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Fulvio Mastrogiovanni - One of the best experts on this subject based on the ideXlab platform.
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A Task Allocation Approach for Human-Robot Collaboration in Product Defects Inspection Scenarios
arXiv: Robotics, 2020Co-Authors: Hossein Karami, Kourosh Darvish, Fulvio MastrogiovanniAbstract:The presence and coexistence of human operators and collaborative robots in shop-floor environments raises the need for assigning tasks to either operators or robots, or both. Depending on task characteristics, operator capabilities and the involved robot functionalities, it is of the utmost importance to design strategies allowing for the concurrent and/or sequential allocation of tasks related to object manipulation and assembly. In this paper, we extend the \textsc{FlexHRC} framework presented in \cite{darvish2018flexible} to allow a human operator to interact with multiple, heterogeneous robots at the same time in order to jointly carry out a given task. The extended \textsc{FlexHRC} framework leverages a concurrent and sequential task representation framework to allocate tasks to either operators or robots as part of a dynamic collaboration process. In particular, we focus on a use case related to the inspection of Product Defects, which involves a human operator, a dual-arm Baxter manipulator from Rethink Robotics and a Kuka youBot mobile manipulator.
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a task allocation approach for human robot collaboration in Product Defects inspection scenarios
Robot and Human Interactive Communication, 2020Co-Authors: Hossein Karami, Kourosh Darvish, Fulvio MastrogiovanniAbstract:The presence and coexistence of human operators and collaborative robots in shop-floor environments raises the need for assigning tasks to either operators or robots, or both. Depending on task characteristics, operator capabilities and the involved robot functionalities, it is of the utmost importance to design strategies allowing for the concurrent and/or sequential allocation of tasks related to object manipulation and assembly. In this paper, we extend the FLEXHRC framework presented in [1] to allow a human operator to interact with multiple, heterogeneous robots at the same time in order to jointly carry out a given task. The extended FLEXHRC framework leverages a concurrent and sequential task representation framework to allocate tasks to either operators or robots as part of a dynamic collaboration process. In particular, we focus on a use case related to the inspection of Product Defects, which involves a human operator, a dual-arm Baxter manipulator from Rethink Robotics and a Kuka youBot mobile manipulator.
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RO-MAN - A Task Allocation Approach for Human-Robot Collaboration in Product Defects Inspection Scenarios
2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2020Co-Authors: Hossein Karami, Kourosh Darvish, Fulvio MastrogiovanniAbstract:The presence and coexistence of human operators and collaborative robots in shop-floor environments raises the need for assigning tasks to either operators or robots, or both. Depending on task characteristics, operator capabilities and the involved robot functionalities, it is of the utmost importance to design strategies allowing for the concurrent and/or sequential allocation of tasks related to object manipulation and assembly. In this paper, we extend the FLEXHRC framework presented in [1] to allow a human operator to interact with multiple, heterogeneous robots at the same time in order to jointly carry out a given task. The extended FLEXHRC framework leverages a concurrent and sequential task representation framework to allocate tasks to either operators or robots as part of a dynamic collaboration process. In particular, we focus on a use case related to the inspection of Product Defects, which involves a human operator, a dual-arm Baxter manipulator from Rethink Robotics and a Kuka youBot mobile manipulator.
Mooweon Rhee - One of the best experts on this subject based on the ideXlab platform.
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Does Reputation Contribute to Reducing Organizational Errors? A Learning Approach
Journal of Management Studies, 2009Co-Authors: Mooweon RheeAbstract:In this study I examine the effect of a firm's reputation for Product quality on its effort in learning to reduce its Product defect rate. Theoretical ideas on the motivation of learning associated with social aspiration levels and the self-serving bias combined with social categorization suggest that poor quality reputation firms are more likely than their counterparts with a good reputation to attend to potential Product Defects and consequently reduce their defect rate. However, a stream of research on the motivation of learning stemming from historical aspiration levels and slack search leads to a different argument: a reputation for good quality is more likely to provide firms with a motivation to avoid Product Defects. I build upon these two competing arguments and hypothesize that stronger motives for learning exist in situations where firms have either a weak or strong reputation for Product quality. My study of Product recalls in the US automotive industry highlights an inverted U-shaped relationship, indicating the liability of an intermediate reputation in reducing Product Defects.
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the liability of good reputation a study of Product recalls in the u s automobile industry
Academy of Management Proceedings, 2003Co-Authors: Mooweon Rhee, Pamela R HaunschildAbstract:The article explores the ways in which organizational reputation responds to the revelation of Product Defects or quality problems, as evidenced by formal Product recalls in the U.S. automobile ind...
A.h. Van Den Boogaard - One of the best experts on this subject based on the ideXlab platform.
