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

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.

Cutting Forces during Inconel 718 Orthogonal Turn-Milling.

Inconel 718 is a material often used in the aerospace and marine industries due to its properties and ability to work in harsh environments. However, its machining is difficult, and therefore methods are sought to facilitate this process. One of such methods is turn-milling.

Experimental Investigation of Surface Roughness in Milling of Duralcan Composite.

In today's developing aircraft and automotive industry, extremely durable and wear-resistant materials, especially in high temperatures, are applied. Due to this practical approach, conventional materials have been superseded by composite materials. In recent years, the application of metal matrix composites has become evident in industry 4.

Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling.

This article deals with the phenomenon of tool wear prediction in face milling of aluminum matrix composite materials (AMC), class as hard-to-cut materials. Artificial neural networks (ANN) are one of the tools used to predict tool wear or surface roughness in machining. Model development is applicable when regression models do not give satisfactory results.

Prediction of Tool Wear Using Artificial Neural Networks during Turning of Hardened Steel.

The ability to effectively predict tool wear during machining is an extremely important part of diagnostics that results in changing the tool at the relevant time. Effective assessment of the rate of tool wear increases the efficiency of the process and makes it possible to replace the tool before catastrophic wear occurs. In this context, the value of the effectiveness of predicting tool wear during turning of hardened steel using artificial neural networks, multilayer perceptron (MLP), was checked.