{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T21:57:33Z","timestamp":1767650253365,"version":"3.41.2"},"reference-count":22,"publisher":"ASME International","issue":"2","content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2014,6,1]]},"abstract":"<jats:p>This paper compares two different methods of graph generation for input into the complexity connectivity method to estimate the assembly time of a product. The complexity connectivity method builds predictive models for assembly time based on 29 complexity metrics applied to the product graphs. Previously, the part connection graph was manually created, but recently the assembly mate method and the interference detection method have introduced new automated tools for creating the part connectivity graphs. These graph generation methods are compared on their ability to predict the assembly time of multiple products. For this research, eleven consumers products are used to train an artificial neural network and three products are reserved for testing. The results indicate that both the assembly mate method and the interference detection method can create connectivity graphs that predict the assembly time of a product to within 45% of the target time. The interference detection method showed less variability than the assembly mate method in the time estimations. The assembly mate method is limited to only solidworks assembly files, while the interference detection method is more flexible and can operate on different file formats including IGES, STEP, and Parasolid. Overall, both of the graph generation methods provide a suitable automated tool to form the connectivity graph, but the interference detection method provides less variance in predicting the assembly time and is more flexible in terms of file types that can be used.<\/jats:p>","DOI":"10.1115\/1.4026293","type":"journal-article","created":{"date-parts":[[2013,12,20]],"date-time":"2013-12-20T17:30:57Z","timestamp":1387560657000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":7,"title":["Comparison of Graph Generation Methods for Structural Complexity Based Assembly Time Estimation"],"prefix":"10.1115","volume":"14","author":[{"given":"Essam Z.","family":"Namouz","sequence":"first","affiliation":[{"name":"Research Assistant CEDAR Group, Department of Industrial Engineering, Clemson University, Clemson, SC 29634-0921 e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua D.","family":"Summers","sequence":"additional","affiliation":[{"name":"Professor CEDAR Group, Department of Mechanical Engineering, Clemson University, Clemson, SC 29634-0921 e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2014,2,26]]},"reference":[{"key":"2019100604491030600_B1","first-page":"60","article-title":"A Survey Report on Implementation of Design for Assembly (DFA) in Malaysian Manufacturing Industries","volume":"1","year":"2000","journal-title":"J. Mek."},{"issue":"3","key":"2019100604491030600_B2","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1108\/01445150810889961","article-title":"Design for Assembly","volume":"28","year":"2008","journal-title":"Assem. Autom."},{"volume-title":"Product Design for Manufacture and Assembly","year":"2011","key":"2019100604491030600_B3"},{"issue":"3","key":"2019100604491030600_B4","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1080\/095119299130281","article-title":"Integrated Knowledge-Based Approach and System for Product Design for Assembly","volume":"12","year":"1999","journal-title":"Int. J. Comput. Integr. Manuf."},{"issue":"14","key":"2019100604491030600_B5","first-page":"29","article-title":"Design for Assembly-A Key Element Within Design for Manufacture","volume":"203","year":"1989","journal-title":"Arch. Proc. Inst. Mech. Eng. Part D Transp. Eng. 1984\u20131988 (vols 198\u2013202)"},{"article-title":"Design for Assembly Versus Design for Disassembly: A Comparison of Guidelines","volume-title":"ASME","year":"2003","key":"2019100604491030600_B6"},{"issue":"1","key":"2019100604491030600_B7","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1080\/07408179508936718","article-title":"A Constraint Network Approach to Design for Assembly","volume":"27","year":"1995","journal-title":"IIE Trans."},{"issue":"7","key":"2019100604491030600_B8","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/S0261-3069(02)00050-X","article-title":"Towards More Strategic Product Design for Manufacture and Assembly: Priorities for Concurrent Engineering","volume":"23","year":"2002","journal-title":"Mater. Des."