{"id":"W2896742911","doi":"10.2351/1.5060357","title":"Optimization of aluminium laser welding using Taguchi and EM methods","year":2004,"lang":"en","type":"article","venue":"","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Aluminium Refining, Degassing and Filtering (Canada)","funders":"","keywords":"Taguchi methods; Fillet (mechanics); Welding; Materials science; Orthogonal array; Laser beam welding; Laser; Laser power scaling; Design of experiments; Process window; Robustness (evolution); Fillet weld; Mechanical engineering; Computer science; Composite material; Optics; Mathematics; Engineering; Optoelectronics","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":[0.002553813,0.001176901,0.001447558,0.001084728,0.0002836796,0.0007878994,0.0008109057,0.0005485893,0.0007405695],"category_scores_gemma":[0.001699264,0.0005716798,0.0007683533,0.001151403,0.0004369942,0.0004615674,0.000391649,0.0005432721,0.0003385567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005035028,"about_ca_system_score_gemma":0.0004486837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007117253,"about_ca_topic_score_gemma":0.001480746,"domain_scores_codex":[0.9979334,0.0006526954,0.0002124999,0.0002095636,0.0008538043,0.0001380531],"domain_scores_gemma":[0.9990608,0.0005369986,0.0001435169,0.00007797093,0.0001616458,0.00001910312],"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":[0.0005672548,0.000253686,0.0007545815,0.00111919,0.0000798445,0.00008540207,0.0001942561,0.05837245,0.7584527,0.001975761,0.0001472829,0.1779976],"study_design_scores_gemma":[0.00009862744,0.001852634,0.002442954,0.00003934425,0.0001583789,0.0001929717,0.000121077,0.1575225,0.8311144,0.001013377,0.005342891,0.0001008666],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1163836,0.001802009,0.8794186,0.0000428011,0.00005628369,0.0002667897,0.00008877141,0.0003613178,0.001579748],"genre_scores_gemma":[0.3056698,0.001384383,0.6903943,0.00003617446,0.00001767993,0.0005588053,0.0001077974,0.00006188505,0.001769235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002553813,"threshold_uncertainty_score":0.013506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01946724354868772,"score_gpt":0.297063774101381,"score_spread":0.2775965305526932,"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."}}