{"id":"W3192368537","doi":"10.1007/s00170-021-07808-7","title":"Combining PMEDM with the tool electrode sloshing to reduce recast layer of titanium alloy generated from EDM","year":2021,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced Machining and Optimization Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Science and Technology Planning Project of Shenzhen Municipality; National Natural Science Foundation of China","keywords":"Materials science; Electrical discharge machining; Titanium alloy; Titanium; Electrode; Alloy; Surface roughness; Machining; Layer (electronics); Groove (engineering); Metallurgy; Surface modification; Composite material; 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":[0.0001726085,0.0001542247,0.0002292162,0.0001899262,0.00006877632,0.00003692241,0.0007627309,0.00007693623,0.00002379867],"category_scores_gemma":[0.0001065677,0.0001009567,0.00005332599,0.0001864997,0.00005919707,0.000165807,0.0001249127,0.000540415,0.000001786397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001124877,"about_ca_system_score_gemma":0.00004516269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004819834,"about_ca_topic_score_gemma":0.00001667163,"domain_scores_codex":[0.9989812,0.00002686302,0.000378072,0.0001370071,0.0002953079,0.0001815464],"domain_scores_gemma":[0.9989696,0.0001475584,0.0002433748,0.0002754383,0.0003353677,0.00002865058],"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":[0.0001096014,0.00001605069,0.00002025328,0.000003334886,0.0002009836,0.00004226427,0.0001557499,0.5100513,0.4690672,0.0006880642,0.0003769285,0.01926837],"study_design_scores_gemma":[0.0003725778,0.00008349725,0.00009179206,0.0001246019,0.00002320151,0.0002918659,0.0002907634,0.001191303,0.9909371,0.002888962,0.003583212,0.00012114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8574992,0.0003184582,0.1367157,0.004536274,0.0003917419,0.00008499237,0.000008820175,0.0001966042,0.0002482357],"genre_scores_gemma":[0.9137933,0.0002469181,0.08554843,0.0001882233,0.0001109768,0.000008428034,0.000005633848,0.00003390603,0.00006424648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5218699,"threshold_uncertainty_score":0.4116895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007626760291117642,"score_gpt":0.238219314631514,"score_spread":0.2305925543403964,"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."}}