{"id":"W4223938533","doi":"10.3390/ma15082855","title":"Can a Black-Box AI Replace Costly DMA Testing?—A Case Study on Prediction and Optimization of Dynamic Mechanical Properties of 3D Printed Acrylonitrile Butadiene Styrene","year":2022,"lang":"en","type":"article","venue":"Materials","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Acrylonitrile butadiene styrene; Fused deposition modeling; Taguchi methods; Materials science; Particle swarm optimization; Orthogonal array; Process optimization; Process (computing); Raster graphics; 3D printing; Design of experiments; Black box; Computer science; Biological system; Algorithm; Composite material; Engineering; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001665797,0.0006676761,0.0005319144,0.0003525821,0.0003252525,0.0009383024,0.0007065836,0.00127469,0.0006474701],"category_scores_gemma":[0.001581165,0.0003723421,0.0004961877,0.0003890817,0.0005706914,0.000737953,0.0004238187,0.000890566,0.0001908599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005418629,"about_ca_system_score_gemma":0.0004987652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001517489,"about_ca_topic_score_gemma":0.002454385,"domain_scores_codex":[0.9994863,0.0001902706,0.00002631938,0.00008662379,0.0001638064,0.00004660488],"domain_scores_gemma":[0.998507,0.0009850323,0.0001596379,0.0001819761,0.0001173694,0.00004898559],"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":[0.0005036821,0.001046661,0.007205754,0.0005524938,0.00008217606,0.000698831,0.0002615336,0.7420465,0.1228724,0.002185673,0.0005140186,0.1220303],"study_design_scores_gemma":[0.00002569496,0.001047727,0.003202024,0.00003637641,0.00003745664,0.0001097479,0.00009849471,0.9042127,0.08837645,0.0008381273,0.00197938,0.00003579606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.859859,0.001002502,0.134389,0.0003812118,0.00003822871,0.0001127209,0.0001195719,0.0003626001,0.003735046],"genre_scores_gemma":[0.938503,0.0003032143,0.06023391,0.00003809699,0.00000741013,0.00005340812,0.00004582027,0.00002999512,0.00078511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001665797,"threshold_uncertainty_score":0.008809686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02046786356924954,"score_gpt":0.2248948695187313,"score_spread":0.2044270059494817,"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."}}