Chatter Stability Prediction and Process Parameters’ Optimization of Milling Considering Uncertain Tool Information

Abstract

Stability is the prerequisite of a milling operation, and it seriously depends on machining parameters and machine tool dynamics. Considering that the tool information, including the tool clamping length, feeding direction, and spatial position, has significant effects on machine tool dynamics, this paper presents an efficient method to predict the tool information dependent-milling stability. A generalized regression neural network (GRNN) is established to predict the limiting axial cutting depth, where the machining parameters and tool information are taken as input variables. Moreover, an optimization model is proposed based on the machining parameters and tool information to maximize the material removal rate (MRR), where the GRNN model is taken as the stability constraint. A particle swarm optimization (PSO) algorithm is introduced to solve the optimization model and provide an optimal configuration of the machining parameters and tool information. A case study has been developed to train a GRNN model and establish an optimization model of a real machine tool. Then, effects of the tool information on milling stability were discussed, and an origin-symmetric phenomenon was observed as the feeding direction varied. The accuracy of the solved optimal process parameters corresponding to the maximum MRR was validated through a milling test.

Stability Analysis of Milling Based on the Barycentric Rational Interpolation Differential Quadrature Method

“…Butcher et al introduced the Chebyshev polynomial method [19] and the Chebyshev collocation method [20] for milling stability analysis. Lin et al [21] established a generalized regression neural network model to predict the limiting axial cutting depth of the milling process.…”

Quality parameter adaptive optimization for spinning process using dynamic non-dominated sorting algorithm

“…Li et al [14] used the expected target variance as the measure target robustness criterion to conduct the process parameter uctuation analysis, and performed the parameter optimization with the robustness criterion and the prediction model as the tness function of parameter optimization. Lin et al [15] for the limit axial cutting depth of different process parameter optimization problem, put forward to establish a generalized regression neural network prediction model to predict the limit axial cutting depth, with the limit axial cutting depth as the milling stability evaluation index, milling stability, tool life, power, surface roughness as the optimization model constraints for process parameters optimization. Cao et al [16] aimed at the optimization of process parameters for small samples, used the hierarchical analysis method to establish the processing effect evaluation model, used the support vector regression to predict the feed amount and spindle speed, and nally solved the optimal parameters under different processing objectives based on the multi-target dragon y algorithm.…”