The Research On Applying Adaptive Control To Grinding Process

Modern advanced manufacturing technique is developing towards to Flexible Manufacturing System (FMS), Computer Integration Manufacture System (CIMS) and Intelligent Manufacture System (IMS). The customer demands that the production performance be perfect, reliable, and possibly a one-time surface finish machining. So not only is the manufacturing system required to be with high efficiency and low cost of raw material and energy, but also the finished production is with high size accuracy, low surface roughness and surface integrality. The grinding result directly affects the final quality and performance of product because the grinding is usually the ultimate machining procedure. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. But the grinding process is non-linearity and random city with too many influence factors;the accuracy mathematical model cannot be built, and the traditional control method cannot resolve the control problem during the grinding. At present, adjusting the grinding process is strongly relied on the operator's experience and skills. Hence, adaptive controller, as one of the intelligent control scheme, is tried to apply in grinding process in this dissertation.In the conventional grinding process, the work piece velocity (Vw) and cut depth as well as circumferential speed of a grinding segment (Vs) are selected the values that can make sure no flaw is occurred in the work piece. As a result the machining cost and time fail to meet what they are desired. In this thesis, the cylindrical plunge grinding, which is one of the universal manners in the factory, is deeply studied. In order to make it low roughness, dimensional and shape error control, sharp tolerance, and optimum tool-life with minimum cost and time, the adaptive control theory is introduced to grinding process. In this control scheme, by using the recursive least squares algorithm, the uncertain parameter in these models are estimated on-line and the predictive models for grinding force and work piece burning as well as wheel wear are also established. Based on the minimum cost and time of process, the objective function is proposed. What is more, due to the fact of that the objective function is a kind of nonlinear function withconstraint condition;the nonlinear programming is introduced to calculate the optimal grinding parameters. At last, the optimization self-adaptive control system is used in this dissertation to optimize grinding parameters in a concrete case. The results are also analyzed and discussed.