Production planning for cloud-based additive manufacturing—A computer vision-based approach
Cloud-based additive manufacturing (AM) has shown its potential in the market in recent years. When the demand increases, it becomes more important to efficiently schedule tasks to achieve lower production time and cost. Current solutions are either less efficient or over-simplified the problem in t...
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sg-ntu-dr.10356-1512102021-06-29T04:46:02Z Production planning for cloud-based additive manufacturing—A computer vision-based approach Wang, Yuanbin Zheng, Pai Xu, Xun Yang, Huayong Zou, Jun School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Additive Manufacturing Cloud Manufacturing Cloud-based additive manufacturing (AM) has shown its potential in the market in recent years. When the demand increases, it becomes more important to efficiently schedule tasks to achieve lower production time and cost. Current solutions are either less efficient or over-simplified the problem in this situation. Therefore, an intelligent AM production planning system is proposed using computer vision algorithms. It firstly sorts tasks based on their heights, areas and remaining time to deadline. Then, models are projected on the printing plane and an improved vision-based method is applied to efficiently find high quality packing solutions. A comparative study has been conducted to verify the packing result and efficiency. At last, a case study has been displayed to illustrate the usefulness of the proposed method. This work was supported by the National Natural Science Foundation of China [Grant No. 51890885]. 2021-06-29T04:46:02Z 2021-06-29T04:46:02Z 2019 Journal Article Wang, Y., Zheng, P., Xu, X., Yang, H. & Zou, J. (2019). Production planning for cloud-based additive manufacturing—A computer vision-based approach. Robotics and Computer-Integrated Manufacturing, 58, 145-157. https://dx.doi.org/10.1016/j.rcim.2019.03.003 0736-5845 0000-0002-9988-6790 0000-0002-2329-8634 0000-0001-6294-8153 https://hdl.handle.net/10356/151210 10.1016/j.rcim.2019.03.003 2-s2.0-85062502010 58 145 157 en Robotics and Computer-Integrated Manufacturing © 2019 Elsevier Ltd. All rights reserved. |
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Engineering::Mechanical engineering Additive Manufacturing Cloud Manufacturing Wang, Yuanbin Zheng, Pai Xu, Xun Yang, Huayong Zou, Jun Production planning for cloud-based additive manufacturing—A computer vision-based approach |
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Cloud-based additive manufacturing (AM) has shown its potential in the market in recent years. When the demand increases, it becomes more important to efficiently schedule tasks to achieve lower production time and cost. Current solutions are either less efficient or over-simplified the problem in this situation. Therefore, an intelligent AM production planning system is proposed using computer vision algorithms. It firstly sorts tasks based on their heights, areas and remaining time to deadline. Then, models are projected on the printing plane and an improved vision-based method is applied to efficiently find high quality packing solutions. A comparative study has been conducted to verify the packing result and efficiency. At last, a case study has been displayed to illustrate the usefulness of the proposed method. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Wang, Yuanbin Zheng, Pai Xu, Xun Yang, Huayong Zou, Jun |
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Article |
author |
Wang, Yuanbin Zheng, Pai Xu, Xun Yang, Huayong Zou, Jun |
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Wang, Yuanbin |
title |
Production planning for cloud-based additive manufacturing—A computer vision-based approach |
title_short |
Production planning for cloud-based additive manufacturing—A computer vision-based approach |
title_full |
Production planning for cloud-based additive manufacturing—A computer vision-based approach |
title_fullStr |
Production planning for cloud-based additive manufacturing—A computer vision-based approach |
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Production planning for cloud-based additive manufacturing—A computer vision-based approach |
title_sort |
production planning for cloud-based additive manufacturing—a computer vision-based approach |
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2021 |
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https://hdl.handle.net/10356/151210 |
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1705151336350220288 |