{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T19:49:24Z","timestamp":1791229764412,"version":"4.1.0"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,6]],"date-time":"2020-04-06T00:00:00Z","timestamp":1586131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008679","name":"Universidad de C\u00f3rdoba","doi-asserted-by":"publisher","award":["Plan Propio de Investigaci\u00f3n"],"award-info":[{"award-number":["Plan Propio de Investigaci\u00f3n"]}],"id":[{"id":"10.13039\/501100008679","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Polymers"],"abstract":"<jats:p>In the present work, ten data mining algorithms have been used to generate models capable of predicting the surface roughness of parts printed on polylactic acid (PLA) by using fused deposition modeling (FDM). The models have been trained using experimental data measured on 27 horizontal (XY) and 27 vertical (XZ) specimens, printed using different values for the parameters studied (layer height, extrusion temperature, print speed, print acceleration and flow). The models generated by multilayer perceptron (MLP) and logistic model trees (LMT) have obtained the best results in a cross-validation. Although it does not obtain such optimal results, the J48 algorithm (C4.5) allows the generation of models in the form of a decision tree. These trees permit to determine which print parameters have an influence on the surface roughness. For XY specimens, the surface roughness measured in the direction parallel to the extrusion path (Ra,0,XY ) depends on the flow, the print temperature and the layer height; in the direction perpendicular to the extrusion path, the surface roughness (Ra,90,XY) depends only on the flow. For XZ specimens, the surface roughness measured in the direction parallel to the extrusion path (Ra,0,XZ) depends only on the print speed; in the direction perpendicular to the extrusion path (Ra,90,XZ), it depends on the layer height and the extrusion temperature. According to the study carried out, the most suitable set up provides values of Ra,0,XY, Ra,90,XY, Ra,0,XZ and Ra,90,XZ equal to 0.46, 1.18, 0.45 and 11.54, respectively. A practical application of this work is the manufacture of PLA frame glasses using FDM.<\/jats:p>","DOI":"10.3390\/polym12040840","type":"journal-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T03:40:19Z","timestamp":1586403619000},"page":"840","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Use of Data Mining Techniques for the Prediction of Surface Roughness of Printed Parts in Polylactic Acid (PLA) by Fused Deposition Modeling (FDM): A Practical Application in Frame Glasses Manufacturing"],"prefix":"10.3390","volume":"12","author":[{"given":"Esther","family":"Molero","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, University of Cordoba, Medina Azahara Avenue, 14071 Cordoba, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Jes\u00fas","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Cordoba, Medina Azahara Avenue, 14071 Cordoba, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9728-258X","authenticated-orcid":false,"given":"Oscar","family":"Rodr\u00edguez-Alabanda","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Cordoba, Medina Azahara Avenue, 14071 Cordoba, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7627-3385","authenticated-orcid":false,"given":"Guillermo","family":"Guerrero-Vaca","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Cordoba, Medina Azahara Avenue, 14071 Cordoba, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9991-458X","authenticated-orcid":false,"given":"Pablo E.","family":"Romero","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Cordoba, Medina Azahara Avenue, 14071 Cordoba, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s12599-014-0334-4","article-title":"Industry 4.0","volume":"6","author":"Lasi","year":"2014","journal-title":"Bus. Inf. Syst. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1108\/13552541311292745","article-title":"Some investigations for small-sized product fabrication with FDM for plastic components","volume":"19","author":"Singh","year":"2013","journal-title":"Rapid Prototyp. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1108\/13552540210441166","article-title":"Anisotropic material properties of fused deposition modeling ABS","volume":"8","author":"Ahn","year":"2002","journal-title":"Rapid Prototyp. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.compositesb.2018.02.012","article-title":"Additive manufacturing (3D printing): A review of materials, methods, applications and challenges","volume":"143","author":"Ngo","year":"2018","journal-title":"Compos. Part B Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1108\/RPJ-01-2013-0012","article-title":"A review of melt extrusion additive manufacturing processes: I. Process design and modeling","volume":"20","author":"Turner","year":"2014","journal-title":"Rapid Prototyp. J."},{"key":"ref_6","first-page":"1","article-title":"Fused Deposition Modeling for Unmanned Aerial Vehicles (UAVs): A Review","volume":"1700552","author":"Klippstein","year":"2017","journal-title":"Adv. Eng. Mater."