Dissociation artificial neural network for tool wear estimation in CNC milling

Tool wear in CNC milling is a gradual process which significantly affects product quality. Left unmonitored, it could increase risks of tool breakage, leading to losses due to scrap and equipment damage. A modular neural network (MNN), the dissociation artificial neural network (Dis-ANN), was proposed in this paper for tool wear prediction. The Dis-ANN consists of a modular structure constructed out of parallel ANN modules (referred to as the dissociation unit), connected to an intermediary. The output of each ANN module is dependent on input feature vectors formed from the concatenation of both previous and current feature values, allowing each module to account for feature trends. Dis-ANN was validated using the Slot Milling Dataset (collected in the University of Malaya workshop) and the 2010 PHM Data Challenge Dataset. The Slot Milling Dataset contains data in the form of images of machined workpiece surfaces and acoustic signals during milling. In order to account for uneven lighting in each workpiece surface image, image features were extracted by processing grey-level co-occurrence matrix–based texture descriptors of different non-overlapping sections within the same image. For model validation using the 2010 PHM Data Challenge Dataset, Dis-ANN was validated using features extracted from the dataset under two conditions — with and without the addition of random feature noise. Results showed Dis-ANN was better at learning complex non-linear relationships while showing higher robustness to input feature noise compared to linear regression, support vector regression, and monolithic ANN. Furthermore, the modular design of Dis-ANN facilitated model architecture optimization to minimize network redundancy.