{"id":"W4406520086","doi":"10.1016/j.ymssp.2025.112312","title":"A physics-informed learning approach for milling stability analysis with deep subdomain adaptation network","year":2025,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China; Canada Research Chairs","keywords":"Stability (learning theory); Adaptation (eye); Deep learning; Artificial intelligence; Network analysis; Engineering; Mechanical engineering; Computer science; Machine learning; Physics; Electrical engineering; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003446738,0.0001673062,0.0003407062,0.00005371411,0.0003240768,0.0001578747,0.00006053576,0.00008534921,0.000001332262],"category_scores_gemma":[0.00002550668,0.0001388628,0.0000521598,0.0007022404,0.00001814884,0.0002314345,0.0000144199,0.0001545132,9.194937e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005941781,"about_ca_system_score_gemma":0.00003346482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001298232,"about_ca_topic_score_gemma":0.00001306269,"domain_scores_codex":[0.9990246,0.00002520674,0.000311823,0.0002617601,0.0001161908,0.0002603913],"domain_scores_gemma":[0.9995663,0.0001121452,0.00009389398,0.00006807797,0.0001054588,0.0000541241],"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":[0.00005043823,0.000009767874,0.0001448576,0.001099903,0.0001169425,1.690953e-7,0.0002515866,0.9746763,0.00009672173,0.002902997,0.000001035218,0.02064927],"study_design_scores_gemma":[0.000345576,0.00005040475,0.000008094682,0.0001364701,0.0002069867,7.33737e-7,0.0006957593,0.9971726,0.0001067878,0.00101586,0.00009029225,0.0001704639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003114173,0.001763244,0.9942178,0.000004857453,0.00002918406,0.0003270662,0.000001742263,0.0001885657,0.0003534374],"genre_scores_gemma":[0.92365,0.0000203836,0.07605787,0.000009547959,0.00007693197,0.0001026648,0.00004462429,0.00001963138,0.00001828559],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9205359,"threshold_uncertainty_score":0.5662659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01417573017555905,"score_gpt":0.2288008029197531,"score_spread":0.2146250727441941,"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."}}