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Shop floor data-driven spatial–temporal verification for manual assembly planning
Journal of Intelligent Manufacturing ( IF 8.3 ) Pub Date : 2019-09-12 , DOI: 10.1007/s10845-019-01491-y
Wei Fang , Lianyu Zheng

Motivated by the increasing demand and highly customized products, accurate and up-to-date information about the manufacturing process become essential to meet these requirements. In manual assembly activities, performing theoretical planning in simulation environments is a crucial procedure to detect and avoid unreasonable assembly operations. However, the deviations between theoretical and actual assembly actions would result in the failure of the manual assembly planning. Therefore, the verification for the manual assembly planning is significant to ensure the correctness of the actual assembly operations, performing a convergence between the cyber and physical world. The challenges involved in retrieving and utilizing the actual data about the manual activities on a shop floor. In this paper, a self-contained wearable tracking system is proposed and applied to collect the shop-floor data during the manual assembly operations. And then, an unsupervised classification method is applied to empower semantic knowledge to the shop-floor data derived from the workplace. Thus, an automatic spatial–temporal verification for manual assembly planning is carried out, providing indicators to optimize the current manual assembly planning. Experimental results illustrate that the proposed work can perform the spatial–temporal verification for manual assembly task and indicate evidence to improve the manual assembly planning objectively.



中文翻译:

车间数据驱动的时空验证以进行手动装配计划

受需求增长和高度定制化产品的激励,有关制造过程的准确和最新信息对于满足这些要求至关重要。在手动装配活动中,在仿真环境中执行理论规划是检测和避免不合理装配操作的关键过程。但是,理论和实际装配动作之间的偏差会导致手动装配计划失败。因此,对手工装配计划的验证对于确保实际装配操作的正确性,实现网络世界与物理世界的融合非常重要。在车间获取和利用有关手动活动的实际数据涉及的挑战。在本文中,提出了一种独立的可穿戴跟踪系统,该系统可用于在手工组装操作期间收集车间数据。然后,采用无监督的分类方法来赋予从工作场所派生的车间数据语义知识。因此,对手动装配计划进行了自动的时空验证,为优化当前的手动装配计划提供了指标。实验结果表明,所提出的工作可以对手工装配任务进行时空验证,并为客观地改善手工装配规划提供证据。一种无监督的分类方法用于赋予从工作场所派生的车间数据语义知识。因此,对手动装配计划进行了自动的时空验证,为优化当前的手动装配计划提供了指标。实验结果表明,所提出的工作可以对手工装配任务进行时空验证,并为客观地改善手工装配规划提供证据。一种无监督的分类方法用于赋予从工作场所派生的车间数据语义知识。因此,对手动装配计划进行了自动的时空验证,为优化当前的手动装配计划提供了指标。实验结果表明,所提出的工作可以对手工装配任务进行时空验证,并为客观地改善手工装配规划提供证据。

更新日期:2020-04-21
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