{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:36:43Z","timestamp":1775561803260,"version":"3.50.1"},"reference-count":41,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2018,8,28]],"date-time":"2018-08-28T00:00:00Z","timestamp":1535414400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[2019,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper, we report on a data analysis process for the automated classification of mechanical components. In particular, here, we describe, how to implement a machine learning system for the automated classification of parts belonging to several sub-categories. We collect models that are typically used in the mechanical industry, and then we represent each object by a collection of features. We illustrate how to set-up a supervised multi-layer artificial neural network with an ad-hoc classification schema. We test our solution on a dataset formed by 2354 elements described by 875 features and spanned among 15 sub-categories. We state the accuracy of classification in terms of average area under ROC curves and the ability to classify 606 unknown 3D objects by similarity coefficients. Our parts\u2019 classification system outperforms a classifier based on the Light Field Descriptor, which, as far as we know, actually represents the gold standard for the identification of most types of 3D mechanical objects.<\/jats:p>","DOI":"10.1017\/s0890060418000197","type":"journal-article","created":{"date-parts":[[2018,8,28]],"date-time":"2018-08-28T11:58:48Z","timestamp":1535457528000},"page":"100-113","source":"Crossref","is-referenced-by-count":19,"title":["A methodology for part classification with supervised machine learning"],"prefix":"10.1017","volume":"33","author":[{"given":"Matteo","family":"Rucco","sequence":"first","affiliation":[]},{"given":"Franca","family":"Giannini","sequence":"additional","affiliation":[]},{"given":"Katia","family":"Lupinetti","sequence":"additional","affiliation":[]},{"given":"Marina","family":"Monti","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2018,8,28]]},"reference":[{"key":"S0890060418000197_ref41","first-page":"856","article-title":"Feature selection for high-dimensional data: a fast correlation-based filter solution","volume":"3","author":"Yu","year":"2003","journal-title":"ICML"},{"key":"S0890060418000197_ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-3035-4"},{"key":"S0890060418000197_ref37","doi-asserted-by":"crossref","unstructured":"Wang F , Kang L and Li Y (2015) Sketch-based 3d shape retrieval using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1875\u20131883). IEEE.","DOI":"10.1109\/CVPR.2015.7298797"},{"key":"S0890060418000197_ref25","doi-asserted-by":"publisher","DOI":"10.1145\/571647.571648"},{"key":"S0890060418000197_ref26","doi-asserted-by":"publisher","DOI":"10.1108\/EC-03-2013-0082"},{"key":"S0890060418000197_ref28","doi-asserted-by":"publisher","DOI":"10.1631\/jzus.C1300185"},{"key":"S0890060418000197_ref10","doi-asserted-by":"crossref","unstructured":"Fodor IK (2002) A survey of dimension reduction techniques. Technical Report, Lawrence Livermore National Laboratory, Livermore.","DOI":"10.2172\/15002155"},{"key":"S0890060418000197_ref33","doi-asserted-by":"publisher","DOI":"10.1037\/1082-989X.9.2.164"},{"key":"S0890060418000197_ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2015.04.002"},{"key":"S0890060418000197_ref40","doi-asserted-by":"publisher","DOI":"10.1080\/16864360.2005.10738325"},{"key":"S0890060418000197_ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2007.10.012"},{"key":"S0890060418000197_ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmgm.2007.08.009"},{"key":"S0890060418000197_ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-005-3674-1"},{"key":"S0890060418000197_ref29","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1096-987X(199903)20:4<383::AID-JCC1>3.0.CO;2-M"},{"key":"S0890060418000197_ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcde.2017.11.003"},{"key":"S0890060418000197_ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2006.06.007"},{"key":"S0890060418000197_ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-007-0181-0"},{"key":"S0890060418000197_ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MCG.2007.89"},{"key":"S0890060418000197_ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2012.02.001"},{"key":"S0890060418000197_ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2006.08.001"},{"key":"S0890060418000197_ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1118890.1118893"},{"key":"S0890060418000197_ref9","volume-title":"Information and Visualization in Data Mining and Knowledge Discovery","author":"Fayya","year":"2002"},{"key":"S0890060418000197_ref36","first-page":"1","article-title":"Introducing dice, Jaccard and other label overlap measures to ITK","volume":"2009","author":"Tustiton","year":"2009","journal-title":"The Insight Journal"},{"key":"S0890060418000197_ref2","doi-asserted-by":"publisher","DOI":"10.1115\/1.1577356"},{"key":"S0890060418000197_ref32","first-page":"16","article-title":"Deriving functional properties of components from the analysis of digital mock-ups","volume":"16","author":"Shahwan","year":"2014","journal-title":"Engineering Computations"},{"key":"S0890060418000197_ref17","unstructured":"Kazhdan M , Funkhouser T and Rusinkiewicz S (2003) Rotation invariant spherical harmonic representation of 3 d shape descriptors. Symposium on geometry processing, 6, pp. 156\u2013164."},{"key":"S0890060418000197_ref38","first-page":"817","article-title":"A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity","volume":"1980","author":"White","year":"1998","journal-title":"Econometrica: Journal of the Econometric Society"},{"key":"S0890060418000197_ref31","doi-asserted-by":"publisher","DOI":"10.1109\/72.80266"},{"key":"S0890060418000197_ref4","doi-asserted-by":"publisher","DOI":"10.1111\/1467-8659.00669"},{"key":"S0890060418000197_ref6","first-page":"385","article-title":"An approach to a feature-based comparison of solid models of machined parts","volume":"16","author":"Cicirello","year":"2002","journal-title":"AI EDAM"},{"key":"S0890060418000197_ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2008.04.033"},{"key":"S0890060418000197_ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2012.10.005"},{"key":"S0890060418000197_ref13","doi-asserted-by":"crossref","unstructured":"Ip CY , Regli W , Sieger L and Shokoufandeh A (2003) Automated learning of model classifications. Proceedings of the eighth ACM symposium on Solid modeling and applications (pp. 322\u2013327). ACM.","DOI":"10.1145\/781606.781659"},{"key":"S0890060418000197_ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2004.07.002"},{"key":"S0890060418000197_ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2009.07.003"},{"key":"S0890060418000197_ref8","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/BF01589116","article-title":"On the limited memory BFGS method for large scale optimization","volume":"45","author":"Dong","year":"1989","journal-title":"Mathematical Programming"},{"key":"S0890060418000197_ref18","first-page":"557","article-title":"Feature scoring by mutual information for classification of mass spectra","author":"Krier","year":"2006","journal-title":"Applied Artificial Intelligence"},{"key":"S0890060418000197_ref19","doi-asserted-by":"crossref","unstructured":"Lupinetti K , Giannini F , Monti M and Philippe PJ (2017 July 17\u201319). Identification of functional sets in mechanical assembly models. ICIDM2017 conference, Milan, Italy.","DOI":"10.1115\/1.4036120"},{"key":"S0890060418000197_ref21","doi-asserted-by":"publisher","DOI":"10.1162\/089976603322385117"},{"key":"S0890060418000197_ref24","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X8900900307"},{"key":"S0890060418000197_ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.09.016"}],"container-title":["Artificial Intelligence for Engineering Design, Analysis and Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0890060418000197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T17:35:38Z","timestamp":1661880938000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0890060418000197\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,28]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,2]]}},"alternative-id":["S0890060418000197"],"URL":"https:\/\/doi.org\/10.1017\/s0890060418000197","relation":{},"ISSN":["0890-0604","1469-1760"],"issn-type":[{"value":"0890-0604","type":"print"},{"value":"1469-1760","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,28]]}}}