{"id":"W4321253252","doi":"10.3390/machines11020297","title":"ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction","year":2023,"lang":"en","type":"article","venue":"Machines","topic":"Lubricants and Their Additives","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Artificial neural network; Feature (linguistics); Pattern recognition (psychology); Feature extraction; Deep learning; Key (lock); Sequence (biology); Machine learning","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":[0.000326787,0.001305053,0.0007417497,0.000644828,0.0002357462,0.0004930468,0.001794206,0.0009238978,0.002376159],"category_scores_gemma":[0.0007902296,0.000428047,0.0008363003,0.0006255243,0.0002892626,0.0009503085,0.0006617347,0.001114114,0.0006482274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007597592,"about_ca_system_score_gemma":0.0007113547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01199517,"about_ca_topic_score_gemma":0.01906697,"domain_scores_codex":[0.9998286,0.00001371051,0.000009062494,0.00006687062,0.00004714723,0.00003451484],"domain_scores_gemma":[0.9997358,0.00007401533,0.00003307889,0.00002351809,0.0001179991,0.00001560395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004575133,0.0003210622,0.003698144,0.0003321255,0.0001949754,0.0002905863,0.00006739482,0.4544396,0.02886653,0.001582057,0.01060693,0.4991432],"study_design_scores_gemma":[0.000005007803,0.00004618945,0.0005075306,0.000008907617,0.00001712836,0.00002587351,0.000004740438,0.9949434,0.003369072,0.000451639,0.000613594,0.000006892766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1541963,0.004173289,0.822876,0.000507311,0.0006252234,0.0001251452,0.001436935,0.009943078,0.006116739],"genre_scores_gemma":[0.9054477,0.0008656621,0.08124419,0.0003434131,0.0001138972,0.0001300644,0.001668015,0.000129744,0.01005737],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01199517,"threshold_uncertainty_score":0.02385068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00939127143012939,"score_gpt":0.2247686080899688,"score_spread":0.2153773366598394,"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."}}