Robotic disc grinding path planning method based on multi-objective optimization for nuclear reactor coolant pump casing

IF 12.2 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Journal of Manufacturing Systems Pub Date : 2024-11-01 DOI:10.1016/j.jmsy.2024.10.021
Bo Zhou , Tongtong Tian
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Abstract

In the nuclear industry, the finishing grinding work of the nuclear reactor coolant pump (RCP) casing is mainly performed manually. Uncontrollable grinding tasks cause the grinding disc to be easily worn during the grinding process, which will greatly affect the grinding accuracy and efficiency. This paper introduces a path planning method that can efficiently and accurately perform a disc grinding task on an RCP casing. First, we provide a wear model for rigid grinding discs and verify its accuracy through finite element simulations and experiments. It can be used to predict the wear conditions of grinding discs during grinding. Then, a series of linear geodesic offset paths with the shortest path length characteristic can be generated and converted to NURBS interpolation paths. The velocity, acceleration, and jerk of the of the NURBS interpolated path generated by the S-shaped acceleration/deceleration (ACC/DEC) feedrate planning method in Cartesian space can be converted into the corresponding angular velocity, acceleration, and jerk of each joint in joint space to ensure that the grinding tasks can be performed under appropriate kinematic constraints; Then, an improved NSGA-II algorithm is proposed and its performance is verified based on benchmark test problem suite in three indicators. The verification results showed that the solution set generated by the proposed algorithm has good distribution uniformity, is closer to the true boundary, and has good convergence compared with other advanced optimization algorithms; Furthermore, by substituting the multi-objective optimization functions and kinematic constraints into the improved NSGA-II algorithm, the compromise minimization problem of grinding time, impact, and disc wear can be solved. The simulation and experimental results demonstrate the superiority and effectiveness of the optimized geodesic grinding paths in terms of grinding precision, accuracy, stability, and efficiency. In contrast, multi-directional paths, e.g., optimized cycloid paths, will produce varying grinding contact forces and varying disc sliding velocities, which will lead to more complex material removal situations, thus affecting the accuracy of the optimization solution.
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基于多目标优化的核反应堆冷却剂泵壳机器人盘磨路径规划方法
在核工业中,核反应堆冷却剂泵(RCP)外壳的精磨工作主要由人工完成。磨削任务的不可控性导致磨盘在磨削过程中容易磨损,从而极大地影响磨削精度和效率。本文介绍了一种路径规划方法,可以高效、准确地完成 RCP 外壳的圆盘磨削任务。首先,我们提供了刚性磨盘的磨损模型,并通过有限元模拟和实验验证了其准确性。该模型可用于预测磨削过程中磨盘的磨损状况。然后,生成一系列具有最短路径长度特征的线性大地偏移路径,并将其转换为 NURBS 插值路径。在直角坐标空间中,通过 S 形加速度/减速度(ACC/DEC)进给率规划方法生成的 NURBS 插值路径的速度、加速度和颠簸可转换为关节空间中各关节的相应角速度、加速度和颠簸,以确保在适当的运动学约束条件下执行磨削任务;然后,提出了一种改进的 NSGA-II 算法,并基于基准测试问题套件对其性能进行了三项指标验证。验证结果表明,与其他先进的优化算法相比,该算法生成的解集具有良好的分布均匀性,更接近真实边界,且具有良好的收敛性;此外,通过将多目标优化函数和运动学约束条件代入改进的 NSGA-II 算法,可以解决磨削时间、冲击和磨盘磨损的折中最小化问题。仿真和实验结果表明,优化后的测地线磨削路径在磨削精度、准确性、稳定性和效率方面都具有优越性和有效性。相比之下,多方向路径(如优化摆线路径)会产生不同的磨削接触力和不同的磨盘滑动速度,从而导致更复杂的材料去除情况,从而影响优化方案的准确性。
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来源期刊
Journal of Manufacturing Systems
Journal of Manufacturing Systems 工程技术-工程:工业
CiteScore
23.30
自引率
13.20%
发文量
216
审稿时长
25 days
期刊介绍: The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs. With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.
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