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Product defect compensation by robust optimization of a cold roll forming process
Journal of Materials Processing Technology, 2013Co-Authors: J.h. Wiebenga, Bernard Rolfe, Matthias Weiss, A.h. Van Den BoogaardAbstract:The quality of roll formed Products is known to be highly dependent on the process design. In addition, unavoidable variations of material properties during mass Production can have a significant deteriorating effect on the Product quality. This study focuses on the question how to compensate for Product Defects while simultaneously minimizing the sensitivity for variation of material properties. This is achieved by using robust optimization techniques to determine the optimal process settings of adjustable tools in the final roll forming stand. The work covers both numerical analyses as well as experiments. Initial roll forming experiments of an Advanced High Strength Steel (AHSS) V-section profile showed a significant amount of longitudinal bow and springback in the final Product. Finite Element (FE) simulations are subsequently performed to determine the relationship between adjustable process settings and uncontrollable variation of incoming material properties with respect to the Product Defects. The computationally expensive non-linear FE simulations are subsequently replaced by the best performing metamodel chosen from of a family of metamodels. Using these metamodels, the optimal robust process settings for the adjustable stand are determined and the effect on Product quality analyzed. The results show that the effect of scattering material properties on the dimensional quality of the roll formed Product is significant. Moreover, it is shown that the adjustment of the tooling in the final roll stand leads to a significantly improved Product quality by compensating for Product Defects and minimizing the deteriorating effects of scattering variables.
Randy K. Smith - One of the best experts on this subject based on the ideXlab platform.
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evaluation of work Product Defects during corrective enhancive software evolution a field study comparison
ACM Sigmis Database, 2011Co-Authors: David P Hale, Joanne E Hale, Randy K. SmithAbstract:Information systems portfolio management assumes that software will evolve to maintain alignment with operational needs, a goal that must be met through effective ongoing maintenance. Thus, a primary goal of software maintainers is to ensure that Production code is updated without the introduction of Defects. However, there is a dearth of research that examines the work Product Defects that occur as these applications evolve. The goal of this study is to characterize software evolution lifecycle work Product Defects and factors that may increase or reduce their occurrence. The study takes place within a global consulting organization conducting ongoing software maintenance for a Fortune 100 telecommunications firm by a project team assessed at Capability Maturity Model Integration (CMMI) Level 3. This study reports on 991 work Product reviews conducted across the evolution activities of the ISO/IEC 12207 Software Development Life Cycle Processes. After controlling for team and expertise differences, the study's major finding is that corrective evolution projects inject a greater number of work Product Defects than enhancive evolution projects. This result does not arise from the schedule compression often associated with corrective evolution. Rather, it is concluded that the increase in work Product Defects is associated with the increased complexity of analysis-stage problem diagnosis found in corrective evolution projects. The analysis is augmented by additional covariates including the number of work Product reviewers, preparation time of reviewers, and size of the project.
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Evaluation of work Product Defects during corrective & enhancive software evolution: A field study comparison
ACM SIGMIS Database, 2010Co-Authors: David P Hale, Joanne E Hale, Randy K. SmithAbstract:Information systems portfolio management assumes that software will evolve to maintain alignment with operational needs, a goal that must be met through effective ongoing maintenance. Thus, a primary goal of software maintainers is to ensure that Production code is updated without the introduction of Defects. However, there is a dearth of research that examines the work Product Defects that occur as these applications evolve. The goal of this study is to characterize software evolution lifecycle work Product Defects and factors that may increase or reduce their occurrence. The study takes place within a global consulting organization conducting ongoing software maintenance for a Fortune 100 telecommunications firm by a project team assessed at Capability Maturity Model Integration (CMMI) Level 3. This study reports on 961 work Product reviews conducted across the evolution activities of the ISO/IEC 12207 Software Development Life Cycle Processes. After controlling for team and expertise differences, the study's major finding is that corrective evolution projects inject a greater number of work Product Defects than enhancive evolution projects. This result does not arise from the schedule compression often associated with corrective evolution. Rather, it is concluded that the increase in work Product Defects is associated with the increased complexity of analysis-stage problem diagnosis found in corrective evolution projects. The analysis is augmented by additional covariates including the number of work Product reviewers, preparation time of reviewers, and size of the project.
Xuan Zhang - One of the best experts on this subject based on the ideXlab platform.
