{"id":"W3177792861","doi":"10.1016/j.addma.2021.102152","title":"Development of a defect-detection platform using photodiode signals collected from the melt pool of laser powder-bed fusion","year":2021,"lang":"en","type":"article","venue":"Additive manufacturing","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Quality assurance; Fusion; 3D printing; Fabrication; Rapid prototyping; Selective laser melting; Process (computing); Sensor fusion; Laser; Computer science; Mechanical engineering; Artificial intelligence; Optics; Composite material; Engineering","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":[0.0004665409,0.0004623232,0.0004552936,0.0007117915,0.0001513581,0.0005130329,0.000918471,0.0006220915,0.0006794594],"category_scores_gemma":[0.0005244964,0.0003020252,0.000295537,0.0004203452,0.0002924978,0.0006450232,0.0003942713,0.0004012417,0.0003002738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003687784,"about_ca_system_score_gemma":0.0003810811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003801301,"about_ca_topic_score_gemma":0.0005683234,"domain_scores_codex":[0.9996413,0.00002168136,0.00001941526,0.0001117668,0.000172721,0.00003314327],"domain_scores_gemma":[0.9995976,0.00008406576,0.0001042352,0.00005992259,0.000132998,0.00002117879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008311067,0.00004292569,0.0008269753,0.00006831004,0.00001066764,0.00006234676,0.00004434205,0.001336874,0.9824234,0.0002109679,0.00009696858,0.01479308],"study_design_scores_gemma":[0.000009769651,0.0002501871,0.00235018,0.000004536962,0.00001410192,0.0001065121,0.00002384687,0.02431998,0.9719967,0.00009577305,0.0008091154,0.00001937877],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6051729,0.0003562736,0.3900132,0.00013036,0.00007840635,0.0002095999,0.0004440404,0.002328333,0.001266677],"genre_scores_gemma":[0.7700207,0.0002672583,0.2276317,0.0000739463,0.00001758058,0.0002410457,0.0002491219,0.0000700442,0.001428592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000918471,"threshold_uncertainty_score":0.002675653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02085570672420737,"score_gpt":0.2189356019536491,"score_spread":0.1980798952294417,"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."}}