{"id":"W3215403001","doi":"10.14733/cadaps.2022.624-661","title":"A Prototype of an Automated Feature Recognition Algorithm for Aerospace Sheet Metal Parts","year":2021,"lang":"en","type":"article","venue":"Computer-Aided Design and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"CAD; Computer Aided Design; Engineering drawing; Computer-aided manufacturing; Aerospace; Computer science; Electronic design automation; Feature recognition; Domain (mathematical analysis); Feature (linguistics); Engineering; Manufacturing engineering; Feature extraction; Artificial intelligence; Mechanical engineering; Embedded system","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":[0.0004466448,0.001017305,0.0009216084,0.001061713,0.0005071147,0.001025669,0.002357547,0.00138739,0.01802784],"category_scores_gemma":[0.001090317,0.0005375128,0.0008127433,0.0008988317,0.0002587765,0.001144114,0.0006917923,0.0007501349,0.01029469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003259609,"about_ca_system_score_gemma":0.0006417087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00279368,"about_ca_topic_score_gemma":0.003859827,"domain_scores_codex":[0.9996101,0.00002360127,0.0000240008,0.0001004976,0.0002073277,0.00003449684],"domain_scores_gemma":[0.9996729,0.00008677656,0.00001877384,0.00005491039,0.000140639,0.00002596446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002512494,0.0001036796,0.0004994319,0.0001408664,0.00007971652,0.0002037272,0.00005295801,0.009506881,0.07513168,0.001651915,0.0174265,0.8949515],"study_design_scores_gemma":[0.0002189841,0.0006071746,0.002536041,0.00005892859,0.0001066537,0.001536268,0.00008860906,0.8452041,0.08608906,0.003748135,0.0597159,0.00009016561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00442506,0.0001955767,0.9749825,0.0001037317,0.0001415687,0.0001423334,0.0001656602,0.01752944,0.002314111],"genre_scores_gemma":[0.04603605,0.0001348806,0.9437012,0.0001855528,0.00004593708,0.000212618,0.000651405,0.0005426897,0.008489714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01802784,"threshold_uncertainty_score":0.06030911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03323692485617578,"score_gpt":0.2689035612067446,"score_spread":0.2356666363505688,"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."}}