Research on the acquisition and identification methods of critical microscopic quality characteristics in the manufacturing process

The study of microscopic quality characteristics (MQC) reveals the significant impact of the formation and fluctuation of subtle characteristics during the manufacturing process on the local performance of the product, which is essential for the optimization of process parameters and for the improvement of product quality and reliability. This paper, based on manufacturing process unit information, mines the systematic information of the process unit through the process constituent elements model and also constructs the feature thesaurus of different process domains by using the Function-Principle-Behaviour-Structure-Resource-Environment (FPBSRE) model. Intelligent acquisition and representation of MQC during machining are realized by using natural language processing (NLP) techniques and topological methods. Additionally, the Triangular fuzzy number (TFN)-Elimination Et Choice Translating Reality (ELECTRE)-Adversarial Interpretive Structure Modeling (AISM) method was used for the identification of critical microscopic quality characteristics (CMQC), and the practicality and validity of the method in high-precision blade processing were verified by sensitivity analysis and method comparison. This study provides a new perspective for the analysis of MQC and a strong technical support for the quality control of related manufacturing processes.