A cluster analysis method for materials selection
Materials have typically been selected based on the familiarities and past experiences of a limited number of designers with a limited number of materials. Problems arise when the designer is unfamiliar with new or improved materials, or production processes more efficient and economical than past c...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en |
| Published: |
Virginia Tech
2014
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10919/41497 http://scholar.lib.vt.edu/theses/available/etd-03122009-041254/ |
| Summary: | Materials have typically been selected based on the familiarities and past experiences of a limited number of designers with a limited number of materials. Problems arise when the designer is unfamiliar with new or improved materials, or production processes more efficient and economical than past choices. Proper utilization of complete materials and processing information would require acquisition, understanding, and manipulation of huge amounts of data, including dependencies among variables and "what-if" situations. The problem of materials selection has been addressed with a variety of techniques, from simple broad-based heuristics as guidelines for selection, to elaborate expert system technologies for specific selection situations. However, most materials selection methodologies concentrate only on material properties, leaving other decision criteria with secondary importance. Factors such as component service environment, design features, and feasible manufacturing methods directly influence the material choice, but are seldom addressed in systematic materials selection procedures.
This research addresses the problem of developing a systematic materials selection procedure that can be integrated with standard materials data bases. The three-phase methodology developed utilizes a group technology code and cluster analysis method for the selection. The first phase is of go/no go nature, and utilizes the possible service environment requirements of ferromagnetism and chemical corrosion resistance to eliminate materials from candidacy. In the second phase, a cluster analysis is performed on key design and manufacturing attributes captured in a group technology code for remaining materials. The final phase of the methodology is user-driven, in which further analysis of the output of the cluster analysis can be performed for more specific or subjective attributes. === Master of Science |
|---|