Integrating multiple adaptation results by utilization of support vector regression in case-based mechanical product design

Jin Qi

doi:10.1177/1687814018767681

Abstract

Case adaptation is crucial for a good and reasonable case-based design which is a common method in computer-aided design, because the solution of old case is not always the exact answer for the enc...

Highlights

Supporting the design of new mechanical product by recalling past experiences and adapting them to the current design requirement is the long cherished desire of designers, as designers rely heavily on past design experiences in actual design, rather than designing everything from scratch.[1]
To determine the statistical differences of performance measures among seven adaptation methods for each k-nearest neighbors (k-NNs) principle and solution feature adaptation, we carried out the analysis of variance (ANOVA) and Tukey’s honesty significant different (HSD) tests, and the corresponding results are described in Tables 8 and 9
To improve the adaptation performances of classic statistical feature-oriented adaptation (SFA), this article presents a hybrid adaptation method called as MSFA-Support vector regression (SVR)

Summary

Introduction

Supporting the design of new mechanical product by recalling past experiences and adapting them to the current design requirement is the long cherished desire of designers, as designers rely heavily on past design experiences in actual design, rather than designing everything from scratch.[1] This kind of approach is known as case-based design (CBD),[2] derived from the methodology of case-based reasoning (CBR). The concept of CBD focuses on the general idea of retaining a memory of previous design requirements and their solutions. How to perform adaptation by reference to k similar cases without having to excessively rely on manual adaptation still remains as challenging obstacles to the CBD system

Results

Discussion

Conclusion