Modares Mechanical Engineering

Modares Mechanical Engineering

Prediction of Tool Wear using Experimental Studies and Artificial Neural Network in Hardened Steel Machining

Document Type : Original Research

Authors
Farshid Jafarian
Mohammad Meghdad Fallah
Sajad Dehghani
10.22034/mme.23.10.89
Abstract
The ability to predict tool wear during machining is a very important part of diagnosis, which makes it possible to replace the tool at the appropriate time. Therefore, in this research, the artificial neural network approach was used to predict tool wear. First, hardened steel 4140 was turned with uncoated cemented carbide tool TCMW 16T304 H13A and with input parameters including cutting speed, feed rate and machining time in three different levels and with constant cutting depth, and the amount of tool wear was measured. And the experimental test results were used to train and validate the artificial neural network. The optimal neural network architecture was obtained with 3 nodes in the input layer, two hidden layers with 12 and 36 nodes in the first and second hidden layers, and 1 node in the output layer to predict tool wear. The prediction values of the artificial neural network model were compared with the experimental results and the average error percentage of the validation data was calculated as 3.32%.
Keywords
artificial neural network
AISI 4140
Machining
Tool wear
Cutting Speed

Subjects

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Modares Mechanical Engineering
Volume 23, Issue 10
October 2023
Pages 89-93