{"id":"W4392913961","doi":"10.1115/1.4065090","title":"Transferability Analysis of Data-Driven Additive Manufacturing Knowledge: A Case Study Between Powder Bed Fusion and Directed Energy Deposition","year":2024,"lang":"en","type":"article","venue":"Journal of Computing and Information Science in Engineering","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"National Research Council Canada; Mitacs; McGill University","keywords":"Transferability; Deposition (geology); Fusion; Energy (signal processing); Sensor fusion; Computer science; Process engineering; Data mining; Materials science; Manufacturing engineering; Engineering drawing; Engineering; Artificial intelligence; Machine learning; Geology; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007185745,0.0005150305,0.0004356512,0.002386455,0.001776452,0.002610032,0.002015969,0.002645796,0.001837196],"category_scores_gemma":[0.01795355,0.0003382388,0.001147886,0.001906172,0.002346193,0.004044461,0.003390568,0.001677819,0.0002267193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003093556,"about_ca_system_score_gemma":0.001549294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00720539,"about_ca_topic_score_gemma":0.005705168,"domain_scores_codex":[0.9944293,0.002731679,0.0003296671,0.0004790333,0.001672255,0.0003580913],"domain_scores_gemma":[0.9734361,0.02085486,0.0009957636,0.002566408,0.001863766,0.0002831562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001303757,0.00423683,0.04898293,0.001742189,0.0003875001,0.02964554,0.03535784,0.3380563,0.03506239,0.1051722,0.003170652,0.3968818],"study_design_scores_gemma":[0.000226061,0.001048409,0.01699799,0.0004446609,0.00021669,0.004026902,0.0255618,0.7434355,0.09309262,0.08113066,0.0336578,0.0001608924],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8125865,0.0002759921,0.1740952,0.001687286,0.00002434003,0.0005934939,0.0002428231,0.0003155989,0.01017889],"genre_scores_gemma":[0.9367031,0.0001174819,0.0615839,0.00006899164,0.000006383973,0.0001317273,0.0002379986,0.00003519208,0.00111514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00720539,"threshold_uncertainty_score":0.03800231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465910528327769,"score_gpt":0.2716628152606723,"score_spread":0.2570037099773946,"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."}}