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In machining process, the machining systems are usually described by the stable theoretical model. However, there are still various process errors existed. To recognize, compensate and eliminate the errors, a method of process system error tracing based on PSO is proposed to explore the errors existing in the machining process. The determined theoretical model is defined and actual measurement data samples are used to find the optimal solution, get the deviation value of each error sources. Finally, the process system error tracing of the Arc Cam is taken as an example to prove the stability and validity of proposed method.

Aiming to the layout structure design and performance prediction for globoidal cam machine, a dynamic performance prediction method for machine layout structure is proposed in this paper. With the method, the motion transmission and layout structure are determined based on the mapping rules between function and structure. The prediction model for dynamic performance is established based on BP neural network, which is used to optimize the dynamic performance of layout structure for globoibal cam machine.

Due to the demands of dynamic quality control in manufacturing process, a system framework of dynamic coupled quality control (DCQC) was proposed. The framework analyzed the coupled relation between quality attributes (QAs) of product, and established the weighted coupled network (WCN) model by defining the nodes properties formalized. According to the model, the coupled mechanism was analyzed and the monitoring technique of key quality points was put forward based on brittleness analysis and autocorrelation analysis. Furthermore, the dynamic quality improving mechanism was performed to realize the real-time control. Finally, an application analysis was presented to demonstrate the rationality and validity of the method.