摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-一项关于机器学习的新研究现在可以获得。根据NewsRx Corresp Ondents从文莱加东传来的消息，研究称，“尽管影响砂带磨削性能和恶化的方面是已知的，”机械臂磨削过程中砂带磨损预测仍然具有挑战性，砂带表面粗颗粒的大量磨损严重影响刀具的完整性，降低了工件的表面质量。文莱达鲁萨兰国大学为这项研究提供了财政支持。我们的新闻记者引用了文莱达鲁萨兰国大学的研究，传统的使用特殊工具的磨损状态监测策略会导致制造生产过程的停止，而制造生产过程有时需要很长的时间，并且高度依赖于人的能力。砂带的动态磨损行为要求制造行业的加工过程配备智能决策方法。在本研究中，为了保持刀具的均匀运动，在机械臂末端执行器上安装砂带磨削对碳钢工件表面进行磨削，该系统集成了加速度传感器和力传感器，实时记录工件和刀具的振动和力，振动信号R反映砂带磨损程度，监测刀具的状态，采用KNeare St Neighbor(KNN)、支持向量机(SVM)、多层感知器(MLP)等机器学习算法对砂带磨削状态进行智能监测。对决策树(DT)进行了研究，利用DT、随机森林(RF)和XGBoost分别建立了具有最优超参数的机器学习模型，得到了平均测试精度最高的机器学习模型，同时DT和RF获得了最低的等待时间。基于决策树的分类器有望成为解决砂带磨削预测问题的一种有前景的模型。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news originating from Gadong, Brunei, by NewsRx corresp ondents, research stated, "Although the aspects that affect the performance and the deterioration of abrasive belt grinding are known, wear prediction of abrasi ve 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 th e tool and it reduces the surface quality of the finished products." Financial support for this research came from Universiti Brunei Darussalam. Our news journalists obtained a quote from the research from the University of B runei Darussalam, "Conventional wear status monitoring strategies that use speci al tools result in the cessation of the manufacturing production process which s ometimes takes a long time and is highly dependent on human capabilities. The er ratic wear behavior of abrasive belts demands machining processes in the manufac turing industry to be equipped with intelligent decision-making methods. In this study, to maintain a uniform tool movement, an abrasive belt grinding is instal led at the end-effector of a robotic arm to grind the surface of a mild steel wo rkpiece. Simultaneously, accelerometers and force sensors are integrated into th e system to record its vibration and forces in real-time. The vibration signal r esponses 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 gr inding conditions using several machine learning algorithms that include KNeare st Neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), a nd Decision Tree (DT) are investigated. The machine learning models with the opt imized hyperparameters that produce the highest average test accuracy were found using the DT, Random Forest (RF), and XGBoost. Meanwhile, the lowest latency wa s obtained by DT and RF. A decision-tree-based classifier could be a promising m odel to tackle the problem of abrasive belt grinding prediction."
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