{"id":"W4394064432","doi":"10.2316/j.2024.201-0405","title":"APPLICATION OF CNC MACHINING FOR ELECTROMECHANICAL PARTS BASED ON MACHINE VISION TECHNOLOGY, 156-165.","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Machining; Numerical control; Machine vision; Engineering drawing; Manufacturing engineering; Computer vision; Computer science; Engineering; Artificial intelligence; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003950924,0.0003172431,0.0002323031,0.001173373,0.0005284668,0.0006039318,0.0004846386,0.000630396,0.003496093],"category_scores_gemma":[0.0004219414,0.0002397454,0.0002581767,0.0006621362,0.0004937775,0.0005329349,0.0003570185,0.0004142381,0.0006127955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009229149,"about_ca_system_score_gemma":0.0009352793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005933015,"about_ca_topic_score_gemma":0.01437296,"domain_scores_codex":[0.9996817,0.00002471192,0.0000105206,0.00004248448,0.0002222406,0.00001839691],"domain_scores_gemma":[0.9998415,0.00002761641,0.000009555006,0.00001531114,0.00009766259,0.000008423326],"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":[0.0002272045,0.00009298151,0.002545635,0.0008177969,0.00003118622,0.0006418466,0.0002112943,0.005291486,0.5423709,0.020822,0.01243814,0.4145095],"study_design_scores_gemma":[0.00003773018,0.0003484106,0.02361685,0.0001045338,0.00008992906,0.002044936,0.0001789064,0.0504581,0.7413948,0.005431235,0.1762265,0.00006807729],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2381887,0.06080786,0.4344779,0.00200628,0.003257229,0.0004633024,0.0005606805,0.001277154,0.2589611],"genre_scores_gemma":[0.7996548,0.008986873,0.1378077,0.0002211114,0.0001473376,0.00005989091,0.0003353892,0.00009393506,0.05269292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005933015,"threshold_uncertainty_score":0.01179695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002621170030004999,"score_gpt":0.2221454152562177,"score_spread":0.2195242452262127,"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."}}