{"id":"W4382238648","doi":"10.1016/j.jmrt.2023.05.088","title":"Towards optimization of polymer filament tensile test for material extrusion additive manufacturing process","year":2023,"lang":"en","type":"article","venue":"Journal of Materials Research and Technology","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; National Research Council Canada; Toronto Metropolitan University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Ultimate tensile strength; Extrusion; Extensometer; Acrylonitrile butadiene styrene; Fused filament fabrication; Tensile testing; Protein filament; Composite material; Charpy impact test; Polymer","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001365914,0.000981134,0.000453648,0.0008913522,0.0002248128,0.0004957563,0.0007658431,0.0006790044,0.001035264],"category_scores_gemma":[0.002132343,0.0003605881,0.0003999248,0.000714702,0.0002937551,0.0006755737,0.0003930782,0.0005241525,0.0005505478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002644579,"about_ca_system_score_gemma":0.0003547593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003294937,"about_ca_topic_score_gemma":0.001071163,"domain_scores_codex":[0.998149,0.0002473974,0.0001772694,0.0002968638,0.001050878,0.00007853231],"domain_scores_gemma":[0.9979926,0.0005189748,0.0004686824,0.000140323,0.0008283058,0.00005112874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007153632,0.00006255822,0.001380226,0.0001591794,0.00000894915,0.00008464312,0.00004090772,0.0005662657,0.9829223,0.0001177829,0.0001322087,0.01445337],"study_design_scores_gemma":[0.000007154219,0.0005550135,0.005986922,0.0000210708,0.00003452713,0.0001648374,0.00005499905,0.00566008,0.9854533,0.00006750473,0.001971988,0.00002254093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6590112,0.004585369,0.3265179,0.0003268933,0.0002553677,0.000518342,0.000562888,0.001389855,0.006832213],"genre_scores_gemma":[0.6981167,0.002387922,0.2951892,0.0001676289,0.00005057706,0.0004887272,0.0006637665,0.0001797934,0.002755608],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001365914,"threshold_uncertainty_score":0.007223725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03140215106497938,"score_gpt":0.3072361672592388,"score_spread":0.2758340161942594,"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."}}