{"id":"W4390653909","doi":"10.1016/j.simpat.2024.102896","title":"Robust parameter design for 3D printing process using stochastic computer model","year":2024,"lang":"en","type":"article","venue":"Simulation Modelling Practice and Theory","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Taguchi methods; Kriging; Hyperparameter; Computer science; Process (computing); Mathematical optimization; Noise (video); Function (biology); Quality (philosophy); Genetic algorithm; Machine learning; 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":[0.001315643,0.000938297,0.001498196,0.0005056949,0.0004579218,0.001261313,0.001159813,0.00159713,0.001579464],"category_scores_gemma":[0.003475059,0.0008496848,0.001592739,0.0005465512,0.0009079492,0.001002941,0.001207342,0.001166267,0.0003934632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018851,"about_ca_system_score_gemma":0.001347636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005678216,"about_ca_topic_score_gemma":0.002590958,"domain_scores_codex":[0.9989198,0.0003225416,0.00005118054,0.0002757929,0.0003473559,0.00008325235],"domain_scores_gemma":[0.9986602,0.0007020045,0.0002334693,0.00009668292,0.0002791001,0.00002864272],"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":[0.00003137161,0.000007382432,0.00007298655,0.00003498952,0.00002048756,0.00001428353,0.00001486618,0.9912124,0.001672407,0.002530506,0.00006963727,0.004318707],"study_design_scores_gemma":[0.000004455272,0.00001438555,0.00004481208,0.000002706014,0.000005818889,0.000004883444,0.000001493407,0.9983936,0.0005240165,0.0008575845,0.0001418296,0.00000440666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004251016,0.00008853319,0.9947309,0.00004448512,0.00001063958,0.00001765415,0.00001570628,0.0001335892,0.0007074956],"genre_scores_gemma":[0.8962099,0.0003853427,0.1000473,0.00008857939,0.00003110918,0.0002912458,0.0001731412,0.0001210314,0.002652313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005678216,"threshold_uncertainty_score":0.01129031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101767494889174,"score_gpt":0.3077883780823231,"score_spread":0.2060208831931492,"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."}}