A parameter-extended case-based reasoning method based on a functional basis for automated experiential reasoning in mechanical product designs. (October 2021)

• Record Type: Journal Article
• Authors: Long, Xinjiani; Li, Haitao; Ren, Wen; Du, Yuefeng; Mao, Enrong; Ding, Ning
• Is Part Of: Advanced engineering informatics. Volume 50(2021)
• Publication Date: 2021-10
• Subjects: Automated experiential reasoning -- Ambiguous synthetic decision -- Implicit knowledge -- Support vector machine -- Case-based reasoning; ASDE Ambiguous synthetic decision of experts -- AER Automated experiential reasoning -- CBR Case-based reasoning -- RBR Rule-based reasoning -- KBE Knowledge-based engineering -- PECBR Parameter-extended case-based reasoning -- FFPM Function-flow-parameter model -- FFP-MS Function-flow-parameter matrix set -- FUP-M MFUP, Function-parameter matrix -- FLP-M MFLP, Flow-parameter matrix -- FUFL-M MFUFL, Function-flow matrix -- PEC-M MPEC, Parameter experiential correlation matrix -- FCA Feature-weighted case adaptation -- SVM Support vector machine -- WK-SVM Weighted kernel support vector machine -- OFW-M Optimal feature-weighted matrix -- DPSO Dynamic particle swarm optimization -- BP Back propagation neuron network -- CSVM Conventional support vector machine; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101409
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: A method was proposed to automate expert experiential reasoning. Expert experiential knowledge was described quantitatively. Correlation between parameters was extracted based on function and energy flow. Automation in experiential parameter solving without expert was achieved. Experiential knowledge was integrated into automated experiential reasoning. Abstract: The mechanical product design process involves much experiential reasoning which relies extensively on accumulated experience knowledge and ambiguous synthetic decision of experts (ASDE). This makes it hard to achieve the automated, intelligent and rapid design of mechanical products. Furthermore, due to the lack of consideration of experts' cognition of product functions and structures in the application of the current case-based reasoning (CBR) method in the field of automated experiential reasoning (AER), the parameter solving process is separated from ASDE. Aiming at improving the accuracy and intelligence level of AER in mechanical product design, this paper proposed a parameter-extended CBR (PECBR) method based on a functional basis by integrating ASDE into AER. The PECBR method mainly contains two parts: firstly, in order to acquire and quantitatively describe expert experiential knowledge to provide an effective basis for AER, a knowledge representation method integrating a function-flow-parameter matrix set (FFP-MS) using functional bases and a parameter experiential correlation matrix (PEC-M) extracted from FFP-MS were presented for mechanical products, where the FFP-MS characterized the operation of function and energy flow during the working process of products. An acquisition rule for FFP-MS was designed to extract the degree of correlation between each two parameters, in which the implicit knowledge hiding among functions, flows and parameters was mined to form PEC-M; secondly, to cope with the difficulty in integrating ASDE into AER, a feature-weighted case adaptation (FCA) method was proposed by adopting a presented weighted kernel support vector machine (WK-SVM) and dynamic particle swarm optimization (DPSO). The FCA method can achieve the intelligent and automated solving of product parameters through identifying PEC-M during the case adaptation process. Two case studies on two-stage reducers and corn huskers were carried out to demonstrate the validity of the PECBR method. Compared with other conventional CBR methods, PECBR method can derive a more accurate value of parameters in mechanical product designs especially in the case of limited similar cases.