{"id":"W4400610646","doi":"10.1007/s40964-024-00711-z","title":"Multi-objective optimization of PLA-FDM parameters for enhancement of industrial product mechanical performance based on GRA-RSM and BBD","year":2024,"lang":"en","type":"article","venue":"Progress in Additive Manufacturing","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Response surface methodology; Product (mathematics); Computer science; Process engineering; Manufacturing engineering; Mathematics; Engineering; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002929712,0.0002637115,0.0003358442,0.0003929889,0.00004296282,0.0000292852,0.0001546935,0.0001341062,0.00001414269],"category_scores_gemma":[0.0001437003,0.0002433906,0.00006973024,0.0001315779,0.000173167,0.0001244973,0.00007865498,0.0003513024,8.16087e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001081456,"about_ca_system_score_gemma":0.00002127321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003228888,"about_ca_topic_score_gemma":0.000001784803,"domain_scores_codex":[0.9987016,0.00003103455,0.0003759672,0.0004077673,0.0001860518,0.0002975509],"domain_scores_gemma":[0.9993034,0.000335018,0.0000931457,0.0001977152,0.00003875899,0.00003197896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002727287,0.000152382,0.0004047711,0.001234513,0.0001759362,0.000007692604,0.0005154714,0.1501265,0.0004410452,0.00009915524,0.00006342737,0.8465064],"study_design_scores_gemma":[0.0005694783,0.0002506867,0.000837978,0.0009124681,0.00002145509,9.001017e-7,0.00006878017,0.226995,0.7700085,0.00007666831,0.00006928353,0.0001888019],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9123415,0.0003060077,0.08491872,0.00004809074,0.0004715665,0.001215194,0.0001768119,0.0004147002,0.000107445],"genre_scores_gemma":[0.9589914,0.00006824154,0.04045787,0.000003909634,0.00004325053,0.0003515553,0.0000372732,0.00004067045,0.000005824727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8463176,"threshold_uncertainty_score":0.992518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02790497194779841,"score_gpt":0.2546920741596193,"score_spread":0.2267871022118209,"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."}}