{"id":"W4382654700","doi":"10.1016/j.powtec.2023.118766","title":"Ball milling process variables optimization for high-entropy alloy development using design of experiment and genetic algorithm","year":2023,"lang":"en","type":"article","venue":"Powder Technology","topic":"High Entropy Alloys Studies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Taguchi methods; Ball mill; Materials science; Alloy; Particle size; Design of experiments; Process engineering; Mechanical engineering; Mathematical optimization; Metallurgy; Engineering; Mathematics; Composite material; Statistics; Chemical engineering","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":[0.001941836,0.0008867884,0.001257965,0.0008003333,0.0005256244,0.0008258487,0.000780553,0.0007893189,0.001208461],"category_scores_gemma":[0.001864089,0.0005764439,0.001081958,0.0006411563,0.0004473059,0.0005814446,0.0004595967,0.0007853643,0.0001088771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006609837,"about_ca_system_score_gemma":0.001222908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002338569,"about_ca_topic_score_gemma":0.002680441,"domain_scores_codex":[0.9994405,0.000209972,0.00003084095,0.000114745,0.0001440213,0.00005992386],"domain_scores_gemma":[0.9987085,0.0008861636,0.0001408571,0.00004828052,0.0001930838,0.00002307614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002633661,0.0002728176,0.00114425,0.0002426856,0.00008609158,0.00003937912,0.00007285088,0.943653,0.01576238,0.001685292,0.0001532413,0.03662457],"study_design_scores_gemma":[0.00004969116,0.0003240069,0.0004951431,0.000006513308,0.0000483416,0.000008448869,0.00001501619,0.9912766,0.007117903,0.0003391526,0.0003112488,0.000008020067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2063082,0.000504884,0.7894846,0.00008146907,0.00004684339,0.0002783738,0.0000579301,0.000392262,0.002845329],"genre_scores_gemma":[0.7746032,0.0001938678,0.2239526,0.00002244572,0.000008696576,0.0003274508,0.00006250229,0.00003513897,0.0007941241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002338569,"threshold_uncertainty_score":0.01026952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01935163184870067,"score_gpt":0.2425271816216248,"score_spread":0.2231755497729241,"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."}}