Automated Graph Grammar Generation for Engineering Design With Frequent Pattern Mining
- Additional Document Info
- View All
© Copyright 2017 ASME. Graph grammars, a technique for formulating new graphs based on a set of rules, is a very powerful tool for computational design synthesis. It is particularly suitable for discrete categorical data where principal component analysis is generally not applicable. Furthermore, this technique utilizes three different programs in conjunction with a design repository, which is opposed to traditional methods that require experts to empirically derive graph grammars. This technique can be separated into three steps. These steps are the creation of the input, graph data mining, and interpretation of the output with the intention of these steps being to automate or assist an expert with the process of extracting engineering graph grammars. Graph grammars that can then serve as guidelines during concept generation. The results of this paper show that this technique is very applicable to computational design synthesis by testing only a small number of products and still producing tangible results that coincide with empirically derived graphs. Fifty electromechanical products from the design repository are used in this study. When comparing, the machine generated grammar rules with expert derived grammar rules, it can be seen that only 14% cannot be developed, 58% cannot be mined with the current setup and 28% were mined with the current set up. However, it is important to keep in mind a few considerations. Specifically, the technique does not replace the expert. Instead, the technique acts as more of an aid than a replacement. Also, while this technique has great potential in regards to computational design synthesis, it is limited to the products in the design repository and the current implementation of the aforementioned programs. Despite these minor considerations, this work proposes application of graph data mining to derive engineering grammars.
author list (cited authors)
Sangelkar, S., & McAdams, D. A.