Knowledge component-based intelligent method for fixture design

A Correlation-Experience-Demand Based Personalized Knowledge Recommendation Approach

“…Each task has one and only one corresponding design process, and its subtasks are included in the nodes of the design process. The knowledge ontology model and the designer ontology model use ontology technology [27] to integrate the information from both knowledge and designer. The mathematical model of the knowledge recommendation algorithm consists of three submodules, namely, the degree of correlation, the degree of demand and the degree of assistance, and the model is used to evaluate the knowledge and build the knowledge recommendation.…”

“…Each form of the forgetting curve can reach the same precision if the coefficients are properly selected. Considering multiple factors such as function complexity, the exponential function is selected to create the forgetting curve in this paper, as shown in (27). + c − a + a r ε t (27) where P(t, t ) is the proportionality coefficient of the correctly memorized information of a piece of knowledge for a designer to the total information of the knowledge at moment t; t is the previous moment that the designer accesses the knowledge; t is a coefficient relevant to the memory ability of the designer; c is the time that the designer has access to the knowledge; a is the age of the designer; a r is the maximum age of designers and can be taken as the retirement age; ε t is the weight of logic for different types of knowledge.…”

“…Combining (27) with (28), the index of mastery for a designer to k l at moment t is defined in (29).…”

“…They built a cooperative intention capture model and then built a knowledge recommendation system that met the requirements of collaboration through a text content matching algorithm. Similarly, Zhang et al [27] constructed a knowledge component model, including knowledge ontology, an inference engine and an evaluation method based on entropy weights to optimize matching results. The model was successfully applied to a plane parts design task.…”

Computer-Aided Design and Optimization of Fixtures for Plastic Parts Machining

“…In the domain of development and application of systems for automated fixture design numerous results have been published [22][23][24]. Other fixture related work include fuzzy based fixture design [25], reuse based fixture design [26], component based fixture design [27], functional based fixture design [28], virtual reality based fixture design [29], knowledge based fixture design [30], ontology based fixture design [31], neural network fixture design [32], etc. None of them, however, met all the requirements.…”