Intelligent optimization of grinding processes using fuzzy logic

Abstract

This paper presents a generalized intelligent grinding advisory system (GIGAS) for the optimization of the following three grinding processes: straight-cut surface grinding, internal and external cylindrical plunge grinding. The framework of GIGAS is based on model-based fuzzy logic. The main feature of GIGAS is that it can interactively accept several different process models pertaining to a specific grinding process, as well as heuristic rules. To this end, it uses generalized process models for the grinding force, the grinding power, the maximum chip thickness, the surface roughness, the grinding ratio, the effective dullness of the wheel and the grinding temperature. The scheme allows the user to change interactively the process models used by GIGAS for optimization and hence can accommodate a large number of grinding conditions. It is also demonstrated that accurate solutions can be obtained in the order of several seconds using fuzzy inferencing, thereby showing the possibility of real-time control. The performance of GIGAS is tested in comparison with a known conventional method of optimization of the internal cylindrical plunge grinding process.