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Milling is one, if not the most important, manufacturing process based on machining in today’s industry. Since this is a continuous process, mechanical tool wear becomes an active concern that affects the final product quality. Tool condition monitoring (TCM) is critical in milling to enhance product quality and prevent tool failures, while tool wear estimation is the first step to achieving a complete system. Several intelligent models have been proposed in the literature to estimate mechanical tool wear, but the use of different combinations of recent algorithms in a hybrid way has not been entirely explored. This study introduces a hybrid tool wear estimation model for milling, combining an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Micro Evolutionary Particle Swarm Optimization (MEPSO) on both antecedent and consequent parameters. MEPSO is a recent modification of the classic particle swarm optimization that has not yet been applied in tool wear estimation or in a hybrid setting with ANFIS. The proposed hybrid ANFIS-MEPSO model addresses tool wear prediction challenges by optimizing ANFIS parameters through MEPSO while employing a feature selection method in the ANFIS-MEPSO inputs. A dataset containing experimental milling data, including vibration, acoustic emission, and electrical current signals, was used to train and test the model with 8 features extracted and a feature selection algorithm to estimate tool wear, achieving promising results with MSE, MAE, and R2 metrics of 0.0045, 0.0578, and 0.9058 on testing data. A sensitivity analysis was conducted to better understand the changes in performance by modifying the optimization hyperparameters. It was revealed that stable and optimal performance occurred with σ and γ between 0.6–0.8 and at least 20 particles, while higher values (0.9) led to inconsistent results. This research highlights ANFIS-MEPSO’s viability for tool wear monitoring in milling, suggesting potential for broader application and further refinement.
Sousa et al. (Thu,) studied this question.