{"id":"W4409986319","doi":"10.1016/j.cirpj.2025.04.005","title":"Two-stage LP/NLP feedrate optimization for spline toolpaths","year":2025,"lang":"en","type":"article","venue":"CIRP journal of manufacturing science and technology","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Pratt and Whitney Canada","keywords":"Stage (stratigraphy); Artificial intelligence; Spline (mechanical); Engineering drawing; Computer science; Computer vision; Mathematics; Engineering; Mechanical engineering; Geology","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":[0.0008370952,0.001112802,0.001273225,0.0005539273,0.0004542843,0.001062288,0.0011052,0.001795926,0.008717357],"category_scores_gemma":[0.002073453,0.0007519958,0.0009979926,0.0005958422,0.0006340928,0.0008531644,0.001156206,0.001468934,0.001153119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006544555,"about_ca_system_score_gemma":0.001169326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003251774,"about_ca_topic_score_gemma":0.003378267,"domain_scores_codex":[0.9996718,0.00008754552,0.00001212979,0.00004135855,0.0001270558,0.00006015246],"domain_scores_gemma":[0.9994831,0.0003123204,0.00003640365,0.00004777153,0.00008804756,0.00003222352],"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":[0.0001500439,0.00006770024,0.0002015703,0.0001222196,0.00001576186,0.00005616627,0.00004157476,0.9508014,0.004352067,0.00672432,0.0006777809,0.0367894],"study_design_scores_gemma":[0.00000667288,0.00002712201,0.00003214412,0.000005690301,0.000002269262,0.000006388668,0.000004681571,0.9978933,0.000793173,0.0008793406,0.0003454243,0.000003790442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0170279,0.0001276799,0.9752967,0.00007606759,0.00004363984,0.00005394612,0.00005265436,0.0002860908,0.007035357],"genre_scores_gemma":[0.5761721,0.0001853325,0.4091184,0.0000710968,0.00006295327,0.0003192783,0.0001905427,0.0003913566,0.01348893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008717357,"threshold_uncertainty_score":0.02916247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005453050098344694,"score_gpt":0.259780998776395,"score_spread":0.2543279486780504,"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."}}