{"id":"W4392237878","doi":"10.1177/09544089241228697","title":"Multi-objective optimization and experimental investigation of friction stir welding under Minimum quantity lubrication","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering","topic":"Advanced Welding Techniques Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Friction stir welding; Materials science; Nozzle; Welding; Lubrication; Surface roughness; Metallurgy; Aluminium; Response surface methodology; Composite material; Volumetric flow rate; Material flow; Mechanical engineering; Computer science; Mechanics; 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":[0.001401278,0.000879957,0.001182526,0.0007228495,0.0004502158,0.0006989697,0.00070401,0.001001367,0.001275695],"category_scores_gemma":[0.001560897,0.0003558546,0.0009754067,0.0006089903,0.000434846,0.0003576045,0.0004049333,0.0006312726,0.0001149604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005654217,"about_ca_system_score_gemma":0.0006214183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006108765,"about_ca_topic_score_gemma":0.004873729,"domain_scores_codex":[0.9996426,0.0001055478,0.00002193085,0.00006199077,0.0000940156,0.00007394367],"domain_scores_gemma":[0.9986876,0.0008417646,0.0001803935,0.00006223515,0.0001785372,0.00004951835],"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":[0.0005186033,0.0007017432,0.001890811,0.0005523642,0.00006743384,0.0001839797,0.0001120031,0.9500651,0.03511296,0.0006582015,0.0001622855,0.009974557],"study_design_scores_gemma":[0.00003606639,0.0009343568,0.002195087,0.00001095461,0.0000294622,0.00001278857,0.00007613622,0.9816596,0.01468366,0.0001524094,0.0001934964,0.00001600136],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841735,0.0002992069,0.01284544,0.00005878045,0.00001741757,0.00005683261,0.0001219638,0.00005364285,0.002373218],"genre_scores_gemma":[0.9922292,0.00008966538,0.00710032,0.000007496114,0.000001796813,0.0000484146,0.00004802693,0.000007071465,0.0004680425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006108765,"threshold_uncertainty_score":0.01214641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01554166795123925,"score_gpt":0.2550541512103456,"score_spread":0.2395124832591063,"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."}}