{"id":"W4322207938","doi":"10.1007/s00170-023-11113-w","title":"Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations","year":2023,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Trent University; Nottingham Trent University","keywords":"Sensor fusion; Tool wear; Engineering; Artificial neural network; Novelty detection; Artificial intelligence; Signal processing; Learning vector quantization; SIGNAL (programming language); Fusion; Control engineering; Machine learning; Computer science; Novelty; Electronic engineering; Digital signal processing; Machining; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003141263,0.00007468965,0.0001278471,0.0003740173,0.00005030311,0.00002334068,0.0003768104,0.00003748955,0.000003442399],"category_scores_gemma":[0.0002134114,0.00005026902,0.00002584071,0.0002542451,0.00007015863,0.0001368138,0.0001097535,0.0002327726,0.000003716669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004092801,"about_ca_system_score_gemma":0.000009398922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005398578,"about_ca_topic_score_gemma":0.00002603199,"domain_scores_codex":[0.9992644,0.00001207739,0.0003832607,0.00007941086,0.0001640246,0.00009688623],"domain_scores_gemma":[0.9995401,0.0001363254,0.0001019122,0.0001109874,0.00009810203,0.00001255314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006424959,0.000004485194,0.00001627613,0.000006432549,0.00001448124,0.000002903514,0.0002919891,0.8644238,0.02578566,0.005220328,0.000002160916,0.1041673],"study_design_scores_gemma":[0.0004219942,0.00005812194,0.001065923,0.0001528394,0.00001748153,0.0001105865,0.00212681,0.3161217,0.5958869,0.08330145,0.000582742,0.0001534228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6627572,0.00007144828,0.3346286,0.002177929,0.0001950207,0.0001084254,0.000001529934,0.00004897306,0.00001086874],"genre_scores_gemma":[0.9847111,0.0005670028,0.01460514,0.0000350657,0.00004856344,0.00001417511,0.000001720213,0.00001119618,0.000006029578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5701013,"threshold_uncertainty_score":0.2049911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009431795264016283,"score_gpt":0.2905177961837886,"score_spread":0.2810860009197724,"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."}}