Experimental Investigation of Tool Wear and Induced Vibration in Turning High Hardness Aisi52100 Steel Using Cutting Parameters and Tool Acceleration

Abstract

In machining of high hardness steel, vibration of cutting tool increases tool wear which reduces its life. Tool wear is catastrophic in nature and hence investigation of its assessment is important. This study investigates experimentally induced vibration during turning of hardened AISI52100 steel of hardness 54±2 HRC using coated carbide insert. In this context, cutting tool acceleration is measured and used to develop a novel mathematical model based on acquired real time acceleration signals of cutting tool. The obtained model is validated as R2= 0.93 while its residuals values closely follow the straight line. The predictions are confirmed by conducting conformity test which revealed a close degree of agreement with respect to the experimental values. The Artificial Neural Network (ANN) examination is performed to determine the model regression value. The study shows that the examined reports forecasts of ANN are more exact than regression analysis. The future directon of this investigation is towards developing a low-cost microcontroller-based hardware unit for in-process tool wear monitoring which could be beneficial for small scale industries.

Identification of Tool Wear During Cast Iron Drilling Using Machine Learning Methods

“…Tool life tests of drilling were carried out for the following parameters: Only the variable cutting speed is analyzed in the article, as it has the greatest impact on tool wear and vibration acceleration [11,12]. After every 50 holes done, the tool wear was measured as determined by the VB C coeffi cient -fl ank wear at nose radius corner, as shown in fi gure 3.…”

“…The VCPSO algorithm was adopted for multi-objective optimization of milling parameters. In [11], the researchers proposed a mathematical model (RA) and a model based on neural networks (ANN) with two hidden layers based on the Levenberg-Marquardt algorithm. The models were used to predict the value of tool wear (VB) when turning AISI 52100 hardened steel with carbide inserts.…”