{"id":"W1991156363","doi":"10.1016/s0019-0578(07)60204-3","title":"Self-optimizing MPC of melt temperature in injection moulding","year":2002,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Injection moulding; Setpoint; Temperature control; Overheating (electricity); Injection molding machine; Overshoot (microwave communication); Model predictive control; Plastics industry; Thermal; Process (computing); Materials science; Control theory (sociology); Mechanical engineering; Computer science; Process engineering; Engineering; Composite material; Control (management); Mold","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":[0.0004094921,0.0005015836,0.0006928203,0.0002595579,0.0003602391,0.0004412626,0.0003643731,0.0004015859,0.000712465],"category_scores_gemma":[0.0006643688,0.0003384507,0.0003477225,0.0002288915,0.000386309,0.0004106438,0.0004315422,0.0005443364,0.00008528321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005437627,"about_ca_system_score_gemma":0.0005054541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004823785,"about_ca_topic_score_gemma":0.004411688,"domain_scores_codex":[0.9998908,0.00002674207,0.000004089032,0.00002387802,0.00002728454,0.00002724066],"domain_scores_gemma":[0.9997618,0.0001107666,0.00004409992,0.00002146595,0.00004826331,0.00001359736],"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":[0.0001157391,0.00003810966,0.0004168744,0.00002757848,0.00001401147,0.00001565717,0.00001671735,0.975467,0.006507436,0.0008960188,0.0001299858,0.0163548],"study_design_scores_gemma":[0.000003242068,0.0000220848,0.0001162466,7.961382e-7,0.00000241292,9.31445e-7,0.000001167756,0.9980662,0.001601267,0.0001364905,0.00004783196,0.000001257621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4762288,0.0004138889,0.5169672,0.0001924102,0.00007526196,0.00004732027,0.00005844484,0.0008373433,0.005179303],"genre_scores_gemma":[0.9931648,0.00002694638,0.006108265,0.000007626845,0.000005708649,0.00001118298,0.00001251714,0.00002070489,0.0006422458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004823785,"threshold_uncertainty_score":0.009591401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006315812016535844,"score_gpt":0.1848475479800837,"score_spread":0.1785317359635479,"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."}}