{"id":"W4402452668","doi":"10.11159/mvml24.120","title":"Integrating Canny Filter and Convolutional Neural Networks for Quality Defect Detection in Injection Molding Process","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Injection Molding Process and Properties","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Centre National pour la Recherche Scientifique et Technique","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Process (computing); Filter (signal processing); Computer vision; Molding (decorative); Canny edge detector; Artificial neural network; Pattern recognition (psychology); Materials science; Image processing; Edge detection; Image (mathematics); Composite material","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004741592,0.0001313978,0.0001645889,0.0002732581,0.0001495169,0.0002924398,0.00009388214,0.00004130829,1.558602e-7],"category_scores_gemma":[0.0000418563,0.00009471684,0.0000279801,0.0006945903,0.00007054051,0.000247622,0.00002987535,0.0002321247,1.921971e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005632402,"about_ca_system_score_gemma":0.00001122486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000320597,"about_ca_topic_score_gemma":0.000007476406,"domain_scores_codex":[0.9991702,0.000004433758,0.0002198469,0.0002462262,0.0001423818,0.0002169007],"domain_scores_gemma":[0.999702,0.00009667395,0.00003355048,0.0000311046,0.00009031633,0.00004633068],"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":[0.00005402129,0.00001336133,0.003033723,0.002613421,0.00004265109,5.014247e-7,0.0006316715,0.8953168,0.00890564,0.02111351,0.00006724857,0.06820741],"study_design_scores_gemma":[0.00009973681,0.00008308708,0.001312712,0.000431778,0.000006337186,0.00003327988,0.000020428,0.9955487,0.00222252,0.00004396207,0.0000759254,0.0001215602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864901,0.002041158,0.009561047,0.00002623535,0.001486968,0.0002275182,0.000001333946,0.0001349083,0.00003068096],"genre_scores_gemma":[0.9996423,0.00002015844,0.00008907652,0.000008583908,0.0001433473,0.00005263778,1.059709e-7,0.00001129199,0.00003256535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1002318,"threshold_uncertainty_score":0.3862441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01147655329365626,"score_gpt":0.2292866163764096,"score_spread":0.2178100630827533,"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."}}