首页|"Threshold Determination For Predictive Process Control Of Factory Processes, Eq uipment And Automated Systems" in Patent Application Approval Process (USPTO 202 40085863)
"Threshold Determination For Predictive Process Control Of Factory Processes, Eq uipment And Automated Systems" in Patent Application Approval Process (USPTO 202 40085863)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A patent application by the inventors Bordelanne, Valerie (New York, NY, US); Constantin, Sarah (Brooklyn, NY, US); Le e, Jonathan (Brooklyn, NY, US); Limoge, Damas (Brooklyn, NY, US); Putman, John B . (Celebration, FL, US), filed on August 31, 2023, was made available online on March 14, 2024, according to news reporting originating from Washington, D.C., b y NewsRx correspondents. This patent application is assigned to Nanotronics Imaging Inc. (Cuyahoga Falls, Ohio, United States). The following quote was obtained by the news editors from the background informa tion supplied by the inventors: "To manufacture products that consistently meet desired design specifications, safely, timely and with minimum waste, requires c onstant monitoring and adjustments to the manufacturing process." In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors' summary information for this pat ent application: "In some embodiments, a manufacturing system is disclosed herei n. The manufacturing system includes one or more process stations, a station con trol system, and a controller. The one or more process stations are configured t o execute a manufacturing process. The manufacturing process includes an initial set of processing parameters. The station control system is configured to contr ol the one or more process stations. The controller is in communication with the one or more process stations and the station control system. The controller is configured to perform operations. The operations include receiving desired proce ss values associated with the one or more process stations. The operations furth er include receiving desired target values for one or more key performance indic ators of the manufacturing process. The operations further include simulating, b y a deep learning processor, the manufacturing process to generate expected proc ess values and expected target values for the one or more key performance indica tors to optimize the one or more key performance indicators. The simulating incl udes generating a proposed state change of at least one processing parameter of the initial set of processing parameters. The operations further include determi ning, by the deep learning processor, that expected process values and the expec ted target values are within an acceptable limit of the desired process values a nd the desired target values. The operations further include, based on the deter mining, causing a change to the initial set of processing parameters based on th e proposed state change.
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