{"id":"W2074671958","doi":"10.1243/14644207jmda187","title":"Deformation behaviour of aluminium during machining: Modelling by Eulerian and smoothed-particle hydrodynamics methods","year":2008,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Machining; Materials science; Eulerian path; Mechanics; Finite element method; Deformation (meteorology); Constitutive equation; Chip formation; Parametric statistics; Plasticity; Flow stress; Smoothed-particle hydrodynamics; Stress (linguistics); Composite material; Strain rate; Structural engineering; Metallurgy; Physics; Mathematics; Engineering; Tool wear","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":[0.0007458281,0.0001063399,0.0002797935,0.00007937619,0.00006876619,0.000010675,0.0001438137,0.00007891294,0.00000284863],"category_scores_gemma":[0.00004816796,0.00008698535,0.00004781645,0.0001312055,0.00008834076,0.000267832,0.00002988258,0.00009020438,9.49798e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002430534,"about_ca_system_score_gemma":0.00001406499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002254184,"about_ca_topic_score_gemma":2.100528e-8,"domain_scores_codex":[0.9989328,0.00001576518,0.0007234215,0.00007077663,0.0001642977,0.00009289889],"domain_scores_gemma":[0.9992916,0.00004075291,0.0003919662,0.00006531911,0.0001549999,0.00005534383],"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":[0.00002810235,0.00002820984,0.00001720165,0.0001719888,0.00002661893,4.622663e-8,0.0001453592,0.04728623,0.9384643,0.01366354,0.00001019086,0.0001581603],"study_design_scores_gemma":[0.0002306007,0.00004439726,0.00003420428,0.00009249173,0.00005052432,0.00004433648,0.000033993,0.1101097,0.8881155,0.001130082,0.0000445398,0.00006964758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6187755,0.00007930172,0.3808842,0.00001210485,0.00004378204,0.0001652922,0.000008131242,0.00002007162,0.00001163441],"genre_scores_gemma":[0.9300482,0.0003092936,0.06958856,0.000002029823,0.00002116659,0.00001431014,0.000001052549,0.00001184766,0.000003564408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3112956,"threshold_uncertainty_score":0.3547159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02063521445751186,"score_gpt":0.2464718974420362,"score_spread":0.2258366829845244,"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."}}