{"id":"W4312707143","doi":"10.1115/detc2022-89511","title":"Iterative Uncertainty Calibration for Modeling Metal Additive Manufacturing Processes Using Statistical Moment-Based Metric","year":2022,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Metric (unit); Calibration; Metamodeling; Moment (physics); Pooling; Computer science; Iterative and incremental development; Process (computing); Mathematical optimization; Algorithm; Mathematics; Artificial intelligence; Engineering; Statistics","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":[0.003022591,0.001047732,0.001097289,0.001473167,0.0003606364,0.001080739,0.001254316,0.00107083,0.0009548668],"category_scores_gemma":[0.005787977,0.0005575162,0.001167803,0.0009711439,0.0009087477,0.0009988301,0.001326662,0.0009907958,0.0001680269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001240271,"about_ca_system_score_gemma":0.001227133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004991973,"about_ca_topic_score_gemma":0.002038489,"domain_scores_codex":[0.9985566,0.0005985236,0.00008537758,0.000231743,0.0004245364,0.0001033178],"domain_scores_gemma":[0.9974954,0.00147408,0.0003657343,0.0001557392,0.0004569081,0.00005222971],"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":[0.00001447111,0.000009592938,0.0002347058,0.00002043177,0.00001379525,0.00001192252,0.00001433592,0.9887401,0.0006942229,0.002848614,0.00005962725,0.007338098],"study_design_scores_gemma":[7.881667e-7,0.000008119006,0.00006629262,0.000002057448,0.000002002494,0.000003362974,0.000001521805,0.9988385,0.000322297,0.0006803153,0.00007193223,0.000002874426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01200284,0.0001188269,0.9869774,0.00004004352,0.000007436476,0.00002694263,0.00002198037,0.0001561015,0.0006484042],"genre_scores_gemma":[0.8495966,0.0002094048,0.1489711,0.00004323967,0.00002049893,0.0001827076,0.0001229275,0.00007181951,0.0007817189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004991973,"threshold_uncertainty_score":0.01598519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1250253476720823,"score_gpt":0.350761914216273,"score_spread":0.2257365665441907,"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."}}