{"id":"W3196439122","doi":"10.1016/j.procir.2020.11.008","title":"High performance grinding of titanium alloys with electroplated diamond wheels","year":2021,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Electroplating; Materials science; Metallurgy; Grinding; Surface roughness; Diamond; Titanium; Surface finish; Titanium alloy; Diamond grinding; Grinding wheel; Composite material; Alloy; Layer (electronics)","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":[0.0005196542,0.0003296888,0.0004957826,0.0004699151,0.0002453643,0.0003372795,0.0005529968,0.0003415218,0.0008310981],"category_scores_gemma":[0.0007151895,0.000256213,0.0003396134,0.0003236419,0.0003341734,0.0002491458,0.0002934388,0.0003116734,0.0002326316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004426565,"about_ca_system_score_gemma":0.0003156381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002753094,"about_ca_topic_score_gemma":0.01081033,"domain_scores_codex":[0.9994736,0.00002659576,0.00002003883,0.00006847402,0.0003247005,0.00008667112],"domain_scores_gemma":[0.999579,0.0001235254,0.00007269352,0.00006782919,0.0001236974,0.00003330095],"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":[0.0000638533,0.00001550909,0.0004268349,0.00006583362,0.000005899607,0.00005728158,0.00005564197,0.0006409799,0.9948483,0.00004149759,0.00003211858,0.003746316],"study_design_scores_gemma":[0.00001259827,0.000333822,0.008637425,0.000005187112,0.00001162198,0.0000994815,0.00004142152,0.002902371,0.9866443,0.00003158009,0.001270314,0.000009859965],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917125,0.0007059393,0.006559152,0.00001615055,0.00002507274,0.0000194264,0.00005885627,0.00006308842,0.0008398564],"genre_scores_gemma":[0.9844503,0.000337338,0.01320889,0.00001039574,0.000005514862,0.000009005801,0.0001051501,0.00004258388,0.00183089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002753094,"threshold_uncertainty_score":0.005474091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003579963357170561,"score_gpt":0.1748881087462988,"score_spread":0.1713081453891282,"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."}}