Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool

Journal title

Bulletin of the Polish Academy of Sciences: Technical Sciences




No 4

Publication authors


General Engineering ; Computer Networks and Communications ; Atomic and Molecular Physics, and Optics ; Artificial Intelligence ; Information Systems

Divisions of PAS

Nauki Techniczne


<jats:title>Abstract</jats:title><jats:p>Nowadays, finishing operation in hardened steel parts which have wide industrial applications is done by hard turning. Cubic boron nitride (CBN) inserts, which are expensive, are used for hard turning. The cheaper coated carbide tool is seen as a substitute for CBN inserts in the hardness range (45–55 HRC). However, tool wear in a coated carbide tool during hard turning is a significant factor that influences the tolerance of machined surface. An online tool wear estimation system is essential for maintaining the surface quality and minimizing the manufacturing cost. In this investigation, the cutting tool wear estimation using artificial neural network (ANN) is proposed. AISI4140 steel hardened to 47 HRC is used as a work piece and a coated carbide tool is the cutting tool. Experimentation is based on full factorial design (FFD) as per design of experiments. The variations in cutting forces and vibrations are measured during the experimentation. Based on the process parameters and measured parameters an ANN-based tool wear estimator is developed. The wear outputs from the ANN model are then tested. It was observed that as the model using ANN provided quite satisfactory results, and that it can be used for online tool wear estimation.</jats:p>


Polish Academy of Sciences




ISSN 0239-7528, eISSN 2300-1917