An intelligent sustainability evaluation system of micro milling

Abstract Micro milling is widely used to manufacture complex miniature structure with high quality, and the related sustainability evaluation is a complicated multi-factor decision-making problem. This paper proposes an intelligent system for evaluating sustainability performance of micro milling process based on the principal component analysis (PCA) algorithm and the back propagation (BP) neural network. As a critical influence factor of sustainability evaluation, the non-linear micro cutting tool life can be predicted by integrating the particle filter (PF) algorithm and the long short-term memory (LSTM) network based on the stochastic tool wear. Then, the systematical sustainability assessment metrics of micro milling process are analyzed in the environmental, economic and social perspectives. Considering the nonlinearity and complexity of sustainability evaluation, the intelligent integrated PCA-BP evaluation method is used to improve the calculation efficiency and simply the evaluation process, in which the dimension of multiple sustainability evaluation factors is reduced by the PCA algorithm. The micro milling experiments with workpiece material Al6061 were conducted to validate the feasibility of the proposed intelligent evaluation methodology. The intelligent sustainability evaluation results agree with the traditional weighted sustainability performance index analysis on the basis of the manner “higher is better”. For the proposed intelligent integrated PCA-BP evaluation method, the training steps reduced from 65 times to 38 times and the prediction accuracy increased from 82.57% to 90.59% compared to the traditional BP network. The comparison results showed that the proposed intelligent integrated PCA-BP evaluation method can obtain the sustainability evaluation value automatically with high efficiency and practicability, and it also provides the decision-making base for the micro milling process optimization.