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.polymertesting.2018.05.020","article-title":"FDM process parameters influence over the mechanical properties of polymer specimens: A review","volume":"69","author":"Popescu","year":"2018","journal-title":"Polym. Test."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s40964-015-0002-3","article-title":"Effects of part build orientations on fatigue behaviour of FDM-processed PLA material","volume":"1","author":"Afrose","year":"2016","journal-title":"Prog. Addit. Manuf."},{"key":"ref_9","unstructured":"(2019, November 13). Smart Materials 3D-PLA3D870. Available online: https:\/\/www.smartmaterials3d.com\/en\/inicio\/172-1611-pla-3d870.html#\/2-size-750g\/25-colour-natural\/62-shoes_size-175mm."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1007\/s11837-015-1367-y","article-title":"Mechanical property optimization of FDM PLA in shear with multiple objectives","volume":"67","author":"Torres","year":"2015","journal-title":"JOM"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.matdes.2017.03.051","article-title":"Measurements of the mechanical response of unidirectional 3D-printed PLA","volume":"123","author":"Song","year":"2017","journal-title":"Mater. Des."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.matdes.2017.03.065","article-title":"Additive manufacturing of PLA structures using fused deposition modelling: Effect of process parameters on mechanical properties and their optimal selection","volume":"124","author":"Caminero","year":"2017","journal-title":"Mater. Des."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Caminero, M.\u00c1., Chac\u00f3n, J.M., Garc\u00eda-Plaza, E., N\u00fa\u00f1ez, P.J., Reverte, J.M., and Becar, J.P. (2019). Additive manufacturing of PLA-based composites using fused filament fabrication: Effect of graphene nanoplatelet reinforcement on mechanical properties, dimensional accuracy and texture. Polymers, 11.","DOI":"10.3390\/polym11050799"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Galati, M., and Minetola, P. (2019). On the measure of the aesthetic quality of 3D printed plastic parts. Int. J. Interact. Des. Manuf.","DOI":"10.1007\/s12008-019-00627-x"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1108\/13552541311323254","article-title":"3D roughness profile model in fused deposition modelling","volume":"19","author":"Boschetto","year":"2013","journal-title":"Rapid Prototyp. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1108\/RPJ-02-2013-0017","article-title":"A review of melt extrusion additive manufacturing processes: II. Materials, dimensional accuracy, and surface roughness","volume":"21","author":"Turner","year":"2015","journal-title":"Rapid Prototyp. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cirpj.2016.10.001","article-title":"Cost, sustainability and surface roughness quality\u2013A comprehensive analysis of products made with personal 3D printers","volume":"16","author":"Li","year":"2017","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_18","first-page":"13","article-title":"Additive technologies and materials used for making glasses frames","volume":"3","author":"Besnea","year":"2018","journal-title":"Int. J. Mechatron. Appl. Mech."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, M., Medina-S\u00e1nchez, G., Garc\u00eda-Collado, A., Gupta, M., and Carou, D. (2018). Surface quality enhancement of fused deposition modeling (FDM) printed samples based on the selection of critical printing parameters. Materials, 11.","DOI":"10.3390\/ma11081382"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"13448","DOI":"10.1016\/j.eswa.2011.04.063","article-title":"A review of data mining applications for quality improvement in manufacturing industry","volume":"38","author":"Batmaz","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1115\/1.2194554","article-title":"Data Mining in Manufacturing: A review","volume":"128","author":"Harding","year":"2006","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s10845-008-0145-x","article-title":"Data mining in manufacturing: A review based on the kind of knowledge","volume":"20","author":"Choudhary","year":"2009","journal-title":"J. Intell. Manuf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Razvi, S., Feng, S., Narayana, A., Lee, Y.-T., and Witherell, P. (2019, January 18\u201321). A review of machine learning applications in additive manufacturing. Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Anaheim, CA, USA.","DOI":"10.1115\/DETC2019-98415"},{"key":"ref_24","unstructured":"Perrouin, G., and Weyns, D. (2019). Towards learning-aided configuration in 3D printing: Feasibility study and application to defect prediction. Proceedings of the 13th International Workshop on Variability Modelling of Software Intensive Systems (VaMoS\u201919), Leuven, Belgium, 6\u20138 February 2019, ACM."},{"key":"ref_25","unstructured":"Wu, D., Wei, Y., and Terpenny, J. (2018, January 18\u201322). Surface roughness prediction in additive manufacturing using machine learning. Proceedings of the ASME 2018 13th International Manufacturing Science and Engineering Conference (MSEC2018), College Station, TX, USA."},{"key":"ref_26","unstructured":"Wulfsberg, J., Hintze, W., and Behrens, B. (2019). Data-driven Prediction of Surface Quality in Fused Deposition Modeling using Machine Learning. Proceedings of the Production at the Leading Edge of Technology, Hamburg, Germany, 30 September\u20132 October 2019, Springer."