{"id":"W4409149547","doi":"10.1016/j.mtcomm.2025.112419","title":"Estimation of mechanical properties of acrylonitrile-butadiene-styrene for additive manufacturing using artificial neural network and LM-BP optimisation method","year":2025,"lang":"en","type":"article","venue":"Materials Today Communications","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Department of Mechanical Engineering, University of Alberta; Visvesvaraya Technological University","keywords":"Materials science; Acrylonitrile butadiene styrene; Artificial neural network; Acrylonitrile; Styrene-butadiene; Composite material; Biological system; Styrene; Process engineering; Artificial intelligence; Copolymer; Polymer; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003480382,0.0004796302,0.000382751,0.0004862531,0.0001898187,0.0003860761,0.0003241666,0.0005219285,0.0007413498],"category_scores_gemma":[0.0005701339,0.0002758237,0.0005152063,0.0004251105,0.0001290579,0.0004029302,0.0001920672,0.0003661736,0.0001902846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002498807,"about_ca_system_score_gemma":0.0003339438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002709421,"about_ca_topic_score_gemma":0.004152553,"domain_scores_codex":[0.9998502,0.00002054769,0.00001045841,0.00002997754,0.00007523491,0.0000136298],"domain_scores_gemma":[0.9997904,0.0001053054,0.00003076833,0.000011735,0.00005512527,0.00000672211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002473896,0.0001831783,0.00349687,0.0004094472,0.00007872078,0.0001572656,0.00004784434,0.6516792,0.2002652,0.0004924177,0.0003508142,0.1425917],"study_design_scores_gemma":[0.000005871591,0.00008515277,0.002882306,0.000004934507,0.00001655112,0.00001340355,0.000007526966,0.9644585,0.03219532,0.00009296455,0.0002285274,0.000009018725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6205734,0.001136209,0.3721532,0.0001137019,0.0000673247,0.00007469576,0.0001712491,0.0005280484,0.005182135],"genre_scores_gemma":[0.9614965,0.0002699905,0.03570104,0.00001310398,0.000008206395,0.00004984326,0.0001153371,0.00002035743,0.002325676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002709421,"threshold_uncertainty_score":0.005387366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05110455197833217,"score_gpt":0.2959872160556473,"score_spread":0.2448826640773152,"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."}}