Name
Abstract | ||
|---|---|---|
A transformable product can perform different functions or change functionality by changing its physical structure. It is formed by integration of different components whose states can be transformed each other. However, there is a lack of systematic methods to guide design of the transformable product. In order to improve the design efficiency of transformable products, a large number of products are studied in this research to build a case base of transformation parameters and transformation principles for the design knowledge. A systematic design process is proposed to apply the design knowledge. The transformation design problem is first mapped from the problem domain to the knowledge domain expressed by transformation parameters. A general solution is then obtained in the knowledge domain. A multi-classification support vector machine is used to train a model of the transformation recommendation based on transformation parameters. Finally, the general solution is mapped into the problem domain for the specific solution using the analogy design. The effectiveness of the method is verified in the design of a self-propelled boom sprayer. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.aei.2022.101638 | Advanced Engineering Informatics |
Keywords | DocType | Volume |
Product design,Transformable product,Knowledge-based method,Analogy design,Support vector machine | Journal | 52 |
ISSN | Citations | PageRank |
1474-0346 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinpu Zhang | 1 | 0 | 0.68 |
| Guozhong Cao | 2 | 4 | 4.68 |
| Qingjin Peng | 3 | 0 | 0.34 |
| Runhua Tan | 4 | 13 | 13.78 |
| Wei Liu | 5 | 11 | 21.81 |
| Huangao Zhang | 6 | 0 | 0.34 |