting with describing this high-dimensional context space. Then it highlights the abstract process of multiphysics model generation/solution and proposes performing top-down and bottom-up metacomputing. In the top-down approach, metacomputing is used for: Automating the process of generating theories; Raising the semantic dimensionality of these theories in higher dimensional algebraic systems that enable simplification of the equational representation and raising the syntactic dimensionality of equational representation from 1-D equational forms to 2-D and 3-D algebraic solution graphs that reduce solving to path-following. In the bottom-up approach, already existing legacy codes evolving over multiple decades are encapsulated at the bottom layer of a multilayer semantic framework that utilizes Category Theory based operations on specifications to enable the user to spend time only for defining the physics of the relevant problem and not have to deal with the rest of the details involved in deploying and executing the solution of the problem at hand. Consequently, these two metacomputing approaches enable the generation, composition, deployment, and execution of directly computable multiphysics models.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45475226","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}
Hao Chen, Hongbin Lin, Qingfeng Xu, Yaguan Li, Yiming Zheng, Jianghua Fei, Kang Yang, Wenhui Fan, Zhenguo Nie
Defect detection is a crucial direction of deep learning, which is suitable for industrial inspection of product quality in strip steel. As the strip steel production line continuously outputs products, it is necessary to take corresponding measures for the type of defect, once a subtle quality problem is found on steel strips. We propose a new defect area detection and classification method for automation strip steel defect detection. In order to eliminate the way of insufficient data in industrial production line scenarios, we design a transfer learning scheme to support the training of defect region detection. Subsequently, in order to achieve a more accurate classification of defect categories, we designed a deep learning model that integrated the detection results of defect regions and defects feature extraction. After applying our method to the test set and production line, we can achieve extremely high accuracy, reaching 87.11%, while meeting the production speed of the production line compared with other methods. The accuracy and speed of the model realize automatic quality monitoring in the manufacturing process of strip steel.
{"title":"Cross-domain Transfer Learning for Galvanized Steel Strips Defect Detection and Recognition","authors":"Hao Chen, Hongbin Lin, Qingfeng Xu, Yaguan Li, Yiming Zheng, Jianghua Fei, Kang Yang, Wenhui Fan, Zhenguo Nie","doi":"10.1115/1.4063102","DOIUrl":"https://doi.org/10.1115/1.4063102","url":null,"abstract":"\u0000 Defect detection is a crucial direction of deep learning, which is suitable for industrial inspection of product quality in strip steel. As the strip steel production line continuously outputs products, it is necessary to take corresponding measures for the type of defect, once a subtle quality problem is found on steel strips. We propose a new defect area detection and classification method for automation strip steel defect detection. In order to eliminate the way of insufficient data in industrial production line scenarios, we design a transfer learning scheme to support the training of defect region detection. Subsequently, in order to achieve a more accurate classification of defect categories, we designed a deep learning model that integrated the detection results of defect regions and defects feature extraction. After applying our method to the test set and production line, we can achieve extremely high accuracy, reaching 87.11%, while meeting the production speed of the production line compared with other methods. The accuracy and speed of the model realize automatic quality monitoring in the manufacturing process of strip steel.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45020229","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 automotive industry is undergoing a massive transformation, driven by the mega-trends of “CASE”: connected, automated, shared, and electric. These trends are affecting the nature of automobiles, both internally and externally. Internally, the transition from internal combustion engines (ICE) to electric drive-trains has resulted in a shift from hardware-defined vehicles to software-defined vehicles (SDVs), where software is increasingly becoming the dominant asset in the automotive value chain. These trends are leading to new design challenges such as how to manage different configurations of design, how to decouple the design of software and services from hardware, and how to design hardware to allow for upgrades. Externally, automobiles are no longer isolated products. Instead, they are part of the larger digital ecosystem with cloud connectivity. Vehicle usage data are increasingly connected with smart factories, which creates new opportunities for agile product development and mass customization of features. The role of the human driver is also changing with increasing levels of autonomy features. In this paper, the authors discuss the ongoing transformation in the automotive industry and its implications for engineering design. The paper presents a road map for engineering design research for next-generation automotive applications.
