首页|Study Results from University of Brunei Darussalam Provide New Insights into Mac hine Learning (Use of Machine Learning Models In Condition Monitoring of Abrasiv e Belt In Robotic Arm Grinding Process)
Study Results from University of Brunei Darussalam Provide New Insights into Mac hine Learning (Use of Machine Learning Models In Condition Monitoring of Abrasiv e Belt In Robotic Arm Grinding Process)
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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."
GadongBruneiCyborgsEmerging Techno logiesMachine LearningRoboticsRobotsUniversity of Brunei Darussalam
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