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1007\/s00170-011-3675-x","article-title":"Bayesian regularization-based Levenberg-Marquardt neural model combined with BFOA for improving surface finish of FDM processed part","volume":"60","author":"Mahapatra","year":"2012","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1007\/s00170-012-4687-x","article-title":"Surface roughness prediction in fused deposition modelling by neural networks","volume":"67","author":"Boschetto","year":"2013","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1108\/RPJ-06-2015-0075","article-title":"Improvement of FDM parts\u2019 surface quality using optimized neural networks-Medical case studies","volume":"23","author":"Vahabli","year":"2017","journal-title":"Rapid Prototyp. J."},{"key":"ref_30","unstructured":"(2020, January 18). Youreyewear. Available online: https:\/\/www.youreyewear.com\/."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Garc\u00eda Plaza, E., N\u00fa\u00f1ez L\u00f3pez, P.J., Caminero Torija, M.\u00c1., and Chac\u00f3n Plaza, J.M. (2019). Analysis of PLA geometric properties processed by FFF additive manufacturing: Effects of process parameters and plate-extruder precision motion. Polymers, 11.","DOI":"10.3390\/polym11101581"},{"key":"ref_32","first-page":"1139","article-title":"Dimensional accuracy and surface roughness of part features manufactured by open source 3D printer","volume":"13","author":"Ramli","year":"2018","journal-title":"ARPN J. Eng. Appl. Sci."},{"key":"ref_33","first-page":"211","article-title":"How Surface Roughness Performance of Printed Parts Manufactured by How Surface Roughness Performance of Printed Parts Manufactured by Desktop FDM 3D Printer with PLA + is Influenced by Measuring Direction","volume":"5","author":"Alsoufi","year":"2017","journal-title":"Am. J. Mech. Eng."},{"key":"ref_34","first-page":"266","article-title":"Printing Parameters Effect on Surface Characteristics of 3D Printed Pla Materials","volume":"269","author":"Kovan","year":"2018","journal-title":"Mach. Technol. Mater."},{"key":"ref_35","unstructured":"Witten, I.H., Frank, E., and Hall, M.A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. [3rd ed.]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.cie.2018.07.041","article-title":"MLCPM: A process monitoring framework for 3D metal printing in industrial scale","volume":"124","author":"Amini","year":"2018","journal-title":"Comput. Ind. Eng."},{"key":"ref_37","unstructured":"Silhavy, R., Senkerik, R., Oplatkova, Z., Prokopova, Z., and Silhavy, P. (2015). On the Performance of Ensemble Learning for Automated Diagnosis of Breast Cancer, Springer."},{"key":"ref_38","unstructured":"Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, Inc."},{"key":"ref_39","unstructured":"John, G., and Langley, P. (1995). Estimating continuous distributions in Bayesian classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, Canada, 18\u201320 August 1995, Morgan Kaufmann Publishers, Inc."},{"key":"ref_40","unstructured":"Negnevitsky, M. (2005). Artificial Intelligence: A Guide to Intelligent Systems, Addison-Wesley."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/BF00153759","article-title":"Instance-Based Learning Algorithms","volume":"6","author":"Aha","year":"1991","journal-title":"Mach. Learn."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Clearly, J.G., and Trigg, L.E. (1995). An Instance-based learner using and entropic distance measure. Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, CA, USA, 9\u201312 July 1995, Morgan Kaufmann Publishers, Inc.","DOI":"10.1016\/B978-1-55860-377-6.50022-0"},{"key":"ref_43","unstructured":"Quinlan, J.R. (2014). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, Inc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10994-005-0466-3","article-title":"Logistic Model Trees","volume":"59","author":"Landwehr","year":"2005","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/978-3-540-39857-8_23","article-title":"Logistic model trees","volume":"2837","author":"Landwehr","year":"2003","journal-title":"Lect. Notes Artif. Intell. Subser. Lect. Notes Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Barrios, J.M., and Romero, P.E. (2019). Decision tree methods for predicting surface roughness in fused deposition modeling parts. Materials, 12.","DOI":"10.3390\/ma12162574"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/S0924-0136(03)00596-X","article-title":"Data mining applied to wire-EDM process","volume":"142","author":"Kuriakose","year":"2003","journal-title":"J. Mater. Process. Technol."}],"container-title":["Polymers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4360\/12\/4\/840\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:15:59Z","timestamp":1760174159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4360\/12\/4\/840"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,6]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["polym12040840"],"URL":"https:\/\/doi.org\/10.3390\/polym12040840","relation":{},"ISSN":["2073-4360"],"issn-type":[{"value":"2073-4360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,6]]}}}