{"title":"Design of Next Generation Automotive Systems: Challenges and Research Opportunities","authors":"Jitesh H. Panchal, Ziran Wang","doi":"10.1115/1.4063067","DOIUrl":"https://doi.org/10.1115/1.4063067","url":null,"abstract":"\u0000 The automotive industry is undergoing a massive transformation, driven by the mega-trends of “CASE”: connected, automated, shared, and electric. These trends are affecting the nature of automobiles, both internally and externally. Internally, the transition from internal combustion engines (ICE) to electric drive-trains has resulted in a shift from hardware-defined vehicles to software-defined vehicles (SDVs), where software is increasingly becoming the dominant asset in the automotive value chain. These trends are leading to new design challenges such as how to manage different configurations of design, how to decouple the design of software and services from hardware, and how to design hardware to allow for upgrades. Externally, automobiles are no longer isolated products. Instead, they are part of the larger digital ecosystem with cloud connectivity. Vehicle usage data are increasingly connected with smart factories, which creates new opportunities for agile product development and mass customization of features. The role of the human driver is also changing with increasing levels of autonomy features. In this paper, the authors discuss the ongoing transformation in the automotive industry and its implications for engineering design. The paper presents a road map for engineering design research for next-generation automotive applications.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47105333","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}
Online detection of wave soldering is an important method of inspecting defective products in the workshop. Accurate quality detection can reduce production costs and provide support for quality warning in wave soldering process. However, there are still problems of improving the detection accuracy for defect class. Although class imbalance in data can be addressed by data level methods such as over-sampling and under-sampling, these methods destroy the integrity of the original data set and may cause information loss and overfitting problems. In order to solve the above problems, this article focuses on how to design a new loss function that fuses class weights from focal loss (FS) and sample weights form AdaBoost to improve attention to the minority samples without changing data distribution. In this way, a FS-AdaBoost-RegNet model based on transfer learning is constructed to enhance the detection accuracy in industrial environment. Finally, the images of the wave soldering from an electronic assembly workshop are taken to validate the performance of the proposed method. The experiment on 941 testing samples of the imbalance datasets showed that the FS-AdaBoost-RegNet model with new loss function reached the overall accuracy of 98.39%, the overall recall of 96.19%. The results proved that the proposed method promotes the ability to identify defect class compared with other methods
{"title":"An online quality detection method with ensemble learning on imbalance data for wave soldering","authors":"Hanpeng Gao, Yu Guo, Shaohua Huang, Jian Xie, Daoyuan Liu, Tao Wu, Xu Tian","doi":"10.1115/1.4063068","DOIUrl":"https://doi.org/10.1115/1.4063068","url":null,"abstract":"\u0000 Online detection of wave soldering is an important method of inspecting defective products in the workshop. Accurate quality detection can reduce production costs and provide support for quality warning in wave soldering process. However, there are still problems of improving the detection accuracy for defect class. Although class imbalance in data can be addressed by data level methods such as over-sampling and under-sampling, these methods destroy the integrity of the original data set and may cause information loss and overfitting problems. In order to solve the above problems, this article focuses on how to design a new loss function that fuses class weights from focal loss (FS) and sample weights form AdaBoost to improve attention to the minority samples without changing data distribution. In this way, a FS-AdaBoost-RegNet model based on transfer learning is constructed to enhance the detection accuracy in industrial environment. Finally, the images of the wave soldering from an electronic assembly workshop are taken to validate the performance of the proposed method. The experiment on 941 testing samples of the imbalance datasets showed that the FS-AdaBoost-RegNet model with new loss function reached the overall accuracy of 98.39%, the overall recall of 96.19%. The results proved that the proposed method promotes the ability to identify defect class compared with other methods","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45375371","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}
Various sources of error can lead to the position accuracy of the robot being orders of magnitude worse than its repeatability. For the accuracy of drilling in the aviation field, high-precision assembly, and other fields depend on the industrial robot's absolute positioning accuracy, it is essential to improve the accuracy of absolute positioning by calibration. In the present paper, an error model of the robot is established considering both constant and joint-dependent kinematic errors, and the robot model is modified by the Hermite polynomial. To identify joint-dependent kinematic errors, a robot calibration method based on back-propagation neural network(BP) optimized by Sparrow Search Algorithm (SSA-BP) is proposed, which optimize the uncertainty of weights and thresholds in the BP algorithm . To validate the efficiency of the proposed method, experiments on an EFORT ECR5 robot were implemented. The positioning error is reduced from 3.1704 mm to 0.2798 mm, and the positioning accuracy is improved by 91.27%. With the new calibration method using SSA-BP, robot positioning errors can be effectively compensated for and the robot positioning accuracy can be improved significantly.