},{"volume-title":"Mechanical Assemblies: Their Design, Manufacture, and Role in Product Development, Knovel","year":"2004","key":"2019100604491030600_B9"},{"volume-title":"Design for Manufacturing","year":"2001","key":"2019100604491030600_B10"},{"volume-title":"Methods-Time Measurement","year":"1948","key":"2019100604491030600_B11"},{"key":"2019100604491030600_B12","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1080\/0951192X.2012.684706","article-title":"Estimating Assembly Time With Connective Complexity Metric Based Surrogate Models","volume":"26","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"2019100604491030600_B13","doi-asserted-by":"crossref","unstructured":"Owensby, E., Shanthakumar, A., Rayate, V., Namouz, E. Z., Summers, J. D., and Owensby, J. E., 2011, \u201cEvaluation and Comparison of Two Design for Assembly Methods: Subjectivity of Information,\u201d ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 28\u201331. 2011, Washington, DC, Paper No. DETC2011\u201347530, pp. 721\u2013731.10.1115\/DETC2011-47530","DOI":"10.1115\/DETC2011-47530"},{"key":"2019100604491030600_B14","doi-asserted-by":"crossref","unstructured":"Owensby, J. E., Namouz, E. Z., Shanthakumar, A., and Summers, J. D., 2012, \u201cRepresentation: Extracting Mate Complexity From Assembly Models to Automatically Predict Assembly Times,\u201d ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 12\u201315, 2012, Chicago, IL, Paper No. DETC2012\u201370995, pp. 383\u2013392.10.1115\/DETC2012-70995","DOI":"10.1115\/DETC2012-70995"},{"key":"2019100604491030600_B15","first-page":"77","article-title":"Complexity Connectivity Metrics-Predicting Assembly Times With Abstract Assembly Models","volume-title":"Smart Product Engineering","year":"2013"},{"key":"2019100604491030600_B16","doi-asserted-by":"crossref","unstructured":"Miller, M., Mathieson, J., Summers, J. D., and Mocko, G. M., 2012, \u201cRepresentation: Structural Complexity of Assemblies to Create Neural Network Based Assembly Time Estimation Models,\u201d ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 12\u201315, 2011, Chicago, IL, Paper No. DETC2012\u201371337, pp. 99\u2013109.10.1115\/DETC2012-71337","DOI":"10.1115\/DETC2012-71337"},{"key":"2019100604491030600_B17","doi-asserted-by":"crossref","unstructured":"Mathieson, J. L., and Summers, J. D., 2010, \u201cComplexity Metrics for Directional Node-Link System Representations: Theory and Applications,\u201d ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 15\u201318, 2010, Montreal, Canada, Paper No. DETC2010\u201328561, pp. 13\u201324.10.1115\/DETC2010-28561","DOI":"10.1115\/DETC2010-28561"},{"key":"2019100604491030600_B18"},{"issue":"10","key":"2019100604491030600_B19","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1109\/5.58346","article-title":"Entropy Nets: From Decision Trees to Neural Networks","volume":"78","year":"1990","journal-title":"Proc. IEEE"},{"issue":"3","key":"2019100604491030600_B20","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S0168-1699(99)00046-0","article-title":"Comparative Accuracies of Artificial Neural Networks and Discriminant Analysis in Predicting Forest Cover Types From Cartographic Variables","volume":"24","year":"1999","journal-title":"Comput. Electron. Agric."},{"volume-title":"Probability and Statistics for Engineers and Scientists","year":"2012","key":"2019100604491030600_B21"},{"volume-title":"Applied Statistics and Probability for Engineers","year":"2010","key":"2019100604491030600_B22"}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/doi\/10.1115\/1.4026293\/6099735\/jcise_014_02_021003.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/doi\/10.1115\/1.4026293\/6099735\/jcise_014_02_021003.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T08:22:18Z","timestamp":1746087738000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/doi\/10.1115\/1.4026293\/371445\/Comparison-of-Graph-Generation-Methods-for"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,2,26]]},"references-count":22,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2014,6,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4026293","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"type":"print","value":"1530-9827"},{"type":"electronic","value":"1944-7078"}],"subject":[],"published":{"date-parts":[[2014,2,26]]},"article-number":"021003"}}