Abstract: Techniques for lane edge detection on an electrode sheet of a battery are described. In one aspect, scan data corresponding to a coating thickness of a plurality of bins distributed into a number of rows and columns across the electrode sheet is obtained. A first average coating thickness is computed for each column of the electrode sheet based on a number of bins identified in the column, based on which, a second average coating thickness is calculated for a first set of columns of the electrode sheet. A difference between the second average coating thickness and a standard deviation value is identified to compute an average minimum edge coating thickness, which is then compared with the first average coating thickness for each column to detect an edge transition of coated to uncoated region of the electrode sheet. Accordingly, a lane edge is determined for the column.

Abstract: Methods, apparatuses, and computer program products for production tracking are provided. For example, a computer-implemented method may include receiving user selections corresponding to (i) equipment stations of a production line, (ii) links from each equipment station to preceding and/or succeeding equipment stations, and (iii) data to be captured each time a turnup event indicating completion of a production unit occurs; creating a graphic representation of the production line; each time a turnup event occurs at one of the equipment stations, determining (i) any immediately preceding equipment stations, (ii) a turnup start time, and (iii) one or more parent production units created by the immediately preceding equipment station immediately prior to the turnup event; obtaining quality status information for each production unit in a parent/child sequence; and displaying the graphic representation of the production line with the quality status information for each of the production units.

Abstract: Methods, apparatuses, and computer program products for quality control are provided. For example, a computer-implemented method may include, each time a turnup event occurs indicating completion of a production unit at one of a plurality of equipment stations of a production line, receiving one or more turnup properties corresponding to the turnup event; selecting a predefined quality procedure matching one or more of the turnup properties; retrieving data related to one or more quality parameters defined in the selected quality procedure, from one or more data sources defined in the selected quality procedure; comparing the retrieved data to one or more tolerances corresponding to the one or more quality parameters defined in the selected quality procedure; and, based on results of the comparing, executing a quality disposition action defined in the respective quality parameter of the selected quality procedure.

Abstract: Example methods, apparatuses, systems, and computer program products are provided. For example, an example computer-implemented method includes receiving a plurality of runtime facility process variable indicators and a plurality of runtime derived process metric indicators, receiving a facility state tree data object that comprises a plurality of facility state tree nodes corresponding to a plurality of facility state indicators; generating a runtime facility state indicator, generating a runtime facility score indicator associated with the runtime facility state indicator, and generating a remote operator assistance data object associated with the facility indicator.

Abstract: Example methods, apparatuses, systems, and computer program products are provided. For example, an example computer-implemented method includes receiving a battery manufacturing operation parameter indicator, determining whether the battery manufacturing operation parameter indicator satisfies a battery manufacturing parameter threshold indicator, and in response to determining that the battery manufacturing operation parameter indicator does not satisfy the battery manufacturing parameter threshold indicator, the example computer-implemented method comprises: generating a battery manufacturing deviation event data object, and generating a battery manufacturing adjustment data object based at least in part on inputting the battery manufacturing deviation event data object to one or more machine learning models.

Abstract: Example methods, apparatuses, systems, and computer program products are provided. For example, an example computer-implemented method includes receiving a plurality of runtime hydrogen production variable indicators from a hydrogen production control system associated with a hydrogen production facility, generating at least one predicted hydrogen production operation indicator. Further, in response to determining that the at least one predicted hydrogen production operation indicator does not satisfy the at least one corresponding hydrogen production operation threshold indicator, generating an adjusted hydrogen production variable indicator corresponding to a runtime hydrogen production variable indicator, and transmitting the adjusted hydrogen production variable indicator to the hydrogen production control system.

Abstract: A system and method include receiving time series values of one or more sensed parameters of a product manufacturing processes. The received time series values are compared to desired time series values to detect one or more anomalies in the received time series values. An action to apply to the product manufacturing process product is determined based on the one or more detected anomalies is orchestrated by an intelligent workflow engine, that derives inferences from analytics, heuristics rules, skills, and a knowledge library.