Systems and methods for making predictions relating to products manufactured via a manufacturing process. A processor receives a plurality of input vectors associated with a plurality of output values and a plurality of time intervals. The processor clusters the plurality of input vectors based on the time intervals associated with the input vectors. The processor trains a machine learning model for each time interval of the plurality of time intervals, where the training of the machine learning model is based on the input vectors associated with the time interval, and the output values associated with the input vectors. The processor further trains a classifier for selecting one of the plurality of time intervals for input data received for a product. In one embodiment, the machine learning model associated with the time interval selected by the classifier is invoked to predict an output based on the input data.

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.

A method for monitoring a process variation index includes operations of: obtaining a target parameter to be monitored and a reference parameter used to increase goodness of fit among structural parameters predicted by measuring a structure in a specific location of a wafer; obtaining a reference parameter set in a reference model; and calculating a process variation index capable of confirming a structural change of the structure according to a change in process conditions using the structural parameter and the reference parameter.

A problem is to specify a more proper manufacturing process for a product as a material. A configuration of the present invention for solving the above problem is a manufacturing process optimization system 1 which includes an input device 12 which receives a final product and information on its manufacturing process, a central control device 11 which in accordance with a product management unit 21 stored in a main storage device 14, separates each process block constituting the manufacturing process into functions that the process thereof is responsible for, and selects the sensitivity of each separated function along the manufacturing process to thereby calculate process conditions in all manufacturing process, and an output device 13 which outputs the process conditions.

Methods for providing feedback control of converters for converting glass tubes to glass articles include a model predictive control framework. The methods include operating the converter, providing target values for attributes of the glass articles or glass tubes, measuring the attributes for the glass articles and glass tubes, conditioning the measurement data to remove outlier data points and calculating statistics representative of the measured attributes, and determine updated settings for one or more process parameters from the previous settings, the statistical properties, and the target values, where the updated settings are those that minimize an objective control function for the converter. The methods further include adjusting the process parameters to the updated settings. The model predictive control framework enables feedback control of the converter that compensates for disturbances that act on the process.

Provided is a prediction apparatus including: a data acquisition unit configured to acquire setting value data indicating a setting value of a controlled object and physical quantity data indicating a physical quantity of a product obtained by controlling the controlled object; a prediction unit configured to calculate, using the setting value data and the physical quantity data, a plurality of prediction values obtained by predicting a plurality of physical quantities in the product on a basis of a setting value used for control of the controlled object; an evaluation unit configured to evaluate the plurality of prediction values on a basis of a predefined reference; and an output unit configured to output a setting value recommended according to a result of the evaluation.

A processor performs an experiment of machining a device to acquire first-type and second-type information each indicating conditions of the experiment of machining and third-type and fourth-type information each indicating a result of the experiment of machining (S401). The processor derives a first expression and a second expression, where the first expression receives first-type and second-type information as inputs and outputs third-type information as more than one solution, and the second expression receives first-type and second-type information as inputs and outputs fourth-type information. The processor derives more than one third expression from the first expression, where the more than one third expression each receives second-type and third-type information as inputs and outputs first-type information (S402). The processor receives second-type and third-type information each measured in machining as inputs and outputs fourth-type information indicating a result of machining using the second expression and the more than one third expression (S403).

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.

A manufacturing defect factor searching method includes: classifying manufacturing monitoring data into a set of non-defective products having an inspection result indicating a non-defective product and a set of defective products having the inspection result indicating a defective product, in accordance with a correspondence relationship between the manufacturing monitoring data and product inspection data indicating the inspection result of the product manufactured in the manufacturing line, the manufacturing monitoring data being collected from a manufacturing line of a product and being multivariate; estimating, for each item of the manufacturing monitoring data, a mixture distribution function approximating to a statistical distribution of each of the set of non-defective products and the set of defective products; resolving the mixture distribution function into components; and generating a list of items including a resolved component having a correlation with a manufacturing quality defect from among items of the manufacturing monitoring data.

In a quality control method applied in manufacturing, product information of a product is obtained. Manufacturing parameters corresponding to the product information are queried. The manufacturing parameters are input into a product quality prediction model which is trained to obtain the value of at least one quality inspection of each product. If such quality inspection value is not equal to a standard value or is not within a standard value range, an incorrect manufacturing parameter is identified from all the manufacturing parameters applicable to each product, the incorrect manufacturing parameter being output when identified.