An online prediction method of tool-electrode consumption adapted for an electrical discharge machining (EDM) apparatus includes an experimental design; extracting electrode consumption variables from machining parameters of the electrical discharge machining (EDM) apparatus; and obtaining a correlation between the machining parameters and the electrode consumption variables through a correlation analysis to obtain a prediction model capable of predicting an effective contact area of a tool-electrode and a workpiece. In addition, a prediction method of machining accuracy is provided.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing material processing. In one aspect, a method includes collecting, from a set of sensors, a set of current manufacturing conditions. Based on the set of current manufacturing conditions collected from the sensors, a set of current qualities of a material currently being processed by manufacturing equipment is determined. A baseline production measure for processing the material according to the set of current qualities is obtained. A candidate set of manufacturing conditions that provide an improved production measure relative to the baseline production measure is determined. A set of candidate qualities for the material produced under the candidate set of manufacturing conditions is determined. A visualization that presents both of the set of candidate qualities of the material and the set of current qualities of the material currently being processed is generated.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a process of manufacturing a product. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of input settings for manufacturing a product, based on a causal model that measures causal relationships between input settings and a measure of a quality of the product; ii) determining the measure of the quality of the product manufactured using the configuration of input settings; and iii) adjusting, based on the measure of the quality of the product manufactured using the configuration of input settings, the causal model.

A method of operating a production for producing a plurality of products is provided. The method includes inspecting the products according to a first inspection rate. The inspection rate determines the number of products that are inspected during a period of time and/or from a given set of products. An inspection of one of the products includes testing (e.g., in a first number of testing steps) at at least one inspection station. The method includes obtaining test data based on the inspection of at least one of the products, and setting a threshold for a number of products not fulfilling the testing (e.g., during a specified period of time). The method also includes determining a second inspection rate based on the threshold set and the test data obtained, and inspecting the products according to the second inspection rate. The second inspection rate may be lower than the first inspection rate.

A substrate processing condition setting method includes acquiring, causing, and setting. In the acquiring, a plurality of estimation processing results are acquired by inputting a plurality of processing conditions to a trained model that is subjected to machine training based on a training processing condition and a processing result obtained by processing a substrate under the training processing condition. In the causing, a display section is caused to display an image based on the estimation processing results. In the setting, one processing condition corresponding to one estimation processing result of the estimation processing results is set, as an actual processing condition in substrate processing, based on the image displayed on the display section.

This calculation device for predicting the surface roughness of a processed product from a physical quantity includes: a measurement data acquisition unit which acquires measurement data of surface roughness measured by a surface roughness measuring device; a physical quantity acquisition unit which acquires a physical quantity indicating a factor that causes surface roughness; a first amplitude spectrum conversion unit which converts the measurement data into a first amplitude spectrum; a second amplitude spectrum conversion unit which converts the physical quantity into a second amplitude spectrum; and a coefficient calculation unit which calculates a coefficient on the basis of a specific frequency, the second amplitude spectrum, and the first amplitude spectrum.

The present disclosure describes a method, apparatus, and computer readable medium for providing predictive and preventive analysis relating to fitness of elements in an assembly line, prior to final product assembling. According to the present disclosure, when the elements in assembly lines pass the testing performed by the element testing units, the test data of the elements is processed by the system to predict whether the elements when assembled, results in a fit product. When the final product is predicted to be unfit, the system detects non-reliable element(s) and provide explanation/feedback for the failure of the final product.

A virtual metrology method using a convolutional neural network (CNN) is provided. In this method, a dynamic time warping (DTW) algorithm is used to delete unsimilar sets of process data, and adjust the sets of process data to be of the same length, thereby enabling the CNN to be used for virtual metrology. A virtual metrology model of the embodiments of the present invention includes several CNN models and a conjecture model, in which plural inputs of the CNN model are sets of time sequence data of respective parameters, and plural outputs of the CNN models are inputs to the conjecture model.

A method for checking the quality of a workpiece, a surface section of the workpiece is finished with a manufacturing device. A reference signal representing a time dependent difference between an ideal tool position and a real tool position of a tool of the manufacturing device in a reference phase is determined when finishing the workpiece. A test signal representing a time dependent difference between an ideal tool position and a real tool position of a tool of the manufacturing device in an operation phase is determined when finishing the workpiece. A mean value and a standard deviation value from the reference signal is determined. Data points of the test signal are determined, where the test signal deviates from the mean value more than a defined multiple of the standard deviation value. The surface quality of the workpiece is estimated by using the determined data points.

The prediction system of strip chew collects and stores first data and second data as adaptive model construction data. The first data indicates the occurrence or non-occurrence of the strip chew in an object rolling path and the occurrence point of the strip chew. The second data includes information on a preceding rolling path and attribute information on an object strip. The system constructs an adaptive model using the stored adaptive model construction data, and stores the constructed adaptive model as an adapted model. The system collects prediction data similar to the second data. Then, the system inputs the prediction data to the adapted model to predict the occurrence or non-occurrence of the strip chew in the object rolling path and all or some of the occurrence points of the strip chew before the prediction object strip reaches the object rolling path.