Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization

Abstract

Tool wear condition significantly influences equipment downtime and machining precision, necessitating the exploration of a more accurate tool wear state identification technique. In this paper, the wavelet packet thresholding denoising method is used to process the acquired multi-source signals and extract several signal features. The set of features most relevant to the tool wear state is screened out by the support vector machine recursive feature elimination (SVM-RFE). Utilizing these selected features, we propose a tool wear state identification model, which utilizes an improved northern goshawk optimization (INGO) algorithm to optimize the support vector machine (SVM), hereby referred to as INGO-SVM. The simulation tests reveal that INGO demonstrates superior convergence efficacy and stability. Furthermore, a milling wear experiment confirms that this approach outperforms five other methods in terms of recognition accuracy, achieving a remarkable accuracy rate of 97.9%.

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“…The attitude of Northern goshawk in hunting is a smart process and its hunting policy has 2 steps. In the 1st phase, the prey is identified by the bird who chases the target with high velocity [ 37 ]. In the 2nd phase, the prey is hunted in a process of short tail-pursuit.…”

A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction

“…In recent years, with the popularity of machine learning and deep learning, new directions have opened up for research on cutting tools, and numerous related studies have sprung up using methods such as Artificial Neural Networks (ANNs) [ 9 , 10 ], Support Vector Machines (SVMs) [ 11 , 12 , 13 ], the Hidden Markov Model (HMM) [ 14 , 15 , 16 , 17 ], Gaussian Process Regression (GPR), etc. [ 18 , 19 ].…”