Abstract
This paper proposes an evolutionary fuzzy neural network (EFNN) for tool wear prediction. Material chips are affected by the cutting conditions during the cutting process. Different tool wear statuses cause material chips to have different colors; thus, the color of a material chip can be a crucial factor in tool wear prediction. In this study, the cutting time and International Commission on Illumination (CIE) xy value were used as the input of the proposed EFNN, and the output was the predicted degree of tool wear. The experimental results indicate that the proposed EFNN with the dynamic group cooperative particle swarm optimization (PSO) algorithm resulted in a smaller mean absolute percentage error (2.83%) than did the backpropagation neural network (9.72%), PSO (7.42%), quantum-based PSO (8.59%), and cooperative PSO (4.09%) algorithms.
Highlights
Tool life prediction is one of the most important technologies in the machinery industry
This paper adopts the chip surface color as feature value which is able to correspond to the temperature of cutting procedure as well as reflect the current cutting status to further enhance the accuracy of tool wear prediction
The objective of this study is to propose an evolutionary fuzzy neural network (EFNN) for tool wear prediction
Summary
Tool life prediction is one of the most important technologies in the machinery industry. The researches on tool wear are extensive such as process parameters, down milling, up milling, cutting force measurement, and sensor signals from machine [3,4]. Using the sensor to measure tools of different materials will obtain different signal values In this way, the established tool wear prediction model is only for one type of tool. The contributions include (1) providing the chip chromaticity coordinate values as one of the feature vectors to predict the tool wear value; (2) proposing an EFNN to establish the tool wear prediction model; (3) designing a dynamic group cooperative particle swarm optimization (DGCPSO) which combines the concepts of dynamic group and cooperative to optimize network parameters. The chip color during the cutting process changes in the following order: yellow → brown → purple → blue → blue green
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