A self-aware and active-guiding training & assistant system for worker-centered intelligent manufacturing
A self-aware and active-guiding training & assistant system for worker-centered intelligent manufacturing
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DOI：
10.1016/j.mfglet.2019.08.003
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发表时间：
2019-08-01
影响因子：
3.9
通讯作者：
Qin, Ruwen
中科院分区：
文献类型：
--
作者：
Tao, Wenjin;Lai, Ze-Hao;Qin, Ruwen
培训和现场援助对于帮助工人掌握所需技能、提高工人生产力并保证产品质量至关重要。传统的培训方法缺乏以工人为中心的考虑，而当工人面临不断变化的需求时，这种考虑尤为必要。本文提出了一种以工人为中心的智能制造培训辅助系统，该系统具有自我感知和主动指导的特点。应用多模态传感技术来感知每个工人，并开发深度学习方法来理解工人的行为和意图。此外，实施对象检测算法以识别工人正在与之交互的部件/工具。然后推断员工的当前状态，并用于量化和评估员工绩效，并从中分析员工潜在的指导需求。此外，在运营过程中，还积极并持续地提供多模式增强现实的现场指导。两个案例研究被用来证明我们所提出的方法和系统的可行性和巨大的潜力，适用于制造业的前线工人。(C)2019年制造工程师协会（SME）。由爱思唯尔有限公司出版。保留所有权利。
Training and on-site assistance is critical to help workers master required skills, improve worker productivity, and guarantee the product quality. Traditional training methods lack worker-centered considerations that are particularly in need when workers are facing ever-changing demands. In this study, we propose a worker-centered training & assistant system for intelligent manufacturing, which is featured with self-awareness and active-guidance. Multi-modal sensing techniques are applied to perceive each individual worker and a deep learning approach is developed to understand the worker's behavior and intention. Moreover, an object detection algorithm is implemented to identify the parts/tools the worker is interacting with. Then the worker's current state is inferred and used for quantifying and assessing the worker performance, from which the worker's potential guidance demands are analyzed. Furthermore, onsite guidance with multi-modal augmented reality is provided actively and continuously during the operational process. Two case studies are used to demonstrate the feasibility and great potential of our proposed approach and system for applying to the manufacturing industry for frontline workers. (C) 2019 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.