{"id":"W2761478393","doi":"10.1007/s12541-017-0162-9","title":"A parametric and accurate CAD model of flat end mills based on its grinding operations","year":2017,"lang":"en","type":"article","venue":"International Journal of Precision Engineering and Manufacturing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"CAD; End mill; Rake angle; Grinding; Rake; Parametric statistics; Parametric model; Machining; Representation (politics); Enhanced Data Rates for GSM Evolution; Mechanical engineering; Engineering; Grinding wheel; Mill; Engineering drawing; Parametric design; Helix angle; Process (computing); Computer science; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000153979,0.0005723101,0.0005219081,0.0004454859,0.0003277352,0.0009275873,0.001254263,0.001144882,0.0087871],"category_scores_gemma":[0.0003418512,0.0005208689,0.0006427269,0.0007109362,0.0003904378,0.0006058147,0.000266486,0.0004898779,0.001221356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003927143,"about_ca_system_score_gemma":0.0008548277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007972728,"about_ca_topic_score_gemma":0.007162236,"domain_scores_codex":[0.9998141,0.00002250735,0.00001034126,0.00003770522,0.00009929892,0.00001610514],"domain_scores_gemma":[0.9998529,0.0000458986,0.0000248665,0.00002872842,0.00004129241,0.0000063321],"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":[0.00005338087,0.00003561757,0.0004199276,0.00007562701,0.000007858218,0.0001869485,0.00005816086,0.9725568,0.005361042,0.004577375,0.0006896121,0.01597755],"study_design_scores_gemma":[0.00001384741,0.00005444528,0.001013954,0.000009386676,0.000008867205,0.0001332016,0.00002130624,0.9919325,0.001555268,0.001076491,0.004161155,0.00001958492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1594793,0.0009042108,0.7430544,0.0004569088,0.0002089994,0.0002972182,0.00335211,0.002380727,0.08986608],"genre_scores_gemma":[0.8982209,0.0007639108,0.07754572,0.00005556198,0.00002999203,0.0002039477,0.001345697,0.0001711874,0.02166305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0087871,"threshold_uncertainty_score":0.02939576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220018426147907,"score_gpt":0.2772178622069038,"score_spread":0.2552160195921131,"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."}}