Determination of optimum cutting parameters is one of the most essential tasks in process planning of metal parts. However, to achieve the optimal machining performance, the cutting parameters have to be regulated in real time. Therefore, utilizing an intelligent-based control system, which can adjust the machining parameters in accordance with optimal criteria, is inevitable. This article presents an intelligent adaptive control with optimization methodology to optimize material removal rate and machining cost subjected to surface quality constraint in finish turning of hardened AISI D2 considering the real condition of the cutting tool. Wavelet packet transform of cutting tool vibration signals is applied to estimate tool wear. Artificial intelligence techniques (artificial neural networks, genetic programming and particle swarm optimization) are used for modeling of surface roughness and tool wear and optimization of machining process during hard turning. Confirmatory experiments indicated that the efficiency of the proposed adaptive control with optimization methodology is 25.6% higher compared to the traditional computer numerical control turning systems.

确定最佳切削参数是金属零件工艺规划中最重要的任务之一。但是，为了获得最佳的加工性能，必须实时调整切削参数。因此，不可避免地要使用基于智能的控制系统，该系统可以根据最佳标准调整加工参数。本文提出了一种具有优化方法的智能自适应控制，可在考虑到切削刀具的实际情况的情况下，在硬化的AISI D2精加工中优化材料去除率和受表面质量约束的加工成本。应用切削刀具振动信号的小波包变换来估计刀具磨损。人工智能技术（人工神经网络，遗传编程和粒子群优化）用于在硬车削过程中对表面粗糙度和刀具磨损进行建模以及对加工过程进行优化。验证性实验表明，与传统的计算机数控车削系统相比，采用优化方法的自适应控制系统的效率高出25.6％。