Bagga, Prashant J.Patel, K.M.Makhesana, M.A.Şirin, ŞenolKhanna, N.Krolczyk, G.M.Pala, A.D.2023-07-262023-07-2620230268-3768https://doi.org/10.1007/s00170-023-11137-2https://hdl.handle.net/20.500.12684/12453One of the essential elements of automated and intelligent machining processes is accurately predicting tool life. It also helps in achieving the goal of producing quality products with reduced production costs. This work proposes a computer vision-based tool wear monitoring and tool life prediction system using machine learning methods. Gradient-boosted trees and support vector machine (SVM) techniques are used to predict tool life. The experimental investigation on the CNC machine is conducted to study the applicability of the proposed tool wear monitoring system. Experiments are performed using workpiece material made of alloy steel and PVD-coated cutting inserts, and flank wear is monitored. An imaging system consisting of an industrial camera, lens, and LED ring light is mounted on the machine to capture tool wear zone images. Images are then processed by algorithms developed in MATLAB®. Boosted tree methods and the SVM methodology have 96% and 97% prediction accuracy, respectively. Validation tests are carried out to determine the accuracy of proposed models. It is observed that the prediction accuracy of boosted three and SVM is good, with a maximum error of 5.89% and 7.56%, respectively. The outcome of the study established that the developed system can monitor the tool wear with good accuracy and can be adopted in industries to optimize the utilization of tool inserts. © 2023, The Author(s).en10.1007/s00170-023-11137-2info:eu-repo/semantics/closedAccessCutting tool wearGradient-boosted treeMachine visionSupport vector machineTool lifeTurningAlloy steelComputer control systemsComputer visionForecastingSupport vector machinesTurningWear of materialsCutting tool wearGradient-boosted treeLife predictionsMachine-visionPrediction accuracySupport vectors machineTool lifeTool wearTool wear monitoringVision basedCutting toolsArticle2-s2.0-85148946451WOS:000940340200005Q1Q2