Research On Expert System For NC Milling Process Parameters Optimization

In modern manufacturing, reasonable selection of NC machining process parameters directly affects the manufacture enterprises’production efficiency and their economic benefits. But at present, selecting cutting parameters is often too conservative for many enterprises in their machining production, resulting in increase of production cycle and waste of manufacturing resources. It is unfavorable to maximize the interests of production. Restricted by specific processing conditions, how to optimize NC milling parameters is always a non-linear problem with complexity and many constraints. And it is difficult to obtain optimal milling parameters by using traditional mathematical methods. Selection of optimal milling parameters has become an extremely important problem in current machinery manufacturing.This paper used quantum-behaved particle swarm optimization (QPSO) algorithm to optimize milling parameters. Also in order to improve machining efficiency and reduce production cost, this intelligent optimization algorithm was incorporated into expert system and then milling parameters optimization expert system was developed to select the process parameters reasonably such as cutting tool, milling parameters etc. The main contents of this paper are as follows:First of all, by analyzing system requirements, every function module of this system was determined and an overall structure of milling parameters optimization expert system based on QPSO algorithm was designed. At the same time, appropriate technology and tools which were required to build this system were selected.Secondly, based on basic theory of expert system, process database of system was established by analyzing milling process. Then milling engineering knowledge base was established on the basis of process database, which used production method and process representation method to express knowledge in the field of milling. And hybrid reasoning method which was on basis of both rule-based reasoning and case-based reasoning was used as system’s reasoning mechanism to achieve effective reasoning of cutting tool, cutting fluid and milling parameters.Thirdly, according to metal cutting theory and optimization theory, milling parameter optimization mathematical model was established which took milling parameters as design variables and was constrained to processing conditions such as machine tool, cutting tool, workpiece and so on. QPSO algorithm was applied to optimize this model in order to achieve optimization of milling parameters. The effectiveness of the QPSO algorithm for model optimization was verified by the living examples. Using optimized milling parameters could get lower cost and higher machining efficiency on the premise of ensuring processing quality.Finally, milling parameters optimization based on QPSO algorithm was combined with inference mechanism of expert system. Object-oriented programming was used to develop milling parameter optimization expert system with VC++6.0and SQL Server2000as development tools. So that after entering processing conditions, system can deduce suited cutting tool, cutting fluid and milling parameters, and can optimize milling parameters by using QPSO algorithm, to provide the most reasonable feed per tooth, cutting speed and cutting depth and other parameters.