Prediction of tool wear width size and optimization of cutting parameters in milling process using novel ANFIS-PSO method

Abstract

<jats:p> In the process of intelligent manufacturing, appropriate learning algorithm and intelligent model are necessary. In this work, a novel learning algorithm named random vibration and cross particle swarm optimization algorithm was proposed. The proposed algorithm is used for the prediction and optimization of machining process. Tool wear is an important factor that affects the machined surface quality during machining process, so it is necessary to find qualified tool wear prediction model and obtain the best combination of machining parameters to prolong tool life. In this study, the adaptive network–based fuzzy inference system was established to predict the tool wear width size. The random vibration and cross particle swarm optimization algorithm was tested using benchmark functions, and the results showed that random vibration and cross particle swarm optimization algorithm is able to find global optimum. Compared with the adaptive network–based fuzzy inference system trained by particle swarm optimization algorithm and adaptive network–based fuzzy inference system trained by differential evolution models, the results showed that the adaptive network–based fuzzy inference system trained by random vibration and cross particle swarm optimization algorithm can give a more accurate predicted value for offline prediction of the tool wear width size. In order to obtain the best combinations of cutting parameters under different removal area, the multi-objective optimization based on random vibration and cross particle swarm optimization algorithm was established. The optimized cutting parameters were verified and could be accepted to prolong tool life and improve machining efficiency. </jats:p>

Multi-objective optimization of WC-12Co coating by high-velocity oxygen fuel spray using multiple regression-based weighted signal-to-noise ratio

“…The main objective of the ANOVA is to determine the degree of importance of a spray parameter (or factor) on each quality characteristic via a so-called “contribution percentage.” Accordingly, the most influential factor, which shows the maximum contribution percentage, can be identified. 23,24…”

Distributed deep learning enabled prediction on cutting tool wear and remaining useful life

“…To predict the wear conditions and RULs of cutting tools, force signals, power signals, sound signals, vibration signals, and acoustic emission signals have been widely collected. Machine learning algorithms, in particular, deep learning algorithms, have been increasingly developed to support relevant decision-making based on the signals [7]. An approach for tool wear prediction was designed based on a genetic algorithm and a backpropagation (BP) neural network [8].…”

“…Machine learning algorithms, in particular, deep learning algorithms, have been increasingly developed to support relevant decision-making based on the signals. 7 An approach for tool wear prediction was designed based on a genetic algorithm and a backpropagation (BP) neural network. 8 An approach integrating a CNN model and a stacked bi-directional/ unidirectional LSTM model was designed to predict the wear condition and RUL of a cutting tool.…”

Application of vibration singularity analysis, stochastic tool wear, and GPR-MOPSO hybrid algorithm to monitor and optimise power consumption in high-speed milling

“…Overview studies by Barbieri et al [15] shown that the particle swarm optimization (PSO) algorithm is a swarmbased intelligence optimization method that simulates the behavior swarm of birds. Xu et al [16] commented that it is an efficient algorithm for solving single-objective optimization problems and achieving satisfactory results. However, in numerical optimization problems and practical engineering applications, multiple objectives should be met simultaneously.…”