{"id":"W3011317869","doi":"10.4236/jmmce.2020.82002","title":"Experimental Investigation of Laser Surface Hardening of AISI 4340 Steel Using Different Laser Scanning Patterns","year":2020,"lang":"en","type":"article","venue":"Journal of Minerals and Materials Characterization and Engineering","topic":"High Entropy Alloys Studies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Materials science; Laser; Hardening (computing); Scanning electron microscope; Laser scanning; Taguchi methods; Hardened steel; Laser power scaling; Case hardening; Design of experiments; Response surface methodology; Composite material; Optics; Hardness; Computer science; 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":[0.0005883087,0.0004879029,0.000439503,0.0004668018,0.0003165552,0.0002077066,0.0004069188,0.0006972359,0.002032942],"category_scores_gemma":[0.0007626067,0.000338981,0.0003808362,0.000779885,0.0003742192,0.0002689227,0.0002695406,0.0004636476,0.0002371093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002160643,"about_ca_system_score_gemma":0.0002496166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005759337,"about_ca_topic_score_gemma":0.0009937093,"domain_scores_codex":[0.9993161,0.00007077424,0.00007484302,0.00010529,0.0003490486,0.00008396982],"domain_scores_gemma":[0.9989198,0.0003694599,0.0002025672,0.0001691123,0.0002940382,0.00004507411],"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":[0.0002050101,0.0001069293,0.0003167907,0.0001610649,0.000008267631,0.00009106099,0.0001727991,0.001365973,0.9937016,0.00005699559,0.00003401717,0.003779482],"study_design_scores_gemma":[0.00003319656,0.003256247,0.007287071,0.000009230984,0.0000254512,0.00007481295,0.0001271484,0.003429486,0.9848535,0.00005627924,0.0008262934,0.00002120046],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968599,0.0001361964,0.002262145,0.00001347712,0.00001043019,0.00004974125,0.00007046589,0.00003927523,0.0005582866],"genre_scores_gemma":[0.9903876,0.0002906322,0.007790714,0.00001892433,0.000008729404,0.00008043987,0.00007729773,0.0000227844,0.00132288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002032942,"threshold_uncertainty_score":0.00680083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0181780018275283,"score_gpt":0.206591849749526,"score_spread":0.1884138479219977,"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."}}