Cognitive shape similarity assessment for 3D part search

Chih-Hsing Chu (), Cheng-Hung Lo and Han-Chung Cheng
Additional contact information
Chih-Hsing Chu: National Tsing Hua University
Cheng-Hung Lo: Xi’an Jiaotong-Liverpool University
Han-Chung Cheng: National Tsing Hua University

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 7, No 13, 1679-1694

Abstract: Abstract Mass customization aims to satisfy diverse customer requirements with high product variety while maintaining reasonable manufacturing cost and lead time. Allowing customers to perceive product differentiation is a critical factor for most design methods developed for mass customization. This study examines 3D part search from the human cognitive perspective. We designed and conducted a quasi-factorial experiment to understand how structured variations of four factors—the shape, type, dimension, and location of the feature volume of a part model—affect human judgment of part similarity. The corresponding factorial similarity values were computed with different shape signatures in the form of the feature adjacency graph. The human responses were obtained by paired comparisons of test parts, and quantified as the cognitive similarity. Statistical analysis of the experimental results showed that the type and shape factors played an important role in the subjects’ judgments. Back-propagation neural networks were trained to model the correlations between the cognitive and the factorial similarity values. The performance of the networks validates our idea of incorporating human cognition into assessment of 3D part similarity. This study presents a systematic approach for personalized part search that reflects individual perception of shape similarity.

Keywords: Part similarity; Part search; Shape cognition; Product variety (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-016-1211-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:28:y:2017:i:7:d:10.1007_s10845-016-1211-4

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-016-1211-4

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().