1.

Adaptive network based inference system for estimation of flank wear in end-milling

The focus of this paper is to develop a reliable method to predict flank wear during end-milling process. A neural-fuzzy scheme is applied to perform the prediction of flank wear from cutting force signals. In this contribution we also discussed the construction of a ANFIS system that seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the neural network. The estimation error is up to 3% by using neural network trained with backpropagation algorithm.

COBISS.SI-ID: 12406806

2.

Particle swarm intelligence based optimisation of high speed end-milling.

This study has presented multi-objective optimization of milling process by using neural network modelling and Particle swarm optimization. A neural network model was used to predict cutting forces during machining and PSO algorithm was used to obtain optimum cutting speed and feed rate. The experimental results show that the MRR is improved by 28%. Machining time reductions of up to 20% are observed. This paper opens the door for a new class of EC based optimization techniques in the area of machining.

COBISS.SI-ID: 13224214