A method of virtually inspecting a quality of a product in a production environment includes receiving production information associated with the product, wherein the information is indicative of an operation performed on the product in relation to a first process of the production environment. The method further includes: determining a label for the product using a first classifier model based on the received information associated with the product; comparing the determined label against inspection data associated with an inspection of the product; and storing the production information and the inspection data associated with the product in an extension buffer, based on the comparison of the determined label and the inspection data, for retraining the first classifier model. The above method allows for storing new fault category information that is then dynamically used to retrain the model. Accordingly, this allows for retraining of classifier models without human intervention.

A data processing apparatus includes a processor. The processor generates visualization data for displaying estimation results of manufacturing conditions based on estimation results and relationship data. The relationship data includes first relationship data as a relationship between first manufacturing conditions recorded during an analysis, and second relationship data as a relationship between second manufacturing conditions corresponding. The processor divides the estimation results of the manufacturing conditions into a first group based on the first relationship data, and into a second group based on the second relationship data. The processor generates the visualization data based on a change in manufacturing condition relationship between the first group and the second group.

A method for determining if an individual integrated circuit was manufactured using an individual instance of tooling includes collecting from the individual integrated circuit first data representing at least one attribute that varies as a function of the tooling used to manufacture the individual integrated circuit and second data identifying the integrated circuit as having been manufactured using the individual instance of tooling. The first data is compared to a signature of the individual instance of tooling identified by the second data. The signature is derived from the at least one attribute measured from a population of integrated circuits that were manufactured using the individual instance of tooling. The individual integrated circuit is identified as having been manufactured using the individual instance of tooling identified in the second data collected from the individual integrated circuit if the first data correlates to the signature by a predetermined threshold.

A molding management system includes: a molded article item storage unit configured to store a plurality of pieces of molded article item information that are identification information of an item of a molded article; a defect classification storage unit configured to store master data including a plurality of pieces of defect classification information that are information related to classification of a defect of the molded article; a first receiving unit configured to receive selection of the molded article item information stored in the molded article item storage unit; a second receiving unit configured to receive selection of the defect classification information provided in the master data; an item defect storage unit configured to store the molded article item information received by the first receiving unit and the defect classification information received by the second receiving unit in association with each other; and a third receiving unit configured to receive, when the defect occurs in the molded article, selection of the defect classification information stored in the item defect storage unit in association with the molded article item information of an item of the molded article in which the defect occurs.

A system includes a microphone configured to generate audio signals associated with a weld tool being used by a user to perform a welding operation on an object. A processing device executes an artificial intelligence agent trained to perform several functions to determine certain information. These functions include processing the audio signals to identify sound signatures indicative of a weld defect occurring during the welding operation, determining the presence of the weld defect based on the identified sound signatures, and issuing cease commands to cease operation of the weld tool in response to the determined presence of the weld defect. The system may also include a welding robot, peripheral haptic devices, and network interfaces to support remote interaction and haptic feedback between users. The system aims to enhance welding quality control through real-time audio monitoring and immediate intervention capabilities.

A control system (1) according to the present invention comprises: a control device (100) that monitors the operation of a plurality of moving parts for machining a workpiece (155), and controls the operation of the plurality of moving parts in each control cycle by issuing command values to the plurality of moving parts; and an inspection device (200) for inspecting the workpiece (155). The control device (100) comprises: an identification unit (160) for identifying, based on inspection results of the inspection device (200) and the command values issued to the plurality of moving parts, which moving part from among the plurality of moving parts has caused an abnormality in the inspection results; and a storage unit (170) for collecting and storing data on the moving part that has been identified by the identification unit (160) and caused the abnormality in the inspection results.

A prediction model creation apparatus includes a feature amount acquisition unit that acquires values of types of feature amounts that are calculated from operating state data indicating an operating state of a production facility that produces a product, for both a normal time at which the production facility produces the product normally and a defective time at which a defect occurs in the product that is produced, a feature amount selection unit that selects a feature amount effective in predicting the defect from among the acquired types of feature amounts, based on a predetermined algorithm that specifies a degree of association between the defect and the types of feature amounts, from the values of the types of feature amounts acquired at the normal time and the defective time, and a prediction model construction unit that constructs a prediction model for predicting occurrence of the defect, using the selected feature amount.

A method, system, and computer program product, include obtaining a dataset related to a power grid collected by a plurality of sensors distributed in the power grid, identifying a region as a candidate fault region based on first data in the dataset, the first data being collected by a first sensor from among the plurality of sensors that is located in the region, and verifying the candidate fault region based on second data in the dataset, the second data being collected by a second sensor from among the plurality of sensors that is adjacent to the first sensor.

An apparatus and method for monitoring a quality of an object of a 3D-print job series of identical objects, each object built from a multitude of stacked 2D-layers printed by a 3D-printer in an additive manufacturing process, including: determining a layer quality indicator of a currently printed layer of an object, comparing the determined layer quality indicator of the currently printed layer with a predetermined lower confidence limit of the layer, the predetermined lower confidence limit being calculated depending on layer quality indicators of previously completely manufactured objects complying with predefined quality requirements, and generating a warning signal, if the layer quality indicator of the currently printed layer has a value equal or lower than the lower quality limit is provided.

Automatic process control of additive manufacturing. The system includes an additive manufacturing device for making an object and a local network computer controlling the device. At least one camera is provided with a view of a manufacturing volume of the device to generate network accessible images of the object. The computer is programmed to stop the manufacturing process when the object is defective based on the images of the object.