Embodiments of the invention are directed to collecting, by a computer system, sensor data of a manufacturing system, the sensor data being measured at intervals smaller than a time interval of a target measurement of the manufacturing system. The sensor data is determined to have a relationship to the target measurement. A synthetic target measurement is generated at an interval smaller than the time interval based on the relationship. An advance warning is automatically generated for the target measurement based on the synthetic target measurement within the interval smaller than the time interval.

The disclosure relates to a decentralized cyber-physical system including a managing unit and a plurality of components. The managing unit includes a root resilient manager including a root contract and a sub-contract generator. The sub-contract generator is configured to decompose the root contract into a plurality of sub-contracts and assign the plurality of sub-contracts to the plurality of components, respectively. Each component includes at least one observer configured to monitor if the property of the individual component violates the sub-contracts corresponding thereto. When one of the plurality of sub-contracts violates during the runtime of the decentralized cyber-physical system, the root resilience manager issues an alarm.

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

A product state estimation device includes: an examination result acquisition device that acquires an examination result related to a state of a product obtained through each shot by a die-casting machine; a time series data acquisition device that acquires time series data based on an output from a sensor that detects an operation state of the die-casting machine at each shot; a time series data manipulation device that performs manipulation that clips data of a predetermined time interval out of the time series data; an estimation model generation device that generates an estimation model by using a neural network with the examination result of the product and the manipulated time series data as learning data; and an estimation device that estimates information related to quality of the product based on the manipulated time series data obtained from a plurality of detection signals at each shot by using the estimation model.

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

A monitoring device monitors a status of a process in a production line in separate unit tasks. The production line includes a plurality of processes. The unit tasks form the process. The monitoring device includes an operational condition acquisition section, a determination section, and an output section. The operational condition acquisition section acquires, on a per-unit task basis, operational conditions of a device being used in the tasks. The determination section determines whether or not the acquired operational conditions are appropriate based on preset reference values. The output section outputs, on a per-unit task basis, information as to whether or not the process is being appropriately performed, based on results of the determination.

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

A computer-implemented system and method for searching comprises receiving digital twin instance part assembly information (IPAD) from a sensor scan of a physical part assembly produced by assembling a first physical part with a second physical part. Digital twin framework part assembly data (FPAD) is received representing a correctly assembled physical part assembly and that corresponds to the physical part assembly. Context data associated with a context within which the physical part assembly is produced is also received. The FPAD is compared with the IPAD, utilizing the context data, to determine whether a deviation of the IPAD from the FPAD exceeds a threshold. Responsive to the deviation exceeding a threshold, the method comprises providing corrective information to a device of an assembler for re-assembling the first physical part to the second physical part to produce a reassembled physical part assembly based on the corrective information.

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

The disclosure relates to a decentralized cyber-physical system including a managing unit and a plurality of components. The managing unit includes a root resilient manager including a root contract and a sub-contract generator. The sub-contract generator is configured to decompose the root contract into a plurality of sub-contracts and assign the plurality of sub-contracts to the plurality of components, respectively. Each component includes at least one observer configured to monitor if the property of the individual component violates the sub-contracts corresponding thereto. When one of the plurality of sub-contracts violates during the runtime of the decentralized cyber-physical system, the root resilience manager issues an alarm.