The Experts below are selected from a list of 272331 Experts worldwide ranked by ideXlab platform
Sanjay Jain - One of the best experts on this subject based on the ideXlab platform.
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Manufacturing Data analytics using a virtual factory representation
International Journal of Production Economics, 2017Co-Authors: Sanjay Jain, Guodong Shao, Seungjun ShinAbstract:Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big Data, simulation can be utilised to perform and support Data analytics for associated performance gains. This requires not only significant model development expertise, but also huge Data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and Data analytics in Manufacturing via reduction of the development effort. The use of Manufacturing simulation models is presented as Data analytics applications themselves and for supporting other Data analytics applications by serving as Data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for Manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models ...
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virtual factory revisited for Manufacturing Data analytics
Winter Simulation Conference, 2014Co-Authors: Sanjay Jain, Guodong ShaoAbstract:Development of an effective Data analytics application for Manufacturing requires testing with large sets of Data. It is usually difficult for application developers to find access to real Manufacturing Data streams for testing new Data analytics applications. Virtual factories can be developed to generate the Data for selected measures in formats matching those of real factories. The vision of a virtual factory has been around for more than a couple decades. Advances in technologies for computation, communication, and integration and in associated standards have made the vision of a virtual factory within reach now. This paper discusses requirements for a virtual factory to meet the needs of Manufacturing Data analytics applications. A framework for the virtual factory is proposed that leverages current technology and standards to help identify the developments needed for the realization of virtual factories.
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Data analytics using simulation for smart Manufacturing
Winter Simulation Conference, 2014Co-Authors: Guodong Shao, Seungjun Shin, Sanjay JainAbstract:Manufacturing organizations are able to accumulate large amounts of plant floor production and environmental Data due to advances in Data collection, communications technology, and use of standards. The challenge has shifted from collecting a sufficient amount of Data to analyzing and making decisions based on the huge amount of Data available. Data analytics (DA) can help understand and gain insights from the big Data and in turn help advance towards the vision of smart Manufacturing. Modeling and simulation have been used by manufacturers to analyze their operations and support decision making. This paper proposes multiple methods in which simulation can serve as a DA application or support other DA applications in Manufacturing environment to address big Data issues. An example case is discussed to demonstrate one use of simulation. In the presented case, a virtual representation of machining operations is used to generate the Data required to evaluate Manufacturing Data analytics applications.
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parallel discrete event simulation of a supply chain in semiconductor industry
IEEE International Conference on High Performance Computing Data and Analytics, 2000Co-Authors: Yokehean Low, Boonping Gan, Sanjay Jain, Wentong Cai, Wenjing Hsu, Shell Ying Huang, Stephen John TurnerAbstract:This paper describes our work on the study of how parallel discrete-event simulation techniques can be applied in a supply-chain simulation. A supply-chain simulation model consists of multiple virtual factory models. Each virtual factory model executes integrated simulation of the Manufacturing and business processes and communications network to allow one to analyze the effects of different system configurations and control policies on actual system performance. Since a supply-chain model will have a large problem size and take a long time to run, it is natural to consider the use of parallel discrete-event simulation techniques (PDES) to reduce its execution time, so that the simulation results can be obtained in a reasonable amount of time. This project uses PDES on real-world Manufacturing Data-sets. A parallel simulation protocol is developed to exploit the shared memory hardware for conducting supply-chain simulation.
Guodong Shao - One of the best experts on this subject based on the ideXlab platform.
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Manufacturing Data analytics using a virtual factory representation
International Journal of Production Economics, 2017Co-Authors: Sanjay Jain, Guodong Shao, Seungjun ShinAbstract:Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big Data, simulation can be utilised to perform and support Data analytics for associated performance gains. This requires not only significant model development expertise, but also huge Data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and Data analytics in Manufacturing via reduction of the development effort. The use of Manufacturing simulation models is presented as Data analytics applications themselves and for supporting other Data analytics applications by serving as Data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for Manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models ...
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virtual factory revisited for Manufacturing Data analytics
Winter Simulation Conference, 2014Co-Authors: Sanjay Jain, Guodong ShaoAbstract:Development of an effective Data analytics application for Manufacturing requires testing with large sets of Data. It is usually difficult for application developers to find access to real Manufacturing Data streams for testing new Data analytics applications. Virtual factories can be developed to generate the Data for selected measures in formats matching those of real factories. The vision of a virtual factory has been around for more than a couple decades. Advances in technologies for computation, communication, and integration and in associated standards have made the vision of a virtual factory within reach now. This paper discusses requirements for a virtual factory to meet the needs of Manufacturing Data analytics applications. A framework for the virtual factory is proposed that leverages current technology and standards to help identify the developments needed for the realization of virtual factories.
