Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

Abstract

The dynamic development of new technologies enables the optimal computer technique choice to improve the required quality in today’s manufacturing industries. One of the methods of improving the determining process is machine learning. This paper compares different intelligent system methods to identify the tool wear during the turning of gray cast-iron EN-GJL-250 using carbide cutting inserts. During these studies, the experimental investigation was conducted with three various cutting speeds vc (216, 314, and 433 m/min) and the exact value of depth of cut ap and federate f. Furthermore, based on the vibration acceleration signals, appropriate measures were developed that were correlated with the tool condition. In this work, machine learning methods were used to predict tool condition; therefore, two tool classes were proposed, namely usable and unsuitable, and tool corner wear VBc = 0.3 mm was assumed as a wear criterium. The diagnostic measures based on acceleration vibration signals were selected as input to the models. Additionally, the assessment of significant features in the division into usable and unsuitable class was caried out. Finally, this study evaluated chosen methods (classification and regression tree, induced fuzzy rules, and artificial neural network) and selected the most effective model.

The analysis of the effect of varying feed values in each pass on tool wear and surface roughness in turning of AISI 4140

“…9 Additionally, it is crucial to monitor tool wear to prevent excessive tool wear or breakage. 10,11 Elbah et al 12 investigated the effect of conventional and wiper inserts on surface roughness (Ra, Rz, and Rt) on hardened AISI 4140 materials. Three different cutting speeds, feeds, and depths of cut were tested in the experiments.…”

“…9 Additionally, it is crucial to monitor tool wear to prevent excessive tool wear or breakage. 10,11…”

Methodology for Tool Wear Detection in CNC Machines Based on Fusion Flux Current of Motor and Image Workpieces

“…Likewise, vibration signals are one of the most exploited measurements for wear detection. Tabaszewski et al [5] used triaxial accelerometers to distinguish between two tool states at different cutting speeds during turning of EN-GJL-250 with carbide cutting inserts using different intelligent techniques and selecting the most appropriate one, which was a classification and regression tree (CART) with a 2.06% error. Patange et al [6] used an accelerometer and machine learning (ML) techniques based on trees to classify six types of wear under fixed machining parameters while turning a stainless steel workpiece on a manual lathe; the authors performed a statistical feature extraction, selection and classification, obtaining the best results with a random forest (RF) model, with 92.6% accuracy.…”

“…Several literature reviews have identified the main techniques, tools and trends for processing [21][22][23][24]. Firstly, signals are processed directly in the time domain [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] or techniques or transforms are used for their analyses in the frequency [3,5,[18][19][20] or time-frequency [19,20] domains. From here, several techniques are used to obtain features that allow the analysis to be carried out in a better way, such as the use of statistical indicators [3][4][5][6]8,14,[18][19][20], time or time-frequency transforms for direct feature extraction [3][4][5]19,20] and, in some cases, methods for the selection of the most appropriate features or dimensionality reduction such as heuristic techniques [5,6,22], linear discriminant analysis (LDA) [19] or principal component analysis (PCA).…”

Monitoring of Cutting Process and Tool Condition of Metal and Metal Composite

“…Starting from classical machining, Tabaszewski, Twardowski, et al [ 23 ] compared different intelligent system methods to identify the tool wear during this kind of machining. They made their tests on gray cast iron using carbide cutting inserts.…”