{"id":"W2051972741","doi":"10.4271/2015-01-1516","title":"Prediction of Component Failure using ‘Progressive Damage and Failure Model’ and Its Application in Automotive Wheel Design","year":2015,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Engineering Applied Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Automotive industry; Component (thermodynamics); Reliability engineering; Computer science; Automotive engineering; Engineering; Aerospace engineering; Physics","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":[0.0002791625,0.0005541288,0.0003501217,0.0004981898,0.0001824765,0.0002793364,0.0005728607,0.0008516861,0.001411998],"category_scores_gemma":[0.0005397489,0.0002546728,0.0005692692,0.0002039883,0.0001813379,0.0002234313,0.0002293912,0.0002396137,0.0003088126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003528166,"about_ca_system_score_gemma":0.0005390397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01460679,"about_ca_topic_score_gemma":0.009084814,"domain_scores_codex":[0.999903,0.00002184275,0.000005670602,0.00001714084,0.00003854248,0.00001383689],"domain_scores_gemma":[0.9997975,0.00009675224,0.00002403998,0.00002582027,0.00004690615,0.000008932358],"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":[0.0000145044,0.00002126662,0.0008465771,0.00001462209,0.000006114515,0.00003016595,0.00001475525,0.9872094,0.003728237,0.0002435122,0.00008165977,0.007789159],"study_design_scores_gemma":[7.058895e-7,0.00001954677,0.0002982499,0.000001089971,0.000001393135,0.000006091073,0.000001882654,0.9990256,0.0005180718,0.0000560782,0.00006964178,0.000001644335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.542632,0.0003366514,0.4513774,0.00009115774,0.00002287185,0.00008506533,0.0002505047,0.0009569778,0.004247472],"genre_scores_gemma":[0.977636,0.0001149182,0.02050158,0.000009045426,0.00000316303,0.00004053716,0.0001340527,0.00003029479,0.001530504],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01460679,"threshold_uncertainty_score":0.02904356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.029287375450164,"score_gpt":0.2548576688832863,"score_spread":0.2255702934331223,"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."}}