{"id":"W3087775605","doi":"10.1016/j.procir.2020.04.044","title":"A Genetic Algorithm-Based Model for Product Platform Design for Hybrid Manufacturing","year":2020,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Variety (cybernetics); Product proliferation; Product (mathematics); Genetic algorithm; Order (exchange); Manufacturing engineering; Build to order; Product design; Computer science; Engineering; New product development; Industrial engineering; Mathematics; Artificial intelligence; Production (economics); Product management; Machine learning; Business","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":[0.0005971367,0.00106641,0.0008357139,0.0008966202,0.0005824241,0.001488363,0.001767116,0.002048913,0.004636385],"category_scores_gemma":[0.001121442,0.0004984116,0.001064483,0.00114084,0.0007709225,0.0007270163,0.0007384666,0.0009937235,0.0005681983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001583695,"about_ca_system_score_gemma":0.001711718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01716015,"about_ca_topic_score_gemma":0.01004322,"domain_scores_codex":[0.9996896,0.0001015273,0.00001290402,0.00006013157,0.00009701823,0.00003888199],"domain_scores_gemma":[0.9997417,0.0001472381,0.00003062368,0.00001075753,0.00005589055,0.00001376376],"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":[0.000006233098,0.000008772065,0.00006179259,0.00001363378,0.000006607781,0.00002417766,0.00001065175,0.9931539,0.0002593918,0.003611694,0.0001116024,0.0027315],"study_design_scores_gemma":[0.000004663268,0.00001065685,0.00002179027,0.000003860119,0.000003785382,0.000005899742,0.000002625994,0.9983124,0.0000689301,0.001227377,0.0003353477,0.000002673184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0103839,0.0003393641,0.9772261,0.0001882187,0.00004031807,0.00009739055,0.00009794708,0.0001768611,0.01144978],"genre_scores_gemma":[0.6351306,0.0009500966,0.3491552,0.0001309395,0.00003689966,0.001071859,0.0003267442,0.00008477581,0.01311291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01716015,"threshold_uncertainty_score":0.0341205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04856195270829058,"score_gpt":0.216345989217419,"score_spread":0.1677840365091284,"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."}}