{"id":"W3120640809","doi":"10.1007/s00170-020-06473-6","title":"Cutting tool temperature monitoring in circular sawing: measurement and multi-sensor feature fusion-based prediction","year":2021,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of British Columbia","funders":"","keywords":"Machining; Thermocouple; Rotational speed; Acoustic emission; Rotation (mathematics); Enhanced Data Rates for GSM Evolution; Mechanical engineering; Acoustics; Temperature measurement; Vibration; Process (computing); Cutting tool; Tool wear; Engineering; Computer science; Artificial intelligence; Electrical engineering","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":[0.0002386448,0.0003186405,0.0004369267,0.0003577952,0.000162141,0.000344944,0.0004100863,0.0005227731,0.000254584],"category_scores_gemma":[0.0004945976,0.0002190896,0.0003082287,0.0004746425,0.0002472529,0.000732072,0.0002408669,0.0003895042,0.0001063387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000226952,"about_ca_system_score_gemma":0.0002559791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001389334,"about_ca_topic_score_gemma":0.001904461,"domain_scores_codex":[0.9998198,0.0000170208,0.00000721208,0.00005844569,0.00007189216,0.00002560973],"domain_scores_gemma":[0.9997262,0.0001112449,0.00005578457,0.00002381007,0.00007063677,0.00001224385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001470616,0.0004406225,0.03232779,0.0002300734,0.000102223,0.0003021372,0.0002248895,0.1558679,0.5177854,0.0007502854,0.0009127025,0.2895853],"study_design_scores_gemma":[0.000009372905,0.0001138221,0.01657023,0.000004464725,0.00002368121,0.00009452387,0.0000269241,0.9406706,0.04203992,0.0002525579,0.0001784165,0.00001548331],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7329051,0.0005707508,0.2647422,0.0001159271,0.00007083278,0.00002088671,0.0001006827,0.0004291546,0.001044306],"genre_scores_gemma":[0.9928311,0.00006854798,0.006924239,0.000008269619,0.000006851539,0.000003363569,0.000026186,0.000006673957,0.0001247094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001389334,"threshold_uncertainty_score":0.002762496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01038135369875787,"score_gpt":0.2343403459596561,"score_spread":0.2239589922608983,"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."}}