{"id":"W2068507382","doi":"10.1007/s00170-014-5835-2","title":"Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE)","year":2014,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":347,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sheridan College","funders":"","keywords":"Raster graphics; Ultimate tensile strength; Process (computing); Orientation (vector space); Process variable; Differential evolution; Response surface methodology; Air gap (plumbing); Design of experiments; Group method of data handling; Fused deposition modeling; Raster data; Materials science; Mechanical engineering; Structural engineering; Computer science; Mathematics; Engineering; Algorithm; Composite material; Geometry; Artificial intelligence; 3D printing; Statistics; Machine learning","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":[0.0004195481,0.0006030776,0.0008288311,0.0003809168,0.0004948515,0.0006889022,0.0008717602,0.001003386,0.001409628],"category_scores_gemma":[0.0009239989,0.0004739033,0.0008496483,0.0006634444,0.0002976277,0.0007102059,0.0005144647,0.0008639231,0.0002586034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006332265,"about_ca_system_score_gemma":0.001085893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005311611,"about_ca_topic_score_gemma":0.00454332,"domain_scores_codex":[0.9998121,0.00002663835,0.00001444783,0.00003955428,0.0000922362,0.00001504503],"domain_scores_gemma":[0.999715,0.0001367406,0.00003266276,0.00004129822,0.00006619866,0.000008096751],"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":[0.00003307988,0.00003261458,0.0004915292,0.0001027191,0.00002305851,0.00004082717,0.0000605427,0.9550455,0.01191726,0.003072972,0.0003875436,0.02879236],"study_design_scores_gemma":[0.000003755116,0.000008783772,0.00008984174,0.000002668338,0.00000387626,0.000008680601,0.000003476264,0.995621,0.003373062,0.000381929,0.0004984504,0.000004406391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05630557,0.0002606242,0.9369499,0.0001204429,0.00008644602,0.00007387839,0.0002814507,0.0008989662,0.0050227],"genre_scores_gemma":[0.6077553,0.0002756593,0.3883333,0.00005270779,0.00002033218,0.0001863493,0.0003531115,0.0001925386,0.002830693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005311611,"threshold_uncertainty_score":0.01056141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02001712045236692,"score_gpt":0.2716361348650239,"score_spread":0.251619014412657,"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."}}