Improving the machining dynamics performance and efficiency for complex channel part manufacturing by planning the tool orientations and feed rate sequence

Improving the machining dynamics performance and efficiency of machine tools is extremely important to finish the processing of complex channel parts with high quality in a short time. In this paper, a three-step frame is adopted to achieve this goal by planning the tool orientations and feed rate, which includes C-spaces determination, collaborative optimization of toolpath and feed rates, and the adjustment for over-limitation dynamic parameters. Firstly, a conical ball-end cutter is used and the feasible C-spaces computation method is provided based on the envelop theory. Secondly, a multi-objective mathematical model with multi-constraint conditions for optimizing the dynamics performance and machining efficiency is built. Thirdly, the solving algorithms for the model but simplified is designed to generate the optimal toolpath and initial variable feed rate sequence. To design the efficient algorithms, the Closed-region Parametric Encoding (CPE) method for data structure and the Parametric Data Algorithm Design (PDAD) for algorithm design are proposed, which can transform the geometric constraints into the parametric surfaces in C-space and compute with the continuous parameters to implement the adaptive local subdivision. The algorithms can automatically generate the different shape of toolpath curve to obtain better optimization results. Finally, considering the dynamic parameters limitation, an adjustment algorithm is designed to adjust the feed rates for reducing the over-limitation dynamic parameters. At the end of the paper, two sets of comparative experiments are given for verification. The first set experiment shows that our approach can efficiently improve the machining dynamics performance and efficiency for complex channel parts. The second set experiment shows that the solving algorithm designed by the CPE method and PDAD method has the better computation efficiency and optimization effect.