School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China.
School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China.
Sci Rep. 2023 Jan 31;13(1):1769. doi: 10.1038/s41598-023-28630-z.
Assembly is a critical step in the manufacturing process. Robotic assembly technology in automatic production lines has greatly improved the production efficiency. However, in assembly process, dynamic disturbances such as processing time change and advance delivery may occur, which cause the scheduling deviation. Traditional scheduling methods are not sufficient to meet the real-time and adaptive requirements in smart manufacturing. Digital twin (DT) has the characteristics of virtual-reality interaction and real-time mapping. In this paper, we propose a DT-based framework of task rescheduling for robotic assembly line (RAL) and its key methodologies, thus to realize the timely and dynamic adjustment of scheduling plan under uncertain interferences. First, a DT model of RAL task rescheduling composed of physical entity (PE), virtual entity (VE), and virtual-reality interaction mechanism is proposed. Then, a mathematical model is established. By analyzing the adaptive objective thresholds from the perspectives of event trigger and user demand trigger, a DT-driven multi-level (production unit level and line level) rescheduling strategy is proposed. Taking both the computing time and solution quality into consideration, the precedence graph is introduced to propose a rescheduling approach based on an improved discrete fireworks algorithm. Finally, the effectiveness of the proposed model and approach are verified by task scheduling experiments of RAL.
装配是制造过程中的一个关键步骤。在自动生产线上，机器人装配技术极大地提高了生产效率。然而，在装配过程中，可能会出现加工时间变化和提前交货等动态干扰，导致调度偏差。传统的调度方法不足以满足智能制造中的实时性和适应性要求。数字孪生（DT）具有虚实互动和实时映射的特点。本文提出了一种基于 DT 的机器人装配线（RAL）任务重调度框架及其关键方法，从而实现不确定干扰下调度计划的及时和动态调整。首先，提出了一个由物理实体（PE）、虚拟实体（VE）和虚实互动机制组成的 RAL 任务重调度 DT 模型。然后，建立了一个数学模型。通过从事件触发和用户需求触发的角度分析自适应目标阈值，提出了一种基于生产单元级和线级的 DT 驱动的多层次（生产单元级和线级）重调度策略。综合考虑计算时间和求解质量，引入了优先图，提出了一种基于改进离散烟花算法的重调度方法。最后，通过 RAL 的任务调度实验验证了所提出模型和方法的有效性。
