Learning Ostensive Knowledge of Shaft Parts based on CRF model

Abstract

During the process of mechanical design, there exit a large amount of knowledge and information that needs to be obtained by consulting and referencing past cases. This type of knowledge is called ostensive knowledge in philosophy, and belongs to a typical relational tacit knowledge. On the one hand, ostensive knowledge cannot be expressed through words, numbers, scientific formulas, and coding procedures. On the other hand, knowledge is parasitic in cases and cannot exist independently from them. This paper introduces ostensive knowledge to the mechanical design field for the first time, and points out that the learning model of ostensive knowledge must have the memories of cases and the ability of case-based reasoning. On this basis, this paper focuses on the shaft parts, and proposes an ostensive knowledge learning model for shaft parts based on the conditional random field model. Finally, an experiment is carried out using actual engineering cases to analyze and verify the model, which shows that the model has good memory and reasoning capabilities for shaft parts cases.