Embodiments describing an approach to aligning multiple time series, calculating an indicator function, estimating a coefficient vector based on the indicator function, and updating the coefficient vector. Additionally, embodiments comprise determining if a change in the coefficient vector is less than a predetermined value, and responsive to determining the change in the coefficient vector is less than the predetermined value outputting a target time series for controlling aluminum smelting.

Provided is a deployment platform for deployment of an industrial application on an edge computing device, ECD, connected to the controller of a machine tool, MT, the deployment platform including a model management component, MMC, adapted to instantiate a generic machine tool model, GMTM, stored in a memory to provide a machine instance model, MIM, of the respective machine tool, MT, on the basis of a machine tool data report, MTDR, received by the model management component, MMC, from the edge computing device, ECD, of the respective machine tool, MT, and further adapted to convert generic data requirements, gR, of a generic industrial application into machine tool specific requirements, mtsR, using the machine instance model, MIM, of the respective machine tool, MT, wherein an instantiated industrial application for the machine tool, MT, is provided by instantiating the generic industrial application by extending its configuration data with machine tool specific requirements.

Embodiments describing an approach to aligning multiple time series. Calculating an indicator function. Estimating a coefficient vector based on the indicator function. Updating the coefficient vector. Determining if a change in the coefficient vector is less than a predetermined value, and responsive to determining the change in the coefficient vector is less than the predetermined value, outputting a target time series for controlling aluminum smelting.

Provided is a deployment platform for deployment of an industrial application on an edge computing device, ECD, connected to the controller of a machine tool, MT, the deployment platform including a model management component, MMC, adapted to instantiate a generic machine tool model, GMTM, stored in a memory to provide a machine instance model, MIM, of the respective machine tool, MT, on the basis of a machine tool data report, MTDR, received by the model management component, MMC, from the edge computing device, ECD, of the respective machine tool, MT, and further adapted to convert generic data requirements, gR, of a generic industrial application into machine tool specific requirements, mtsR, using the machine instance model, MIM, of the respective machine tool, MT, wherein an instantiated industrial application for the machine tool, MT, is provided by instantiating the generic industrial application by extending its configuration data with machine tool specific requirements.

Embodiments describing an approach to aligning multiple time series, calculating an indicator function, estimating a coefficient vector based on the indicator function, and updating the coefficient vector. Additionally, embodiments comprise determining if a change in the coefficient vector is less than a predetermined value, and responsive to determining the change in the coefficient vector is less than the predetermined value outputting a target time series for controlling aluminum smelting.

Described herein are methods, apparatuses, and systems for determining adaptive predictive algorithms for virtual metrology. In some embodiments, a computer implemented method identifies a plurality of predictive algorithms. The method determines when to use one or more of the plurality of predictive algorithms to predict one or more virtual metrology variables in a manufacturing facility.

Implementations of the present disclosure generally relate to an improved factory interface that is coupled to an on-board metrology housing configured for measuring film properties of a substrate. In one implementation, an apparatus comprises a factory interface, and a metrology housing removably coupled to the factory interface through a load port, the metrology housing comprises an on-board metrology assembly for measuring properties of a substrate to be transferred into the metrology housing.

Embodiments describing an approach to aligning multiple time series. Calculating an indicator function. Estimating a coefficient vector based on the indicator function. Updating the coefficient vector. Determining if a change in the coefficient vector is less than a predetermined value, and responsive to determining the change in the coefficient vector is less than the predetermined value, outputting a target time series for controlling aluminum smelting.

Described herein are methods, apparatuses, and systems for determining adaptive predictive algorithms for virtual metrology. In some embodiments, a computer implemented method identifies a plurality of predictive algorithms. The method determines when to use one or more of the plurality of predictive algorithms to predict one or more virtual metrology variables in a manufacturing facility.

Described herein are methods, apparatuses, and systems for determining adaptive predictive algorithms for virtual metrology. In some embodiments, a computer implemented method identifies a plurality of predictive algorithms. The method determines when to use one or more of the plurality of predictive algorithms to predict one or more virtual metrology variables in a manufacturing facility.