Partitioning around medoids as a systematic approach to generative design solution space reduction
This study explores an approach to generative design solution space reduction by offering a flexible, efficient, and accessible method by leveraging clustering techniques. The studied method of generative design solution space reduction uses clustering analysis with a combination of the Gower distan...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123022002146 |
| _version_ | 1798003294486921216 |
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| author | Michael Botyarov Erika E. Miller |
| author_facet | Michael Botyarov Erika E. Miller |
| author_sort | Michael Botyarov |
| collection | DOAJ |
| description | This study explores an approach to generative design solution space reduction by offering a flexible, efficient, and accessible method by leveraging clustering techniques. The studied method of generative design solution space reduction uses clustering analysis with a combination of the Gower distance matrix and partitioning around medoids in an iterative process. This iterative generative design solution space reduction method retains the originality of unique design solutions, while simultaneously reducing the quantity of design solutions presented to the user, theoretically improving cognitive function during the design process. Design originality is maintained since the clustering process groups similar designs into clusters, from which a systematic reduction of similar designs can be achieved, thereby leaving novel solutions from the design envelope. This paper presents this clustering approach in the context of an aircraft engine loading bracket with multiple nominal and continuous variables, however, this approach be transferred to other applications with similar variables. Further work can explore the relationship between reduced generative design solution spaces and human cognitive function. |
| first_indexed | 2024-04-11T12:05:30Z |
| format | Article |
| id | doaj.art-8202626d982e4ac3b9355320210628b9 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-11T12:05:30Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-8202626d982e4ac3b9355320210628b92022-12-22T04:24:44ZengElsevierResults in Engineering2590-12302022-09-0115100544Partitioning around medoids as a systematic approach to generative design solution space reductionMichael Botyarov0Erika E. Miller1Corresponding author.; Department of Systems Engineering, Colorado State University, Fort Collins, CO, 80523, USADepartment of Systems Engineering, Colorado State University, Fort Collins, CO, 80523, USAThis study explores an approach to generative design solution space reduction by offering a flexible, efficient, and accessible method by leveraging clustering techniques. The studied method of generative design solution space reduction uses clustering analysis with a combination of the Gower distance matrix and partitioning around medoids in an iterative process. This iterative generative design solution space reduction method retains the originality of unique design solutions, while simultaneously reducing the quantity of design solutions presented to the user, theoretically improving cognitive function during the design process. Design originality is maintained since the clustering process groups similar designs into clusters, from which a systematic reduction of similar designs can be achieved, thereby leaving novel solutions from the design envelope. This paper presents this clustering approach in the context of an aircraft engine loading bracket with multiple nominal and continuous variables, however, this approach be transferred to other applications with similar variables. Further work can explore the relationship between reduced generative design solution spaces and human cognitive function.http://www.sciencedirect.com/science/article/pii/S2590123022002146Cluster analysisGower distanceGenerative designSolution spaceDesign methodologyCognition |
| spellingShingle | Michael Botyarov Erika E. Miller Partitioning around medoids as a systematic approach to generative design solution space reduction Results in Engineering Cluster analysis Gower distance Generative design Solution space Design methodology Cognition |
| title | Partitioning around medoids as a systematic approach to generative design solution space reduction |
| title_full | Partitioning around medoids as a systematic approach to generative design solution space reduction |
| title_fullStr | Partitioning around medoids as a systematic approach to generative design solution space reduction |
| title_full_unstemmed | Partitioning around medoids as a systematic approach to generative design solution space reduction |
| title_short | Partitioning around medoids as a systematic approach to generative design solution space reduction |
| title_sort | partitioning around medoids as a systematic approach to generative design solution space reduction |
| topic | Cluster analysis Gower distance Generative design Solution space Design methodology Cognition |
| url | http://www.sciencedirect.com/science/article/pii/S2590123022002146 |
| work_keys_str_mv | AT michaelbotyarov partitioningaroundmedoidsasasystematicapproachtogenerativedesignsolutionspacereduction AT erikaemiller partitioningaroundmedoidsasasystematicapproachtogenerativedesignsolutionspacereduction |