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One 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.

Purpose The main purpose of this work is to explore the erosion wear characteristics of additively manufactured aluminium alloy. Additive manufacturing (AM), also known as three-dimensional (3D) manufacturing, is the process of manufacturing a part designed in a computer environment using different types of materials such as plastic, ceramic, metal or composite. Similar to other materials, aluminum alloys are also exposed to various wear types during operation. Production efficiency needs to be aware of its reactions to wearing mechanisms. Design/methodology/approach In this study, quartz sands (SiO2) assisted with oxide ceramics were used in the slurry erosion test setup and its abrasiveness on the AlSi10Mg aluminum alloy material produced by the 3D printer as selective laser melting (SLM) technology was investigated. Quartz was sieved with an average particle size of 302.5 µm, and a slurry environment containing 5, 10 and 15% quartz by weight was prepared. The experiments were carried out at the velocity of 1.88 (250 rpm), 3.76 (500 rpm) and 5.64 m/s (750 rpm) and the impact angles 15, 45 and 75°. Findings With these experimental studies, it has been determined that the abrasiveness of quartz sand prepared in certain particle sizes is directly related to the particle concentration and particle speed, and that the wear increases with the increase of the concentration and rotational speed. Also, the variation of weight loss and surface roughness of the alloy was investigated after different wear conditions. Surface roughness values at 750 rpm speed, 10% concentration and 75° impingement angle are 0.32 and 0.38 µm for 0 and 90° samples, respectively, with a difference of approximately 18%. Moreover, concerning a sample produced at 0°, the weight loss at 250 rpm at 10% concentration and 45° particle impact angle is 32.8 mg, while the weight loss at 500 rpm 44.4 mg, and weight loss at 750 rpm is 104 mg. Besides, the morphological structures of eroded surfaces were examined using the scanning electron microscope to understand the wear mechanisms. Originality/value The researchers verified that this specific coating condition increases the slurry wear resistance of the mentioned steel. There are many studies about slurry wear tests; however, there is no study in the literature about the quartz sand (SiO2) assisted slurry-erosive wear of AlSi10Mg alloy produced with AM by using SLM technology. This study is needed to fill this gap in the literature and to examine the erosive wear capability of this current material in different environments. The novelty of the study is the use of SiO2 quartz sands assisted by oxide ceramics in different concentrations for the slurry erosion test setup and the investigations on erosive wear resistance of AlSi10Mg alloy manufactured by AM.