Study And Design On Milling Database Expert System

Metal cutting database is a CAD technology which is the combination of the rapidly developing modern computer information technology and mechanical processing technology. Metal cutting database has become a hot topic in the direction of manufacturing information in recent years. Milling process is a common mechanical cutting method. With the diversification of milling cutter and the higher quality requirements of milling, quick and accurate selection of milling tools as well as a reasonable choice of effective milling parameters become particularly important.In this thesis, an intelligent and data-rich milling expert system database is established, according to milling cutting tools of a large carbide cutting tools company. The system’s main purpose is to provide users a variety of milling and reliable information, such as tool types, tool geometry, tool system, cutting parameters, et al. This system can save processing time and get reasonable proposals of process parameters, which effectively increase productivity, reduce production costs, and promote applications of enterprise tools, to improve the competitiveness of enterprises.The architecture and framework of the milling database expert system is designed in this thesis, using the knowledge representation method which is the combination of object-oriented representation and rule-based production. A forward chaining inference mechanism is established, and then the system knowledge acquisition mechanism and data sources of milling are analyzed and described, resulting in the complete theoretical design which comprises of the integrated databases, knowledge base and inference engine. After the systematic research of the relationship between the milling tools, milling amount and milling process conditions, the milling database expert system software is developed, using object-oriented development tools Delphi 7.0. When users input milling field conditions, the system can rapidly recommend millers and reasonable optimization cutting parameters.