In one embodiment, a computing device includes interface circuitry and processing circuitry. The processing circuitry receives, via the interface circuitry, a data stream captured at least partially by sensor(s), which contains feature values corresponding to an unlabeled instance of a feature set. The processing circuitry then groups the data stream into a data stream group, which is assigned from a set of data stream groups based on the feature values in the data stream. The processing circuitry then selects a predictive model for the data stream group from a set of predictive models, which are each trained to predict a target variable for a corresponding data stream group. The processing circuitry then predicts the target variable for the data stream using the predictive model, which infers the target variable based on the set of feature values in the data stream.

A material characteristic value prediction system that can predict material characteristic values with high accuracy is provided. Also provided is a method of manufacturing a metal sheet that can improve the product yield rate, by changing manufacturing conditions of subsequent processes. The material characteristic value prediction system (100) includes a material characteristic value predictor configured to acquire input data including line output factors in a metal sheet manufacturing line, disturbance factors, and component values of a metal sheet being manufactured, and predict material characteristic values of the manufactured metal sheet using a prediction model configured to take the input data as inputs, wherein the prediction model includes a machine learning model generated by machine learning and configured to take the input data as inputs and output production condition factors, and a metallurgical model configured to take the production condition factors as inputs and output the material characteristic values.

Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of faults, determine a priority level for each fault by applying the extracted feature data to a predictive model, determine at least one high priority fault, and generate at least one operator alert based on the at least one high priority fault.

Aspects of the disclosed technology encompass the use of a deep learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process, predicting an expected value for an intermediate or final output of an article of manufacture, based on the control values, and determining if the predicted expected value for the article of manufacture is in-specification. In some aspects, the process can further include steps for generating control inputs if the predicted expected value for the article of manufacture is not in-specification. Systems and computer-readable media are also provided.

A computer-performed process estimation method and a process estimation method using the computer are provided for estimating, based on a first process data including a process information of a predetermined target step performed in a first manufacturing device that manufactures a material through at least one step including the target step, a second process data including a process information of the target step performed in a second manufacturing device that is a different device from the first manufacturing device and manufactures the material through at least one step including the target step. This method includes machine-learning a relationship between the first process data and a first structure data obtained from a sample after the target step in the first manufacturing device, and creating a first regression model representing a correlation between the first process data and the first structure data, machine-learning a relationship between the second process data and a second structure data obtained from a sample after the target step in the second manufacturing device, and creating a second regression model representing a correlation between the second process data and the second structure data, creating a third regression model representing a correlation between the first process data and the second process data based on the first regression model and the second regression model, and by using the third regression model, estimating an estimated second process data that includes the second process data corresponding to an estimation source-first process data including the first process data that is an arbitrary estimation source.

An industrial monitoring system comprises a monitoring device attached to an industrial device by a bond. Sensor data collected by the monitoring device during a commissioning period is received and used to train a machine learning model. Subsequent to the commissioning period, additional sensor data is collected by the monitoring device. An abnormal state of the bond between the monitoring device and industrial device is determined based on the additional sensor data and a characteristic inferred by the trained machine learning model. A notification of the abnormal state is generated.

One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

Aspects of the disclosed technology encompass the use of a deep-learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.

Embodiments of the present disclosure provide a method for predicting an occurrence of a tool processing event, thereby determining whether to activate a virtual metrology. In a model-building stage, plural sets of model-building data are used to create at least one classification model in accordance with at least one classification algorithm, in which each classification model includes plural decision trees. Then, probabilities of the decision trees are used to create at least one reliance index model, and the sets of model-building data are used to create at least one similarity index model in accordance with a statistical distance algorithm. In a conjecture stage, a set of processing data of a workpiece is inputted into each classification model, each reliance index model and each similarity index model to determine whether to activate (start) virtual metrology.

A machine tool machining dimensions prediction device (100) includes: a data collector (10) to acquire driving state information of a machine tool; a feature amount extractor (211) to extract a feature amount from the driving state information; a data analyzer (311) to analyze the extracted feature amount; and a machining quality prediction model generator (312) to generate, from the analyzed information, a prediction model of a machining dimension of a workpiece. The machine tool machining dimensions prediction device (100) applies the feature amount and the driving state information to the prediction model during machining of the workpiece to predict a machining quality and refers to a machining dimension quality regulation to determine whether the machining quality satisfies a standard.