铣削自适应控制系统中的工况识别技术

机械加工过程中的工况辨识对于机械加工自动化中控制、决策和故障诊断都十分重要，而且对于提高系统的智能化程度、提高机加工的生产效率及安全生产有重要意义。该文综合分析机械加工中的多种模式识别的新方法，提出了一种综合运用专家系统、神经元网络和模糊模式识别等多种智能技术的工况辨识模型。还提出了一种自学习的模糊评判算法，使模糊建模更简便。该工况辨识模型已成功的用于铣削适应控制系统，辨识结果为控制提供了重要的依据。

Machining pattern recognition in adaptive control for milling process

Machining pattern recognition is very important for control, decision and fault diagnosis in automatic machining, which can improve the intellectualization of machining system, reduce in process time and ensure quality and safety in production. On the basis of comprehensive analysis of new methods used in machining pattern recognition, this paper describes a method of machining pattern recognition, by means of comprehensive utilization of expert system, multiple artifical neural network and fuzzy set. A fuzzy judge method is also proposed. The weight matrixes of fuzzy juge are determined by self learning, which make it much easier to set up the fuzzy mode. The mode of machining pattern recognition has been successfully used in the adaptive control system for milling process and the result has been an important basis for control.