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Data analytics using simulation for smart Manufacturing
Winter Simulation Conference, 2014Co-Authors: Guodong Shao, Seungjun Shin, Sanjay JainAbstract:Manufacturing organizations are able to accumulate large amounts of plant floor production and environmental Data due to advances in Data collection, communications technology, and use of standards. The challenge has shifted from collecting a sufficient amount of Data to analyzing and making decisions based on the huge amount of Data available. Data analytics (DA) can help understand and gain insights from the big Data and in turn help advance towards the vision of smart Manufacturing. Modeling and simulation have been used by manufacturers to analyze their operations and support decision making. This paper proposes multiple methods in which simulation can serve as a DA application or support other DA applications in Manufacturing environment to address big Data issues. An example case is discussed to demonstrate one use of simulation. In the presented case, a virtual representation of machining operations is used to generate the Data required to evaluate Manufacturing Data analytics applications.
Chris Yuan - One of the best experts on this subject based on the ideXlab platform.
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Life Cycle Assessment of Titania Perovskite Solar Cell Technology for Sustainable Design and Manufacturing
Chemsuschem, 2015Co-Authors: Jingyi Zhang, Xianfeng Gao, Yelin Deng, Chris YuanAbstract:Perovskite solar cells have attracted enormous attention in recent years due to their low cost and superior technical performance. However, the use of toxic metals, such as lead, in the perovskite dye and toxic chemicals in perovskite solar cell Manufacturing causes grave concerns for its environmental performance. To understand and facilitate the sustainable development of perovskite solar cell technology from its design to Manufacturing, a comprehensive environmental impact assessment has been conducted on titanium dioxide nanotube based perovskite solar cells by using an attributional life cycle assessment approach, from cradle to gate, with Manufacturing Data from our laboratory-scale experiments and upstream Data collected from professional Databases and the literature. The results indicate that the perovskite dye is the primary source of environmental impact, associated with 64.77% total embodied energy and 31.38% embodied materials consumption, contributing to more than 50% of the life cycle impact in almost all impact categories, although lead used in the perovskite dye only contributes to about 1.14% of the human toxicity potential. A comparison of perovskite solar cells with commercial silicon and cadmium-tellurium solar cells reveals that perovskite solar cells could be a promising alternative technology for future large-scale industrial applications.
Seungjun Shin - One of the best experts on this subject based on the ideXlab platform.
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Manufacturing Data analytics using a virtual factory representation
International Journal of Production Economics, 2017Co-Authors: Sanjay Jain, Guodong Shao, Seungjun ShinAbstract:Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big Data, simulation can be utilised to perform and support Data analytics for associated performance gains. This requires not only significant model development expertise, but also huge Data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and Data analytics in Manufacturing via reduction of the development effort. The use of Manufacturing simulation models is presented as Data analytics applications themselves and for supporting other Data analytics applications by serving as Data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for Manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models ...
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Data analytics using simulation for smart Manufacturing
Winter Simulation Conference, 2014Co-Authors: Guodong Shao, Seungjun Shin, Sanjay JainAbstract:Manufacturing organizations are able to accumulate large amounts of plant floor production and environmental Data due to advances in Data collection, communications technology, and use of standards. The challenge has shifted from collecting a sufficient amount of Data to analyzing and making decisions based on the huge amount of Data available. Data analytics (DA) can help understand and gain insights from the big Data and in turn help advance towards the vision of smart Manufacturing. Modeling and simulation have been used by manufacturers to analyze their operations and support decision making. This paper proposes multiple methods in which simulation can serve as a DA application or support other DA applications in Manufacturing environment to address big Data issues. An example case is discussed to demonstrate one use of simulation. In the presented case, a virtual representation of machining operations is used to generate the Data required to evaluate Manufacturing Data analytics applications.
Jingyi Zhang - One of the best experts on this subject based on the ideXlab platform.
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Life Cycle Assessment of Titania Perovskite Solar Cell Technology for Sustainable Design and Manufacturing
Chemsuschem, 2015Co-Authors: Jingyi Zhang, Xianfeng Gao, Yelin Deng, Chris YuanAbstract:Perovskite solar cells have attracted enormous attention in recent years due to their low cost and superior technical performance. However, the use of toxic metals, such as lead, in the perovskite dye and toxic chemicals in perovskite solar cell Manufacturing causes grave concerns for its environmental performance. To understand and facilitate the sustainable development of perovskite solar cell technology from its design to Manufacturing, a comprehensive environmental impact assessment has been conducted on titanium dioxide nanotube based perovskite solar cells by using an attributional life cycle assessment approach, from cradle to gate, with Manufacturing Data from our laboratory-scale experiments and upstream Data collected from professional Databases and the literature. The results indicate that the perovskite dye is the primary source of environmental impact, associated with 64.77% total embodied energy and 31.38% embodied materials consumption, contributing to more than 50% of the life cycle impact in almost all impact categories, although lead used in the perovskite dye only contributes to about 1.14% of the human toxicity potential. A comparison of perovskite solar cells with commercial silicon and cadmium-tellurium solar cells reveals that perovskite solar cells could be a promising alternative technology for future large-scale industrial applications.