{"id":"W2143664202","doi":"10.5267/j.ijiec.2014.11.001","title":"Response surface and artificial neural network prediction model and optimization for surface roughness in machining","year":2015,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Response surface methodology; Surface roughness; Artificial neural network; Machining; Parametric statistics; Parametric model; Materials science; Coefficient of determination; Design of experiments; Mathematics; Biological system; Computer science; Statistics; Artificial intelligence; Composite material; Metallurgy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006854176,0.0006413705,0.0005415693,0.0003754636,0.0001990028,0.0005269955,0.0005710462,0.0008232378,0.0009469413],"category_scores_gemma":[0.001116643,0.0003654717,0.0007067329,0.0004383133,0.0002800561,0.0005288745,0.0002631342,0.0005552436,0.0002015227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004606733,"about_ca_system_score_gemma":0.0004983295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006672028,"about_ca_topic_score_gemma":0.004332362,"domain_scores_codex":[0.9996177,0.0001281499,0.000020491,0.00007335073,0.0001245466,0.00003571344],"domain_scores_gemma":[0.9996666,0.0001820293,0.00004093438,0.00001896848,0.00008309066,0.000008505198],"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":[0.00002780516,0.00002373249,0.0005954414,0.00003962387,0.00001618351,0.00002565534,0.00001056252,0.9857826,0.001983517,0.0003152392,0.0001140169,0.01106566],"study_design_scores_gemma":[0.000001168318,0.00001474937,0.0001811259,9.476166e-7,0.000001805726,0.000002705284,0.000001352478,0.9993206,0.0003594342,0.00007261154,0.00004165807,0.000001761852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1866871,0.0005647046,0.8082952,0.0001768303,0.00003980623,0.00005450162,0.00007592158,0.000698922,0.003406925],"genre_scores_gemma":[0.9492452,0.0002271143,0.04818249,0.00002748454,0.00001103233,0.0001081565,0.0001129005,0.00003582661,0.002049912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006672028,"threshold_uncertainty_score":0.01326638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03944196855066009,"score_gpt":0.2795422055519603,"score_spread":0.2401002370013002,"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."}}