{"title":"Preliminary User Study on Design Heuristics for Additive Manufacturing","authors":"Alexandra Blösch-Paidosh, K. Shea","doi":"10.1115/DETC2018-85908","DOIUrl":null,"url":null,"abstract":"Additive manufacturing (AM) has unique capabilities when compared to traditional manufacturing, such as shape, hierarchical, functional, and material complexity, a fact that has fascinated those in research, industry, and the media for the last decade. Consequently, designers would like to know how they can incorporate AM’s special capabilities into their designs, but are often at a loss as to how to do so. Design for Additive Manufacturing (DfAM) methods are currently in development but the vast majority of existing methods are not tailored to the needs and knowledge of designers in the early stages of the design a process. The authors have previously derived 29 design heuristics for AM. In this paper, the efficacy of these heuristics is tested in the context of a re-design scenario with novice designers. The preliminary results show that the heuristics positively influence the designs generated by the novice designers. Analysis of the use of specific heuristics by the participants and future research to validate the impact of the design heuristics for additive manufacturing with expert designers and in original design scenarios is planned.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 2A: 44th Design Automation Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/DETC2018-85908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Additive manufacturing (AM) has unique capabilities when compared to traditional manufacturing, such as shape, hierarchical, functional, and material complexity, a fact that has fascinated those in research, industry, and the media for the last decade. Consequently, designers would like to know how they can incorporate AM’s special capabilities into their designs, but are often at a loss as to how to do so. Design for Additive Manufacturing (DfAM) methods are currently in development but the vast majority of existing methods are not tailored to the needs and knowledge of designers in the early stages of the design a process. The authors have previously derived 29 design heuristics for AM. In this paper, the efficacy of these heuristics is tested in the context of a re-design scenario with novice designers. The preliminary results show that the heuristics positively influence the designs generated by the novice designers. Analysis of the use of specific heuristics by the participants and future research to validate the impact of the design heuristics for additive manufacturing with expert designers and in original design scenarios is planned.