A system includes a memory and a processing device, operatively coupled to the memory, to perform operations including receiving, as input to a trained machine learning model for identifying defect impact with respect to at least one type defect type, data associated with a process related to electronic device manufacturing. The data associated with the process comprises at least one of: an input set of recipe settings for processing a component, a set of desired characteristics to be achieved by processing the component, or a set of constraints specifying an allowable range for each setting of the set of recipe settings. The operations further include obtaining an output by applying the data associated with the process to the trained machine learning model. The output is representative of the defect impact with respect to the at least one defect type.

Various embodiments of the teachings herein include a method for building a graph-based industrial flow model. The method may comprise: importing a set of entities based on an industrial flow including a user-defined entity and a system-defined entity; querying the model for the user-defined entity, and if found, obtaining a connection of the user-defined entity, otherwise generating a new implementation layer; querying the model for an implementation layer of the user-defined entity, and if found, obtaining a connection of the implementation layer of the user-defined entity, otherwise generating a new implementation layer; querying the model for the system-defined entity, and if the system-defined entity is found, obtaining a connection of the system-defined entity, otherwise generating a new implementation layer; and iteratively performing the above queries until a final graph-based model for the industrial flow results.

Various embodiments described herein relate to management of industrial process optimization related to batch operations. In this regard, an optimization request to optimize an industrial process that produces an industrial process product is received. In response to the optimization request, product spent characteristics for one or more blending components of a batch operation subprocess are determined. Also in response to the optimization request, demand data for one or more feed products associated with the one or more blending components is updated based on the product spent characteristics and inventory data indicative of an inventory level for the one or more feed products. Furthermore, a control signal configured based on the demand data is transmitted to a controller configured for optimization associated with the industrial process that produces the industrial process product.

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.

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.

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.

An industrial plant machine learning system includes a machine learning model, providing machine learning data, an industrial plant providing plant data and an abstraction layer, connecting the machine learning model and the industrial plant, wherein the abstraction layer is configured to provide standardized communication between the machine learning model and the industrial plant, using a machine learning markup language.

The invention relates to a production machine with a control program for visualizing stations and/or machine components, highlighting the speed-determining station and/or the speed-determining machine component in order to optimize the process time. The invention also relates to a method of optimizing the process time for a production machine with such a control program and to a data carrier with such a control program.

Provided are a system, method, and computer program product for optimizing a manufacturing process. The method includes receiving manufacturing data associated with a manufacturing process for manufacturing a product. The manufacturing data may include data from a plurality of data sources associated with a plurality of stages of the manufacturing process, and the manufacturing data may include values for a plurality of parameters including at least one process parameter value and at least one quality parameter value. The method includes generating a time-sequenced data structure including the manufacturing data and transforming the time-sequenced data structure to a positionally-dimensioned data structure based on timing data associated with the plurality of stages. The method includes determining a new value for the at least one process parameter value based on the positionally-dimensioned data structure and at least one algorithm and optimizing the manufacturing process based on the new value.

A maintenance planning system for a production plant comprises: a production planning system for determining a production sequence for the production plant; an automation system for controlling production in the production plant; a state monitoring system for acquiring states of the production plant and its components; and a business planning system for the economic management of production and maintenance in the production plant. The maintenance planning system is designed for determining maintenance measures for the production plant. When determining the maintenance measures, the maintenance planning system takes into account the information of the production planning system, the automation system, the state monitoring system and the business planning system and performs optimization with regard to an economic utilization of the production plant. The disclosure further relates to a method for determining maintenance measures for a production plant and corresponding computer programs.