{"id":"W1851214885","doi":"10.1007/s00158-015-1324-y","title":"Drilling optimization of woven CFRP laminates under different tool wear conditions: a multi-objective design of experiments approach","year":2015,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Economía y Competitividad","keywords":"Drilling; Factorial experiment; Thrust; Design of experiments; Woven fabric; Abrasive; Materials science; Composite laminates; Response surface methodology; Delamination (geology); Carbon fiber reinforced polymer; Composite number; Engineering design process; Composite material; Structural engineering; Computer science; Mechanical engineering; Engineering; Geology; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007677601,0.0002305385,0.0002870599,0.0001324096,0.0001140953,0.0000213578,0.00008391696,0.0001073723,0.00001077938],"category_scores_gemma":[0.00003781431,0.0002000867,0.00003666489,0.0002053043,0.00009302027,0.0004575229,0.00006230351,0.00008823571,1.708449e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006118946,"about_ca_system_score_gemma":0.00002087752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005191168,"about_ca_topic_score_gemma":4.587094e-7,"domain_scores_codex":[0.99899,0.00004630733,0.0003859453,0.0002387848,0.0001680438,0.0001708924],"domain_scores_gemma":[0.999402,0.00004721574,0.0001679249,0.0001196288,0.0001942372,0.00006900311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007206517,0.00003869119,0.0001949818,0.0001428512,0.0000430525,2.612711e-7,0.002532614,0.9950758,0.001550503,0.0001674791,0.000001811573,0.0001799205],"study_design_scores_gemma":[0.001228532,0.0001193942,0.0006467804,0.00006596273,0.00005611411,0.000005338517,0.002064865,0.9893588,0.005872812,0.0003561341,1.477943e-7,0.0002250557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1021254,0.0004714304,0.8966455,0.000005088763,0.0001524097,0.0004270532,0.00002633771,0.00009552801,0.00005123814],"genre_scores_gemma":[0.6310055,0.0001768367,0.3685131,0.000001638999,0.00001793423,0.00002638435,0.0002135163,0.00002589905,0.00001914166],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5288802,"threshold_uncertainty_score":0.81593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03090484833856814,"score_gpt":0.2800269366340931,"score_spread":0.2491220882955249,"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."}}