{"id":"W2093752567","doi":"10.1016/j.jmsy.2012.09.002","title":"Fuzzy approach to select machining parameters in electrical discharge machining (EDM) and ultrasonic-assisted EDM processes","year":2012,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Advanced Machining and Optimization Techniques","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Electrical discharge machining; Machining; Surface roughness; Mechanical engineering; Fuzzy logic; Ultrasonic sensor; Engineering; Materials science; Computer science; Acoustics; Composite material; Artificial intelligence; Physics","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":[0.0007470089,0.0004563203,0.000614085,0.0007274976,0.0004797514,0.0008258955,0.0007869634,0.0007551702,0.0007789299],"category_scores_gemma":[0.001449067,0.0003354077,0.0005602933,0.000515459,0.0003403215,0.0005012096,0.0002652222,0.0004267722,0.00009993403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007622521,"about_ca_system_score_gemma":0.0006260778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003656207,"about_ca_topic_score_gemma":0.004659792,"domain_scores_codex":[0.9996525,0.0000737819,0.00003001984,0.00005173176,0.0001620015,0.00003003276],"domain_scores_gemma":[0.9994695,0.0002611548,0.00004618897,0.00001475477,0.0001915429,0.00001685251],"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":[0.0005498056,0.0002349371,0.002226943,0.0004665492,0.0001506617,0.0002659375,0.0002431097,0.7298114,0.0398584,0.01582032,0.0008852273,0.2094867],"study_design_scores_gemma":[0.00002262886,0.0001036372,0.000732135,0.00002080856,0.00004234208,0.00004236939,0.00004104495,0.9899966,0.005432426,0.002969768,0.0005794491,0.00001689213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09152259,0.0006482567,0.9025258,0.0001188653,0.00005069348,0.00007289044,0.00004577373,0.00008493739,0.004930241],"genre_scores_gemma":[0.848921,0.000299859,0.1488459,0.00004638395,0.00002533884,0.00009743411,0.00004044295,0.00001108777,0.001712505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003656207,"threshold_uncertainty_score":0.007269859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378585522508007,"score_gpt":0.2355224830187623,"score_spread":0.2217366277936822,"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."}}