{"id":"W4381857294","doi":"10.1007/s10845-023-02164-7","title":"Tool wear prediction in milling CFRP with different fiber orientations based on multi-channel 1DCNN-LSTM","year":2023,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Delamination (geology); Materials science; Fiber; Feature (linguistics); Machining; Tool wear; Abrasive; Representation (politics); Anisotropy; Pyramid (geometry); SIGNAL (programming language); Artificial intelligence; Computer science; Layer (electronics); Pattern recognition (psychology); Composite material; Geometry; Mathematics; Geology","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":[0.0002769043,0.0004307506,0.0003734608,0.0004092301,0.0001806725,0.0003582674,0.0004337733,0.0005912179,0.000595751],"category_scores_gemma":[0.000548034,0.0002000926,0.0003508979,0.0003808235,0.0002232136,0.000500327,0.000183707,0.0003058474,0.0001497954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002713941,"about_ca_system_score_gemma":0.0003137109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007261939,"about_ca_topic_score_gemma":0.01312056,"domain_scores_codex":[0.9998364,0.00001045549,0.00000907656,0.00004866664,0.00006521477,0.00003020178],"domain_scores_gemma":[0.9996475,0.0001134666,0.00004763989,0.0000376781,0.0001362183,0.00001742755],"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":[0.001416486,0.0003478347,0.02329276,0.0004075168,0.0001076138,0.0003694326,0.0003005282,0.3848217,0.1945887,0.0003519644,0.001772318,0.3922232],"study_design_scores_gemma":[0.000005535649,0.0000903637,0.0112269,0.000006563973,0.00001493719,0.00004589895,0.00004083486,0.9710926,0.01723715,0.00007809015,0.000146697,0.00001443874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8997811,0.0004237859,0.09771328,0.00005189101,0.0000679579,0.00001831405,0.0001637914,0.0006223789,0.001157499],"genre_scores_gemma":[0.9891121,0.00008430578,0.01006258,0.00001228007,0.00000452429,0.00000712528,0.00009264142,0.00001495916,0.0006096038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007261939,"threshold_uncertainty_score":0.01443934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649364299578437,"score_gpt":0.2413036082208616,"score_spread":0.2248099652250772,"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."}}