Hybrid similarity measure for retrieval in case-based reasoning systems and its applications for computer numerical control turret design

Abstract

Analyzing the history of design is helpful in creating the computer numerical control turret design. Knowledge acquired from reasonably organizing and reusing designs may contribute to establishing computer numerical control turret design tasks. This article presents the results of our study on the representation and reuse of computer numerical control turret design knowledge and highlights the application of a case-based reasoning method in the structure design of a computer numerical control turret. The primary step in case-based reasoning systems is case retrieval where the similarity measure plays a significant role. The objective of this study is to develop a new method for a hybrid similarity measure with five formats of attribute values: crisp symbols, crisp numbers, fuzzy numbers, fuzzy linguistic variables, and fuzzy intervals. First, a hybrid similarity measure for a mixture of crisp and fuzzy sets is proposed for retrieving cases. Subsequently, a synthesis weight is formed through the combination of the subjective weight and objective weight. The calculation formula of the global similarity, which can retrieve the proper historical case, can be established by combining the hybrid similarity measure and the synthesis weight measure accordingly. Finally, the hybrid similarity measure and weight assignment method were applied in a computer numerical control turret conceptual design case-based reasoning system. The results showed that the global similarity of these five attribute types and the distribution of weight coefficients could improve the accuracy of case retrieval, which would help designers achieve the goal of rapid design.

A review of the development and future challenges of case-based reasoning

“…(1) An improved method based on a hybrid similarity measure (Zheng et al 2019;Wang et al 2018) is developed mainly through the processing of attribute features to improve the accuracy of similarity calculation, such as adding other information or setting a variety of attribute value format (clear set and fuzzy set). Lin (2020) incorporated mixed metric scales such as interval and ratio scales into metric computation to retrieve design case similarity.…”

A Case Retrieval Strategy for Traffic Congestion Based on Cluster Analysis

“…Experiments show that the algorithm has better performance in terms of matching cases and matching accuracy. In Reference [7], the case similarity calculation methods of 5 di erent attributes are analyzed, and a mode of combining subjective weights and objective weights is put forward. A combination of local and global similarity calculation methods for di erent types of tra c congestion is adopted in Reference [8].…”

A case weighted similarity deep measurement method based on a self-attention Siamese neural network

“…In Ref. [15], combining the subjective weight with the objective weight was proposed to form the comprehensive weight and then the mixed similarity measure was combined with the comprehensive weight to establish the calculation formula of global similarity.…”