A quality determination device includes an acquisition unit for acquiring a drive current during tapping, from a motor provided in a machine tool that performs tapping, and a determination unit for determining acceptance or rejection of a screw hole formed by the tapping, based on the drive current, an electric power of the motor obtained from the drive current, or a torque of the motor obtained from the drive current.

Disclosed are a product inspection method and device, producing system and a computer storage medium. The method comprises: conducting image acquisition on a product assembly line to obtain a production line image; extracting a product image including a product to be inspected from the production line image; extracting an inspection point image in a part inspection area in the product image; inputting the inspection point image into an inspection model to obtain an inspection result; and determining that the product to be inspected in the product image has defects under the condition that the inspection result meets any of the following conditions.

The present disclosure provides systems and methods for controlling a semiconductor manufacturing equipment. The control system includes an inspection unit capturing a set of images of the semiconductor manufacturing equipment, a sensor interface receiving the set of images and generating at least one input signal for a database server, and a control unit. The control unit includes a front end subsystem, a calculation subsystem, and a message and feedback subsystem. The calculation subsystem receives the data signal from the front end subsystem, wherein the calculation subsystem performs an artificial intelligence analytical process to determine, according to the data signal, whether a malfunction has occurred in the semiconductor manufacturing equipment and to generate an output signal. The message and feedback subsystem generates an alert signal and a feedback signal according to the output signal, and the alert signal is transmitted to a user of the semiconductor manufacturing equipment.

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 is described for measuring workpieces, each having structural features that form test features for measurement. The method determines an unstable one and a stable one of the test features, based on expected violation or satisfaction, respectively, of a statistical control rule. The method measures workpieces such that the unstable test feature is measured more frequently than the stable test feature. The method ascertains whether the unstable test feature remains unstable and whether the stable test feature remains stable. The method measures additional workpieces if the unstable test feature remained unstable and the stable test feature remaining stable. The determining is repeated if the unstable test feature is no longer unstable, the stable test feature is no longer stable, or any other measurement feature changes, such as if a new batch of workpieces is to be measured, environmental conditions change, or measurement has proceeded longer than a predefined threshold.

A quality determination device includes an acquisition unit for acquiring a drive current during tapping, from a motor provided in a machine tool that performs tapping, and a determination unit for determining acceptance or rejection of a screw hole formed by the tapping, based on the drive current, an electric power of the motor obtained from the drive current, or a torque of the motor obtained from the drive current.

A facility diagnosis device includes: a process data acquirer that acquires process data indicating a state of a process executed in a plant; a production data acquirer that acquires production data indicating a production state in the plant; a state information generator that generates state information for estimating an operation state of a facility operating in the process based on the acquired process data or the acquired production data, or the process data and the production data; and a determiner that determines the operation state of the facility based on the acquired process data or the generated state information and the acquired production data.

Example systems may relate to component failure prediction. A non-transitory computer readable medium may contain instructions to analyze a plurality of features corresponding to a component of a system. The non-transitory computer readable medium may further contain instructions to determine which of the plurality of features to use to model a failure of the component. The non-transitory computer readable medium may contain instructions to generate a plurality of models to model the failure of the component and assemble the plurality of models into a single model for predicting component failure. The non-transitory computer readable medium may further contain instructions to extract data associated with a component failure predicted by the single model and correlate the data associated with the predicted component failure with the single model.

There is provided a system and method of analysing anomalies in one or more electronic appliances including at least one computer. The method comprises, by a processor and memory circuitry, upon detection of a deviation of a given parameter representative of the one or more electronic appliances from an operational state, providing a model associated with the given parameter, wherein the model links one or more other parameters to the given parameter, wherein the one or more other parameters affect the given parameter, and based at least on the model, identifying, among the one or more other parameters, at least one parameter P.sub.j for which a change in its value allows bringing back the given parameter to the operational state.

A method includes receiving, from one or more sensors associated with manufacturing equipment, current trace data associated with producing, by the manufacturing equipment, a plurality of products. The method further includes performing signal processing to break down the current trace data into a plurality of sets of current component data mapped to corresponding component identifiers. The method further includes providing the plurality of sets of current component data and the corresponding component identifiers as input to a trained machine learning model. The method further includes obtaining, from the trained machine learning model, one or more outputs indicative of predictive data and causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.