{"id":"W4253408418","doi":"10.1115/msec2017-2796","title":"Multi-Objective Build Orientation Optimization for Powder Bed Fusion by Laser","year":2017,"lang":"en","type":"article","venue":"","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Visualization; Graphical user interface; Surface roughness; Orientation (vector space); Genetic algorithm; Surface finish; Process (computing); Fusion; Work (physics); Sensor fusion; Interface (matter); Mathematical optimization; Mechanical engineering; Materials science; Data mining; Artificial intelligence; Engineering; Mathematics; Machine learning; Geometry","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":[0.00004520591,0.00009007801,0.00007985276,0.00001943867,0.0002096051,0.0001180755,0.00007581341,0.00004864281,0.0003594727],"category_scores_gemma":[0.00005320333,0.00007903444,0.00001923787,0.00001057674,0.00001493178,0.0002628216,0.00001822466,0.00002366045,0.00001047416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001917831,"about_ca_system_score_gemma":0.000003833939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002769578,"about_ca_topic_score_gemma":0.00001148476,"domain_scores_codex":[0.9996364,0.000004547948,0.00008666939,0.0001169547,0.00004852836,0.0001069225],"domain_scores_gemma":[0.999734,0.00002393685,0.0000398383,0.0001189948,0.00005757792,0.00002568969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000362158,0.0002605845,0.001256623,0.001138241,0.0002767921,0.000003692837,0.002056216,0.5617356,0.3073754,0.0004446903,0.0810139,0.044076],"study_design_scores_gemma":[0.0007249361,0.00003119259,0.001530957,0.00001592849,0.00001288149,3.675557e-7,0.00005577598,0.06006808,0.9329914,0.00008730577,0.004303036,0.0001781066],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3229665,0.00002910814,0.6734333,0.00007330265,0.0007424995,0.0003998337,0.0001699011,0.0002681636,0.001917338],"genre_scores_gemma":[0.9776708,0.00003168221,0.02126285,0.00003048598,0.00009098101,0.00005575995,0.0001658553,0.00002537704,0.0006661913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6547043,"threshold_uncertainty_score":0.3935974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120558047020051,"score_gpt":0.250041118901957,"score_spread":0.2379853141999519,"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."}}