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discovering Product Defects and solutions from online user generated contents
The Web Conference, 2019Co-Authors: Xuan Zhang, Zhilei Qiao, Aman Ahuja, Weiguo Fan, Edward A Fox, Chandan K ReddyAbstract:The recent increase in online user generated content (UGC) has led to the availability of a large number of posts about Products and services. Often, these posts contain complaints that the consumers purchasing the Products and services have. However, discovering and summarizing Product Defects and the related knowledge from large quantities of user posts is a difficult task. Traditional aspect opinion mining models, that aim to discover the Product aspects and their corresponding opinions, are not sufficient to discover the Product defect information from the user posts. In this paper, we propose the Product Defect Latent Dirichlet Allocation model (PDLDA), a probabilistic model that identifies domain-specific knowledge about Product issues using interdependent three-dimensional topics: Component, Symptom, and Resolution. A Gibbs sampling based inference method for PDLDA is also introduced. To evaluate our model, we introduce three novel Product review datasets. Both qualitative and quantitative evaluations show that the proposed model results in apparent improvement in the quality of discovered Product defect information. Our model has the potential to benefit customers, manufacturers, and policy makers, by automatically discovering Product Defects from online data.
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WWW - Discovering Product Defects and Solutions from Online User Generated Contents
The World Wide Web Conference on - WWW '19, 2019Co-Authors: Xuan Zhang, Zhilei Qiao, Aman Ahuja, Weiguo Fan, Edward A Fox, Chandan K ReddyAbstract:The recent increase in online user generated content (UGC) has led to the availability of a large number of posts about Products and services. Often, these posts contain complaints that the consumers purchasing the Products and services have. However, discovering and summarizing Product Defects and the related knowledge from large quantities of user posts is a difficult task. Traditional aspect opinion mining models, that aim to discover the Product aspects and their corresponding opinions, are not sufficient to discover the Product defect information from the user posts. In this paper, we propose the Product Defect Latent Dirichlet Allocation model (PDLDA), a probabilistic model that identifies domain-specific knowledge about Product issues using interdependent three-dimensional topics: Component, Symptom, and Resolution. A Gibbs sampling based inference method for PDLDA is also introduced. To evaluate our model, we introduce three novel Product review datasets. Both qualitative and quantitative evaluations show that the proposed model results in apparent improvement in the quality of discovered Product defect information. Our model has the potential to benefit customers, manufacturers, and policy makers, by automatically discovering Product Defects from online data.
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a domain oriented lda model for mining Product Defects from online customer reviews
Hawaii International Conference on System Sciences, 2017Co-Authors: Zhilei Qiao, Xuan Zhang, Mi Zhou, Gang Alan WangAbstract:Online reviews provide important demand-side knowledge for Product manufacturers to improve Product quality. However, discovering and quantifying potential Products’ Defects from large amounts of online reviews is a nontrivial task. In this paper, we propose a Latent Product Defect Mining model that identifies critical Product Defects. We define domain-oriented key attributes, such as components and keywords used to describe a defect, and build a novel LDA model to identify and acquire integral information about Product Defects. We conduct comprehensive evaluations including quantitative and qualitative evaluations to ensure the quality of discovered information. Experimental results show that the proposed model outperforms the standard LDA model, and could find more valuable information. Our research contributes to the extant Product quality analytics literature and has significant managerial implications for researchers, policy makers, customers, and practitioners.
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HICSS - A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews
Proceedings of the 50th Hawaii International Conference on System Sciences (2017), 2017Co-Authors: Zhilei Qiao, Xuan Zhang, Mi Zhou, Gang Alan Wang, Weiguo FanAbstract:Online reviews provide important demand-side knowledge for Product manufacturers to improve Product quality. However, discovering and quantifying potential Products’ Defects from large amounts of online reviews is a nontrivial task. In this paper, we propose a Latent Product Defect Mining model that identifies critical Product Defects. We define domain-oriented key attributes, such as components and keywords used to describe a defect, and build a novel LDA model to identify and acquire integral information about Product Defects. We conduct comprehensive evaluations including quantitative and qualitative evaluations to ensure the quality of discovered information. Experimental results show that the proposed model outperforms the standard LDA model, and could find more valuable information. Our research contributes to the extant Product quality analytics literature and has significant managerial implications for researchers, policy makers, customers, and practitioners.
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identifying Product Defects from user complaints a probabilistic defect model
Americas Conference on Information Systems, 2016Co-Authors: Xuan Zhang, Zhilei Qiao, Weiguo Fan, Edward A Fox, Lijie Tang, Gang WangAbstract:The recent surge in using social media has created a massive amount of unstructured textual complaints about Products and services. However, discovering and quantifying potential Product Defects from large amounts of unstructured text is a nontrivial task. In this paper, we develop a probabilistic defect model (PDM) that identifies the most critical Product issues and corresponding Product attributes, simultaneously. We facilitate domain-oriented key attributes (e.g., Product model, year of Production, defective components, symptoms, etc.) of a Product to identify and acquire integral information of defect. We conduct comprehensive evaluations including quantitative evaluations and qualitative evaluations to ensure the quality of discovered information. Experimental results demonstrate that our proposed model outperforms existing unsupervised method (K-Means Clustering), and could find more valuable information. Our research has significant managerial implications for mangers, manufacturers, and policy makers. [Category: Data and Text Mining]