Model Predictive Control (MPC) of Injection Molding Machines
{'Code': '15030000', 'Directorate': {'Abbreviation': 'TIP', 'LongName': 'Dir for Tech, Innovation, & Partnerships'}, 'Division': {'Abbreviation': 'TI', 'LongName': 'Translational Impacts'}}
{'SignBlockName': 'Ritchie B. Coryell', 'PO_EMAI': None, 'PO_PHON': None}
Modern injection molding machines are much more sophisticated than              previous designs and include microprocessors for control of key                 parameters in order to enhance throughput and quality of molded parts.          However, they are susceptible to changes in operating conditions,               which can reduce performance, and which are not adequately controlled           by current single-loop PID controllers.                                                                                                                         This research is directed toward development of more advanced multi-            variate control methods which can adapt to changes in the machine               characteristics and provide more effective control of key parameters,           such as melt temperature, than current designs.  The Phase I research           has demonstrated feasibility by using a combined system identification          and model predictive control methodology, using data taken from an              operating machine at Cincinnati Milacron, Inc.                                                                                                                  Phase II will continue development of the identification and control            algorithms.  Particular emphasis will be placed on adequacy of the              identification algorithm for model order selection and handling of              dead time in the machine impulse response functions.  The control               algorithm will be designed for maximum robustness in the face of                variations in operating conditions.  Algorithms for on-line estimation          of unmeasured machine parameters, such as melt temperature, will be             developed.  A performance monitoring algorithm will be employed to              detect the need for reidentification of the system, utilizing                   statistical tests on the measured machine parameters.
{'FirstName': 'Donald', 'LastName': 'Gustafson', 'PI_MID_INIT': 'E', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Donald E Gustafson', 'EmailAddress': None, 'PI_PHON': None, 'NSF_ID': '000276576', 'StartDate': '05/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Scientific Systems Inc', 'CityName': 'Cambridge', 'ZipCode': '021402325', 'PhoneNumber': '6176616364', 'StreetAddress': 'One Alewife Place', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'MA07', 'ORG_UEI_NUM': None, 'ORG_LGL_BUS_NAME': None, 'ORG_PRNT_UEI_NUM': None}
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{'Code': '5373', 'Text': 'SBIR Phase II'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8706113.xml'}