Lv Qibing, Zhang Rong, Sun Xuemin, Lu Yuqian, Bao Jinsong
College of Mechanical Engineering, Donghua University, Shanghai, 201620, China.
Department of Mechanical Engineering, The University of Auckland, 0632, New Zealand.
J Manuf Syst. 2021 Jul;60:837-851. doi: 10.1016/j.jmsy.2021.02.011. Epub 2021 Feb 25.
In the wake of COVID-19, the production demand of medical equipment is increasing rapidly. This type of products is mainly assembled by hand or fixed program with complex and flexible structure. However, the low efficiency and adaptability in current assembly mode are unable to meet the assembly requirements. So in this paper, a new framework of human-robot collaborative (HRC) assembly based on digital twin (DT) is proposed. The data management system of proposed framework integrates all kinds of data from digital twin spaces. In order to obtain the HRC strategy and action sequence in dynamic environment, the double deep deterministic policy gradient (D-DDPG) is applied as optimization model in DT. During assembly, the performance model is adopted to evaluate the quality of resilience assembly. The proposed framework is finally validated by an alternator assembly case, which proves that DT-based HRC assembly has a significant effect on improving assembly efficiency and safety.
在新冠疫情之后，医疗设备的生产需求迅速增长。这类产品主要通过手工或固定程序进行组装，结构复杂且灵活。然而，当前组装模式的低效率和适应性不足无法满足组装要求。因此，本文提出了一种基于数字孪生（DT）的人机协作（HRC）组装新框架。所提框架的数据管理系统整合了来自数字孪生空间的各类数据。为了在动态环境中获得HRC策略和动作序列，将双深度确定性策略梯度（D-DDPG）作为数字孪生中的优化模型。在组装过程中，采用性能模型来评估弹性组装的质量。所提框架最终通过一个交流发电机组装案例进行了验证，这证明基于数字孪生的人机协作组装对提高组装效率和安全性有显著效果。
