A method of the present disclosure includes (a) retrieving from a memory a plurality of measured values of the factor variable, and a label indicating good or bad of the quality corresponding to each of the plurality of measured values, (b) dividing a factor variable space defined by the factor variable into a plurality of grids by equally dividing a range determined by a maximum value and a minimum value of the plurality of measured values for each factor variable, (c) setting a plurality of candidate areas each of which includes one grid or a plurality of adjacent grids, and deriving, for each of the plurality of candidate areas, a good density based on the label associated with the measured value that is within the candidate area, and (d) selecting one of the plurality of candidate areas as the factor variable area, based on the good density.

A method of detecting and classifying anomalies during semiconductor processing includes executing a wafer recipe a semiconductor processing system to process a semiconductor wafer; monitoring sensor outputs from a sensors that monitor conditions associated with the semiconductor processing system; providing the sensor outputs to models trained to identify when the conditions associated with the semiconductor processing system indicate a fault in the semiconductor wafer; receiving an indication of a fault from at least one of the models; and generating a fault output in response to receiving the indication of the fault.

A method for checking at least one subregion of a component, in particular a component of a turbomachine, including at least the steps of a) providing a blank; b) producing at least the subregion from the blank by machining the blank using at least one tool and using at least one force sensor-to record at least one force curve of at least one force acting during machining on the at least one tool; c) checking whether there is at least one deviation-of the at least one force curve from at least one predetermined target curve-of the at least one force curve, the at least one deviation-characterizing at least one material defect-contained in an unmachined segment of the subregion. A checking device for checking at least a subregion of a component is also provided.

Example implementations involve fault detection and isolation in industrial networks through defining a component as a combination of measurements and parameters and define an industrial network as a set of components connected with different degrees of connections (weights). Faults in industrial network are defined as unpermitted changes in component parameters. Further, the fault detection and isolation in industrial networks are formulated as a node classification problem in graph theory. Example implementations detect and isolate faults in industrial networks through 1) uploading/learning network structure, 2) detecting component communities in the network, 3) extracting features for each community, 4) using the extracted features for each community to detect and isolate faults, 5) at each time step, based on the faulty components provide maintenance recommendation for the network.

A bead appearance inspection device includes an input unit configured to enter input data related to a welding bead of a workpiece produced by welding, a first determination unit configured to perform a first inspection determination related to a shape of the welding bead based on a comparison between the input data and a master data, k second determination units, where k is an integer of 1 or more, that are equipped with k types of artificial intelligence and that are configured to perform a second inspection determination related to a welding defect of the welding bead based on processings of the k types of artificial intelligence targeting the input data, and a comprehensive determination unit configured to output a result of an appearance inspection of the welding bead to an output device based on determination results of the first determination unit and the k second determination units.

A prediction system configured to predict a defect of a target product includes a first pre-trained model trained based on a defect characteristic value indicating a defect associated with a location in an existing product, a feature of a three-dimensional shape of the existing product, and conditional information indicating a manufacturing condition of the existing product. The first pre-trained model is configured to, when a feature of a three-dimensional shape of the target product is input, output a defect characteristic value indicating a defect associated with a location in the target product.

The present invention can detect early an abnormality or signs of abnormality in a workpiece. A sensor system 1 is provided with: a first sensor 30a that measures a workpiece; a second sensor 30b that measures the workpiece in a relatively longer cycle than the first sensor 30a; and a master unit 10. The master unit 10 includes: an acquisition unit 11 that acquires data measured by the first sensor 30a and data measured by the second sensor 30b; and a generation unit 12 that generates learning data which is used for machine learning of a learning model and in which the acquired data of the first sensor 30a is regarded as input data and the acquired data of the second sensor 30b is regarded as label data indicating a property of the input data.

A distributed light-guided processing method includes obtaining an order from a requester, for at least one completed product. Raw components are provided to at least one selected remote processing location. The selected remote processing location includes a light guided system. Work instructions are provided to the selected remote processing location, wherein the work instructions enable the light guided system to guide construction of the completed product. The completed product is processed, using at least the raw components, the work instructions, and the light guided system. The completed product is shipped from the selected remote processing location upon completion of the processing.

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 and device for predicting a defect. The method includes determining a sequence between a plurality of sub-models by modeling a production process into the plurality of sub-models, mapping production process data into each of the plurality of sub-models, determining, by a corresponding sub-model, output data comprising defect information on a potential defect occurring in a corresponding step, for each of the plurality of sub-models, predicting information associated with a defect in the production process based on the output data corresponding to each of the plurality of sub-models, and inputting the output data of each of the sub-models to a subsequent sub-model of the corresponding sub-model, based on the sequence.