Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mochamad Denny Surindra, Gusti Ahmad Fanshuri Alfarisy, Wahyu Caesarendra, Mohamad Iskandar Petra, Totok Prasetyo, Tegoeh Tjahjowidodo, Grzegorz M. Królczyk, Adam Glowacz, Munish Kumar Gupta
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Abstract

Although the aspects that affect the performance and the deterioration of abrasive belt grinding are known, wear prediction of abrasive belts in the robotic arm grinding process is still challenging. Massive wear of coarse grains on the belt surface has a serious impact on the integrity of the tool and it reduces the surface quality of the finished products. Conventional wear status monitoring strategies that use special tools result in the cessation of the manufacturing production process which sometimes takes a long time and is highly dependent on human capabilities. The erratic wear behavior of abrasive belts demands machining processes in the manufacturing industry to be equipped with intelligent decision-making methods. In this study, to maintain a uniform tool movement, an abrasive belt grinding is installed at the end-effector of a robotic arm to grind the surface of a mild steel workpiece. Simultaneously, accelerometers and force sensors are integrated into the system to record its vibration and forces in real-time. The vibration signal responses from the workpiece and the tool reflect the wear level of the grinding belt to monitor the tool’s condition. Intelligent monitoring of abrasive belt grinding conditions using several machine learning algorithms that include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Decision Tree (DT) are investigated. The machine learning models with the optimized hyperparameters that produce the highest average test accuracy were found using the DT, Random Forest (RF), and XGBoost. Meanwhile, the lowest latency was obtained by DT and RF. A decision-tree-based classifier could be a promising model to tackle the problem of abrasive belt grinding prediction. The application of various algorithms will be a major focus of our research team in future research activities, investigating how we apply the selected methods in real-world industrial environments.

Abstract Image

在机械臂打磨过程中使用机器学习模型监测砂带状态
尽管影响砂带磨削性能和劣化的因素已众所周知,但对机械臂磨削过程中砂带的磨损进行预测仍是一项挑战。砂带表面粗粒的大量磨损会严重影响工具的完整性,并降低成品的表面质量。使用特殊工具的传统磨损状态监测策略会导致生产流程停止,这有时需要很长时间,而且高度依赖人力。砂带不稳定的磨损行为要求制造业的加工过程配备智能决策方法。在这项研究中,为了保持工具的均匀运动,在机械臂的末端执行器上安装了砂带磨削装置,以磨削低碳钢工件的表面。同时,加速度计和力传感器被集成到系统中,以实时记录其振动和力。来自工件和工具的振动信号反应反映了砂带的磨损程度,从而监测工具的状况。研究了使用 K-Nearest Neighbor (KNN)、Support Vector Machine (SVM)、Multi-Layer Perceptron (MLP) 和 Decision Tree (DT) 等几种机器学习算法对砂带磨削状况进行智能监控。使用 DT、随机森林(RF)和 XGBoost 找到了具有优化超参数的机器学习模型,这些模型能产生最高的平均测试准确率。同时,DT 和 RF 获得了最低的延迟。基于决策树的分类器可能是解决砂带磨削预测问题的一个有前途的模型。在未来的研究活动中,各种算法的应用将是我们研究团队的重点,我们将研究如何在实际工业环境中应用所选方法。
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来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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