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
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
This paper discusses the application of neural adaptive control strategy to the problem of cutting force control in high speed end milling operations. The purpose of the paper is to present a reliable, robust neural controller aimed at adaptively adjusting feed-rate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate.
COBISS.SI-ID: 14702614
To increase productivity, a new adaptive learning control system in milling processes has been developed. Based on proposed control system which consists of neural network dynamics model of the process and fuzzy feedback control module. The developed control system can reduce the machining time, protect the cutting tool, and increase the cutting efficiency. The main advantage of this approach is that the use of an adaptive learning control of milling processes does not require a priori knowledge about the servo-loops and the milling process dynamics.
COBISS.SI-ID: 14723350
The original contribution of the research was the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network.
COBISS.SI-ID: 14606358