{"id":"W2899347417","doi":"10.1115/detc2018-85253","title":"Cutting Tool Wear Estimation Using a Genetic Algorithm Based Long Short-Term Memory Neural Network","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Computer science; Term (time); Long short term memory; Genetic algorithm; Set (abstract data type); Automation; Machine learning; Time series; Training set; Data mining; Artificial intelligence; Tool wear; Algorithm; Recurrent neural network; Engineering; Machining","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":[0.0005192324,0.0006738706,0.000563778,0.0005911799,0.0002718572,0.0005477763,0.0008228163,0.001002447,0.0006546149],"category_scores_gemma":[0.001290539,0.0002961126,0.0004671169,0.0004364074,0.0002860953,0.0005885373,0.0003300517,0.0006143736,0.0001179865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007606913,"about_ca_system_score_gemma":0.0008774921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01466,"about_ca_topic_score_gemma":0.0134464,"domain_scores_codex":[0.9998362,0.00003117868,0.000009310226,0.00005707242,0.00003601851,0.00003025609],"domain_scores_gemma":[0.9996314,0.0001765923,0.00004888829,0.00001956406,0.0001118049,0.00001183781],"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":[0.00006510587,0.00009010283,0.001847124,0.00002640289,0.00004909488,0.0000600141,0.00002742274,0.9057532,0.003535651,0.0005172372,0.0003083357,0.08772025],"study_design_scores_gemma":[0.000002520511,0.00001460979,0.000167756,0.000001303592,0.000004062632,0.000004372273,0.000001835053,0.9992734,0.0004094088,0.00009263721,0.00002641908,0.000001729846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3454678,0.000688427,0.6488348,0.000321132,0.00008364288,0.00008393118,0.00009807666,0.001275754,0.003146532],"genre_scores_gemma":[0.9118861,0.0001216252,0.08612336,0.0000873225,0.00001334241,0.00008128011,0.0001042304,0.00002389569,0.001558865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01466,"threshold_uncertainty_score":0.02914935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123056791604643,"score_gpt":0.253482602005603,"score_spread":0.2411769228451387,"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."}}