An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

A system for solving an event-dependent multicriteria optimization problem of at least one cyber-physical system, comprising a control device for controlling the at least one cyber-physical system, the control device controlling the cyber-physical system in dependence on a list of prioritized objectives by solving at least one event-dependent suboptimization problem is characterized in that each objective from the list of prioritized objectives is captured as an objective function, each objective function consisting of at least two parts, a first part of which relates to directly capturing the objective and a second part of which describes a condition under which each result of one of the preceding objectives of each of the preceding suboptimization problems is substantially not negatively affected.

There are provided methods and systems for making or repairing a specified part. For example, there is provided a method for creating a manufacturing process to make or repair the specified part. The method includes receiving data from a plurality of sources, the data including as-designed, as-manufactured, as-simulated, as-operated, as-inspected, and as-tested data relative to one or more parts similar to the specified part. The method includes updating, in real time, a surrogate model corresponding with a physics-based model of the specified part, wherein the surrogate model forms a digital twin of the specified part. The method includes generating a multi-variant distribution including component performance and manufacturing variance, the manufacturing variance being associated with at least one of an additive manufacturing process step and a reductive manufacturing process step. The method includes comparing a performance from the multi-variant distribution with an expected performance of the new part based on the surrogate model. The method includes executing, based on the digital twin, the optimized process to either repair or make the specified part.

Systems and methods are described for managing articles. The systems and methods described herein may comprise an example method for manufacturing an article. The systems and methods provides an end-to-end manufacturing value chain as a closed system and feedback loop.

A computer-implemented method for retrieving data that relates to at least one industrial asset includes the steps of: receiving, from a requesting entity, a query for data objects from one or more lifecycle phases of the at least one industrial asset; mapping the query to one or more types of data objects that are available from one or more given data sources relating to the at least one industrial asset; obtaining, from the one or more data sources, one or more data objects of the one or more types; producing, from the one or more data objects, a response to the query; and transmitting the response to the requesting entity.

The present invention relates to a computer-implemented method for performing a chemical engineering process, in particular in an air separation plant or a natural gas plant, wherein a multiplicity of process simulations are performed simultaneously, in the course of each of which the process in the process plant is in each case simulated for a particular application case, wherein each application case is characterized by values of process plant variables and/or values of process parameters, wherein, in the multiplicity of process simulations, values for the process plant variables and/or for the process parameters are determined such that at least one predefined condition is met, wherein free values for process plant variables and/or process parameters are determined, and wherein dependent values for process plant variables and/or process parameters are determined from the free values for process plant variables and/or process parameters.

Systems, methods, and computer programming products for self-learning order dressing rules applied to manufacturing products in accordance with received product specifications. The translation from commercial characteristics to manufacturing characteristics of the product being manufactured are learned and adjusted to meet the specifications for quality required by the provided commercial characteristics. Reinforcement learning models learn from the quality characteristics of produced products by applying positive scores when the commercial to manufacturing characteristic translation is on-specification, otherwise a penalty is applied when an off-spec product is produced. Digital twins of manufacturing equipment, simulated in real time, provide insight and recommendations for achieving correct quality characteristics. Sensors in each device or within the surrounding environment help digital twins to measure operational performance and lifecycle of the manufacturing equipment against historical baselines. Reinforcement models dynamically adjust equipment settings for producing products to account for equipment performance degradation over time and changes in operation performance.

The program code, when executed by a processor, causes the processor to input fabrication data including a plurality of parameters associated with a semiconductor fabricating process to a framework to generate a first class for analyzing the fabrication data, to extract a first parameter targeted for analysis and a second parameter associated with the first parameter from the plurality of parameters and generate a second class for analyzing the first parameter as a sub class of the first class, to modify the first parameter and the second parameter into a data structure having a format appropriate to store in the second class, so as to be stored in the second class, to perform data analysis on the first parameter and the second parameter, to transform the first parameter and the second parameter into corresponding tensor data, and to input the tensor data to the machine learning model.

A method for processing a takt at a station includes: obtaining takt data of each station within a preset time period, and determining a takt boxplot of each station according to the takt data; obtaining a material blocking time, a material shortage time and a failure time in each takt, determining an effective takt of each station based on the takt data, the material blocking time, the material shortage time and the failure time, and determining an effective takt mode; obtaining planning takt data of each station, generating a station takt wall station based on the takt boxplot, the effective takt mode and the planning takt data; determining a takt fluctuation status and a bottleneck of each station according to the station takt wall. A system for processing a takt at a station, an apparatus, and a storage medium are also disclosed.

A non-transitory computer-readable storage medium storing a planning program that causes at least one computer to execute a process, the process includes specifying a process order of a plurality of types of objects in a work line for processing the plurality of types of objects; specifying a ratio of a number of objects of one of the plurality of types to the total number of the plurality of types for each of the plurality of types; acquiring an appearance probability obtained by subtracting the number of objects of a type of which the process order is determined, from a product of a process number and the ratio; selecting the type to be processed next by using the appearance probability; and changing a frequency of the selecting according to a completion ratio of a number of types which the selecting is completed to the total number of the plurality of types.