{"id":"W2759297652","doi":"10.1115/1.4038000","title":"Time-Domain Modeling of Varying Dynamic Characteristics in Thin-Wall Machining Using Perturbation and Reduced-Order Substructuring Methods","year":2017,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Perturbation (astronomy); Machining; Stiffness; Frequency domain; Time domain; Eigenvalues and eigenvectors; Rayleigh quotient; Algorithm; Computer science; Mathematical analysis; Mathematics; Control theory (sociology); Structural engineering; Engineering; Physics; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001102443,0.0001637946,0.0003015652,0.000398006,0.0002315353,0.0001631946,0.0002398803,0.00005715948,0.00000142009],"category_scores_gemma":[0.0003021051,0.0001588217,0.00002439863,0.00009883813,0.00007886728,0.00122545,0.00006687984,0.0003008849,4.963108e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000110269,"about_ca_system_score_gemma":0.00003482545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008344737,"about_ca_topic_score_gemma":5.095095e-7,"domain_scores_codex":[0.9989003,0.000009994276,0.0004545073,0.0001568106,0.0002288541,0.0002495679],"domain_scores_gemma":[0.9993672,0.00005498644,0.0002501052,0.0001476999,0.00009643366,0.00008353461],"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":[0.00000712492,0.000002439646,0.00008058464,0.0001578298,0.000008691033,0.000004594505,0.0009233716,0.9092306,0.07984646,0.00001090794,2.701754e-8,0.009727345],"study_design_scores_gemma":[0.0002275622,0.00001561573,0.004886213,0.0004068016,0.00001489232,0.0001121854,0.00007253487,0.981108,0.01274407,0.000247093,0.000004090331,0.0001609143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7593811,0.0003064743,0.2400036,0.00001334611,0.0001923217,0.00003790526,5.472781e-7,0.00001807162,0.00004659986],"genre_scores_gemma":[0.81152,0.0002197672,0.1882019,0.000002984035,0.00003161254,4.28966e-7,2.666578e-7,0.00002095197,0.000002084925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0718774,"threshold_uncertainty_score":0.647656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287627847781674,"score_gpt":0.2771429759062842,"score_spread":0.2642666974284674,"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."}}