{"id":"W4390734031","doi":"10.1007/s40430-023-04637-5","title":"The use of machine learning in process–structure–property modeling for material extrusion additive manufacturing: a state-of-the-art review","year":2024,"lang":"en","type":"review","venue":"Journal of the Brazilian Society of Mechanical Sciences and Engineering","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Process (computing); Context (archaeology); Machine learning; 3D printing; Property (philosophy); Computer science; Plastics extrusion; Artificial intelligence; Process modeling; Convolutional neural network; Extrusion; Industrial engineering; Process engineering; Mechanical engineering; Engineering; Process optimization; Materials science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009614053,0.0002752132,0.0009025957,0.00005968833,0.0001294882,0.00004858672,0.0007662972,0.0001394058,0.000003466821],"category_scores_gemma":[0.0003422994,0.0001122062,0.0006548837,0.0002862265,0.0001517988,0.0001196392,0.000302954,0.0007957059,8.370556e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005830185,"about_ca_system_score_gemma":0.00006604881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006266588,"about_ca_topic_score_gemma":0.000003709149,"domain_scores_codex":[0.9982471,0.00004807642,0.0009249222,0.0001766433,0.0003506928,0.0002526002],"domain_scores_gemma":[0.9989436,0.0002979644,0.0004888296,0.0001759869,0.00005902442,0.00003463042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001212319,0.00001311981,8.266902e-7,0.05562973,0.000282997,7.916032e-7,0.0002163863,0.103454,0.0003498331,0.00005739843,0.0006829796,0.8392998],"study_design_scores_gemma":[0.000280991,0.0002260382,0.000005693811,0.1625205,0.0008405708,0.0001137194,0.0001311046,0.4230583,0.01771777,0.001949997,0.392555,0.0006003379],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.007143266,0.9860032,0.004567389,0.0002556059,0.001028563,0.0007872535,0.0001509001,0.00006090386,0.000002982971],"genre_scores_gemma":[0.01339745,0.9829975,0.003474888,0.000006939603,0.00005345305,0.00000964567,0.000001438721,0.00003248492,0.00002620241],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8386995,"threshold_uncertainty_score":0.4575638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03923134528634591,"score_gpt":0.2733524940911904,"score_spread":0.2341211488048445,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}