{"id":4545,"date":"2023-11-16T10:30:41","date_gmt":"2023-11-16T15:30:41","guid":{"rendered":"https:\/\/cqfa.quebec\/?p=4545"},"modified":"2023-11-16T10:38:24","modified_gmt":"2023-11-16T15:38:24","slug":"machine-learning-study-of-the-effect-of-process-parameters-on-tensile-strength-of-fff-pla-and-pla-cf","status":"publish","type":"post","link":"https:\/\/cqfa.quebec\/en\/machine-learning-study-of-the-effect-of-process-parameters-on-tensile-strength-of-fff-pla-and-pla-cf\/","title":{"rendered":"Machine Learning Study of the Effect of Process Parameters on Tensile Strength of FFF PLA and PLA-CF"},"content":{"rendered":"

Ziadia<\/span>, A.; <\/span>Habibi<\/span>, M.; <\/span>Kelouwani<\/span>, S. (2023). <\/span><\/span>Machine <\/span>Learning <\/span>Study<\/span> of the <\/span>Effect<\/span> of Process <\/span>Parameters<\/span> on <\/span>Tensile<\/span> Strength<\/span> of FFF PLA and PLA-CF.<\/span> <\/span>Eng<\/span>\u00a0<\/span>2023<\/span>,\u00a0<\/span>4<\/span>(4), 2741-2763.<\/span><\/span><\/em><\/p>\n

 <\/p>\n

Material extrusion is a popular additive manufacturing technology due to its low cost, wide market availability, ability to construct complex parts, safety, and cleanliness. However, <\/span>optimizing<\/span> the process parameters to obtain the best possible mechanical properties has not been extensively studied. This paper aims to develop ensemble learning-based models to predict the ultimate tensile strength, Young\u2019s modulus, and the strain at break of PLA and PLA-CF 3D-printed parts, using printing temperature, printing speed, and layer thickness as process parameters. Additionally, the study investigates the impact of process parameters and material selection on the mechanical properties of the printed parts and uses genetic algorithms for multi-<\/span>objective<\/span> optimization according to user specifications. The results <\/span>indicate<\/span> that process parameters and material selection significantly influence the mechanical properties of the printed parts. The ensemble learning predictive models yielded an R<\/span><\/span>2<\/span><\/span>\u00a0value of 91.75% for ultimate tensile strength, 94.08% for Young\u2019s modulus, and 88.54% for strain at break. The genetic algorithm successfully <\/span>identified<\/span> optimal<\/span> parameter values for the desired mechanical properties. For <\/span>optimal<\/span> ultimate tensile strength, PLA-CF was used at 222.28 \u00b0C, 0.261 mm layer, 40.30 mm\/s speed, yielding 41.129 MPa. For Young\u2019s modulus: 4423.63 MPa, PLA-CF, 200.01 \u00b0C, 0.388 mm layer, 40.38 mm\/s. For strain at break: 2.249%, PLA, 200.34 \u00b0C, 0.390 mm layer, 45.30 mm\/s. Moreover, this work is the first to model the process\u2013structure property relationships for an additive manufacturing process and to use a multi-<\/span>objective<\/span> optimization approach for multiple mechanical properties, <\/span>utilizing<\/span> ensemble learning-based algorithms and genetic algorithms.<\/span><\/span><\/p>\n

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Read the publication<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Ziadia, A.; Habibi, M.; Kelouwani, S. (2023). Machine Learning Study of the Effect of Process Parameters on Tensile Strength of FFF PLA and PLA-CF. Eng\u00a02023,\u00a04(4), 2741-2763.   Material extrusion is […]<\/p>\n","protected":false},"author":80,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[43],"tags":[],"class_list":["post-4545","post","type-post","status-publish","format-standard","hentry","category-publications-academiques-quebecoises"],"acf":[],"yoast_head":"\nMachine Learning Study of the Effect of Process Parameters on Tensile Strength of FFF PLA and PLA-CF - CQFA - Carrefour qu\u00e9b\u00e9cois de la fabrication additive<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cqfa.quebec\/en\/machine-learning-study-of-the-effect-of-process-parameters-on-tensile-strength-of-fff-pla-and-pla-cf\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning Study of the Effect of Process Parameters on Tensile Strength of FFF PLA and PLA-CF - CQFA - Carrefour qu\u00e9b\u00e9cois de la fabrication additive\" \/>\n<meta property=\"og:description\" content=\"Ziadia, A.; Habibi, M.; Kelouwani, S. 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