{"title":"Enhancing Robot Calibration through Reliable High-Order Hermite Polynomials Model and SSA-BP Optimization","authors":"Yujie Zhang, Qi Fang, Yu Xie, Weijie Zhang, Runxiang Yu","doi":"10.1115/1.4063035","DOIUrl":"https://doi.org/10.1115/1.4063035","url":null,"abstract":"\u0000 Various sources of error can lead to the position accuracy of the robot being orders of magnitude worse than its repeatability. For the accuracy of drilling in the aviation field, high-precision assembly, and other fields depend on the industrial robot's absolute positioning accuracy, it is essential to improve the accuracy of absolute positioning by calibration. In the present paper, an error model of the robot is established considering both constant and joint-dependent kinematic errors, and the robot model is modified by the Hermite polynomial. To identify joint-dependent kinematic errors, a robot calibration method based on back-propagation neural network(BP) optimized by Sparrow Search Algorithm (SSA-BP) is proposed, which optimize the uncertainty of weights and thresholds in the BP algorithm . To validate the efficiency of the proposed method, experiments on an EFORT ECR5 robot were implemented. The positioning error is reduced from 3.1704 mm to 0.2798 mm, and the positioning accuracy is improved by 91.27%. With the new calibration method using SSA-BP, robot positioning errors can be effectively compensated for and the robot positioning accuracy can be improved significantly.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44355432","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 connectivity of modern vehicles allows for the monitoring and analysis of a large amount of sensor data from vehicles during their normal operations. In recent years, there has been a growing interest in utilizing this data for the purposes of predictive maintenance. In this paper, a multi-label transfer learning approach is proposed using fourteen different pretrained classifier models retrained with engine simulation data to predict the failure conditions of a selected set of engine components. The retrained classifiers are designed such that the failure modes, including multimode failure, of an EGR, Compressor, Intercooler, and Fuel Injectors of a four-cylinder diesel engine can be identified. Time-series simulation data of various failure conditions, which includes performance degradation, is generated to retrain the classifier models to predict which components are failing at any given time. The test results of the retrained classifier models show that the overall classification performance is good, with the value of mean average precision varying from 0.7 to 0.75 for most retrained networks. To the best of the authors' knowledge, this work represents the first attempt to characterize such time-series data utilizing a multi-label deep learning approach.
{"title":"Determination of Multi-Component Failure in Automotive System using Deep Learning","authors":"John O'Donnell, Hwan-Sik Yoon","doi":"10.1115/1.4063003","DOIUrl":"https://doi.org/10.1115/1.4063003","url":null,"abstract":"\u0000 The connectivity of modern vehicles allows for the monitoring and analysis of a large amount of sensor data from vehicles during their normal operations. In recent years, there has been a growing interest in utilizing this data for the purposes of predictive maintenance. In this paper, a multi-label transfer learning approach is proposed using fourteen different pretrained classifier models retrained with engine simulation data to predict the failure conditions of a selected set of engine components. The retrained classifiers are designed such that the failure modes, including multimode failure, of an EGR, Compressor, Intercooler, and Fuel Injectors of a four-cylinder diesel engine can be identified. Time-series simulation data of various failure conditions, which includes performance degradation, is generated to retrain the classifier models to predict which components are failing at any given time. The test results of the retrained classifier models show that the overall classification performance is good, with the value of mean average precision varying from 0.7 to 0.75 for most retrained networks. To the best of the authors' knowledge, this work represents the first attempt to characterize such time-series data utilizing a multi-label deep learning approach.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43558183","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}
Quantum computing as the emerging paradigm for scientific computing has attracted significant research attention in the past decade. Quantum algorithms to solve the problems of linear systems, eigenvalue, optimization, machine learning, and others have been developed. The main advantage of utilizing quantum computer to solve optimization problems is that quantum superposition allows for massive parallel searching of solutions. This article provides an overview of fundamental quantum algorithms that can be used to solve optimization problems, including Grover search, quantum phase estimation, quantum annealing, quantum approximate optimization algorithm, variational quantum eigensolver, and quantum walk. A review of recent applications of quantum optimization methods for engineering design, including materials design and topology optimization, is also given. The challenges to develop scalable and reliable quantum algorithms for engineering optimization are discussed.
