{"id":"W2790466880","doi":"10.1007/s00170-017-1448-x","title":"A more accurate analytical formulation of surface roughness in layer-based additive manufacturing to enhance the product’s precision","year":2018,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Surface roughness; Surface (topology); Layer (electronics); Surface finish; Process (computing); Product (mathematics); Industrial and production engineering; Mechanical engineering; Mathematics; Algorithm; Process engineering; Mathematical optimization; Computer science; Engineering drawing; Engineering; Materials science; Geometry; Composite material","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005138409,0.0006441103,0.0005581649,0.0003535338,0.0001873231,0.0008997207,0.001002602,0.0008757943,0.001125351],"category_scores_gemma":[0.001412192,0.0003308112,0.000824743,0.0003339445,0.0002920484,0.001435244,0.0004277101,0.001137214,0.0005829378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004649584,"about_ca_system_score_gemma":0.0004856331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001830569,"about_ca_topic_score_gemma":0.002553775,"domain_scores_codex":[0.9995182,0.0000728162,0.00003042886,0.00007741611,0.0002701005,0.00003111675],"domain_scores_gemma":[0.9995748,0.0001469912,0.00003697992,0.00007647576,0.00015841,0.000006345093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007977121,0.0001489008,0.000764427,0.0005712385,0.0001119038,0.0001775949,0.0001844817,0.3881589,0.4461742,0.05182662,0.002825645,0.1089764],"study_design_scores_gemma":[0.000005001622,0.0000341558,0.0001986971,0.00001019016,0.00001405392,0.00003718186,0.00001052664,0.9678813,0.02799229,0.001396022,0.002404589,0.00001593489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01817582,0.0008217039,0.9764768,0.0001569277,0.0001956278,0.00004685079,0.00006639196,0.0002299475,0.00382987],"genre_scores_gemma":[0.5435495,0.001888352,0.4428415,0.0003071197,0.00014495,0.000130919,0.000209829,0.0002462127,0.01068157],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001830569,"threshold_uncertainty_score":0.003764749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439631223460824,"score_gpt":0.2978694972719503,"score_spread":0.283473185037342,"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."}}