On-line monitoring of flank wear in turning with multilayered feed-forward neural network

A multilayer feed-forward neural network (MLFF N-Network) algorithm is presented for on-line monitoring of tool wear in turning operations. The algorithm is based on the cutting conditions (cutting speed and feed rate) and measured cutting forces, which are used as inputs to a three-layer MLFF N-Network. The network is first trained using a set of workpiece material (P20 mold steel) and a tungsten carbide (H13A) cutting tool at various cutting conditions. The algorithm is later successfully verified on-line during turning of the same mold steel at conditions that differ from the data used in training. The algorithm is packaged in a software module, and integrated to an open Intelligent Machining Module used on industrial CNC systems.