A method of planning the manufacture of component carriers includes defining a set of final product parameters as a target for component carriers to be manufactured, ranking the process parameters concerning their impact on the final product parameters, selecting a subset of higher ranked process parameters, inputting the selected subset of process parameters for processing by an artificial intelligence module, and determining an action plan for the manufacturing based on an output of the artificial intelligence module, where the product parameters are influenceable by a set of process parameters settable during the manufacturing method.

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 system includes at least one processor and a memory. The memory stores a dimensionally aware model generated based on a training set and guided by feature dimensions and instructions for execution by the at least one processor. The instructions include, in response to receiving a set of data from a user device, identifying a set of features from the set of data and applying the dimensionally aware model to the set of features by implementing a boundary representation. The instructions include classifying the set of features as acceptable in response to the implementation of the boundary representation indicating the set of features are outside the boundary representation, classifying the set of features as unacceptable in response to the implementation of the boundary representation indicating the set of features are inside the boundary representation, and generating, for display on the user device, an alert based on the classification.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing material processing. In one aspect, a method includes collecting, from a set of sensors, a set of current manufacturing conditions. Based on the set of current manufacturing conditions collected from the sensors, a set of current qualities of a material currently being processed by manufacturing equipment is determined. A baseline production measure for processing the material according to the set of current qualities is obtained. A candidate set of manufacturing conditions that provide an improved production measure relative to the baseline production measure is determined. A set of candidate qualities for the material produced under the candidate set of manufacturing conditions is determined. A visualization that presents both of the set of candidate qualities of the material and the set of current qualities of the material currently being processed is generated.

Machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of manufacturing processes are described.

A method for measuring components produced by a production device includes selecting components to be measured from multiple components. The selection is made according to at least one selection parameter. The at least one selection parameter includes a sampling frequency. The method includes determining at least one production parameter. The at least one production parameter includes a production condition. The method includes adapting the sampling frequency based on the production parameter or a change in the production parameter. Adapting includes reducing the sampling frequency in response to one or more production parameters not changing by more than a predetermined amount.

Disclosed is a method for treating containers, wherein the containers are transported along a predetermined transport path by a transport device and are treated in a predetermined manner by a first treatment device, wherein predetermined working parameters are used for the treatment of the container, wherein individual containers being inspected after their treatment and at least one variable characteristic of a quality of this container being determined, wherein that at least one identification information is generated by an inspected container and/or an inspection operation can be uniquely identified.

Provided is a computer-implemented method for determining at least one quality attribute for at least one defect of interest, including the steps: a. providing an input data set including the at least one defect of interest; b. determining the at least one quality attribute for the at least one defect of interest using a classification algorithm based on the input data set; and c. providing the determined at least one quality attribute and/or additional output information as output. Further, a computing unit and a computer program product are provided.

Production plant (1) for producing at least one end product (3) from at least one primary starting material (2), comprising at least one processing station (41-43) which processes at least one starting material (21-23) to form at least one product (31, 32, 33), and a process controller (51-53) which can control at least one variable (71-73), which is a measure of a quality feature of the product (31-33) and/or is correlated with a quality feature of the product (31-33), by influencing at least one manipulated variable (61-63) acting on the processing station (41-43), wherein the process controller (51-53) is additionally designed to control the production rate (31a-33a) of the processing station (41-43) for the product (31-33) and/or the consumption rate (21a-23a) of the processing station (41-43) with regard to starting material (21-23) by acting on the manipulated variable (61-63).

A machine learning module is provided trained to generate from a design data record specifying a design variant, a predictive performance distribution and a constraint compliance distribution of the design variant. A predictive performance distribution and a constraint compliance distribution are generated by the machine learning module. The predictive performance distribution is compared with performance values of previously evaluated design data records. A simulation of the corresponding design variant is either run or skipped. A design evaluation record is output which includes a performance value and constraint compliance data each derived from the simulation if the simulation is run or, otherwise, each derived from the predictive performance distribution and the constraint compliance distribution. Depending on the design evaluation records, a performance-optimizing and constraint-compliant design data record is selected from the variety of design data records. The selected design data record is then output for controlling the production system.