{"title":"Opportunities and Challenges of Quantum Computing for Engineering Optimization","authors":"Yan Wang, Jungin E. Kim, K. Suresh","doi":"10.1115/1.4062969","DOIUrl":"https://doi.org/10.1115/1.4062969","url":null,"abstract":"\u0000 Quantum computing as the emerging paradigm for scientific computing has attracted significant research attention in the past decade. Quantum algorithms to solve the problems of linear systems, eigenvalue, optimization, machine learning, and others have been developed. The main advantage of utilizing quantum computer to solve optimization problems is that quantum superposition allows for massive parallel searching of solutions. This article provides an overview of fundamental quantum algorithms that can be used to solve optimization problems, including Grover search, quantum phase estimation, quantum annealing, quantum approximate optimization algorithm, variational quantum eigensolver, and quantum walk. A review of recent applications of quantum optimization methods for engineering design, including materials design and topology optimization, is also given. The challenges to develop scalable and reliable quantum algorithms for engineering optimization are discussed.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46728345","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}
Ananya Ipsita, Runlin Duan, Hao Li, Subramanian C, Yuanzhi Cao, Min Liu, Alexander J. Quinn, Karthik Ramani
Domain users (DUs) with a knowledge base in specialized fields are frequently excluded from authoring Virtual Reality (VR)-based applications in corresponding fields. This is largely due to the requirement of VR programming expertise needed to author these applications. To address this concern, we developed VRFromX, a system workflow design to make the virtual content creation process accessible to DUs irrespective of their programming skills and experience. VRFromX provides an in-situ process of content creation in VR that (a) allows users to select regions of interest in scanned point clouds or sketch in mid-air using a brush tool to retrieve virtual models, and (b) then attach behavioral properties to those objects. Using a welding use case, we performed a usability evaluation of VRFromX with 20 DUs from which 12 were novices in VR programming. Study results indicated positive user ratings for the system features with no significant differences across users with or without VR programming expertise. Based on the qualitative feedback, we also implemented two other use cases to demonstrate potential applications. We envision that the solution can facilitate the adoption of the immersive technology to create meaningful virtual environments.
{"title":"The Design of a Virtual Prototyping System for Authoring Interactive VR Environments from Real World Scans","authors":"Ananya Ipsita, Runlin Duan, Hao Li, Subramanian C, Yuanzhi Cao, Min Liu, Alexander J. Quinn, Karthik Ramani","doi":"10.1115/1.4062970","DOIUrl":"https://doi.org/10.1115/1.4062970","url":null,"abstract":"\u0000 Domain users (DUs) with a knowledge base in specialized fields are frequently excluded from authoring Virtual Reality (VR)-based applications in corresponding fields. This is largely due to the requirement of VR programming expertise needed to author these applications. To address this concern, we developed VRFromX, a system workflow design to make the virtual content creation process accessible to DUs irrespective of their programming skills and experience. VRFromX provides an in-situ process of content creation in VR that (a) allows users to select regions of interest in scanned point clouds or sketch in mid-air using a brush tool to retrieve virtual models, and (b) then attach behavioral properties to those objects. Using a welding use case, we performed a usability evaluation of VRFromX with 20 DUs from which 12 were novices in VR programming. Study results indicated positive user ratings for the system features with no significant differences across users with or without VR programming expertise. Based on the qualitative feedback, we also implemented two other use cases to demonstrate potential applications. We envision that the solution can facilitate the adoption of the immersive technology to create meaningful virtual environments.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47636829","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}
There is a growing interest in using deep learning technologies within the manufacturing industry to improve quality, productivity, safety, and efficiency, while also reducing costs and cycle time. This paper discusses the primary applications of deep learning currently being employed, including identifying defects during high-mix production, optimizing processes, streamlining the supply chain, predicting maintenance needs, and recognizing human activity. The paper offers a brief summary of the various components of deep learning technology and their roles. Additionally, the paper draws attention to the current challenges and limitations that need to be addressed to fully realize the potential of deep learning technology in manufacturing. Lastly, several future directions for research within the field are proposed to further improve the use of deep learning in manufacturing.
{"title":"The Role of Deep Learning in Manufacturing Applications: Challenges and Opportunities","authors":"R. Malhan, S. Gupta","doi":"10.1115/1.4062939","DOIUrl":"https://doi.org/10.1115/1.4062939","url":null,"abstract":"\u0000 There is a growing interest in using deep learning technologies within the manufacturing industry to improve quality, productivity, safety, and efficiency, while also reducing costs and cycle time. This paper discusses the primary applications of deep learning currently being employed, including identifying defects during high-mix production, optimizing processes, streamlining the supply chain, predicting maintenance needs, and recognizing human activity. The paper offers a brief summary of the various components of deep learning technology and their roles. Additionally, the paper draws attention to the current challenges and limitations that need to be addressed to fully realize the potential of deep learning technology in manufacturing. Lastly, several future directions for research within the field are proposed to further improve the use of deep learning in manufacturing.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75367032","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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