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 computer-implemented method of automatically generating inspection templates of a plurality of known good fasteners to identify fasteners at an inspection station is provided. The method includes providing a data entry mechanism to provide content needed to identify a plurality of unidentified mixed fasteners. The method also includes storing the content in a database, extracting the content from the database and creating the inspection templates from the extracted content. Each of the templates including a fastener profile and a set of features. Each of the features includes a range of acceptable values. Each of the templates has a fastener identification code associated therewith.

In production management systems, management devices includes a management device of a pre-process which adds a hash value of a production condition information received from a preceding process and a hash value calculated from the production condition information of the pre-process to calculate a first hash value and transmit the production condition information of the pre-process and the first hash value to a management device of a current process, and the management devices includes a management device of a current process which calculates a second hash value from production condition information received, calculates a third hash value obtained by subtracting the second hash value from the first hash value.

The technical decision relates to control of technological processes and can be used for monitoring and optimizing the production of glass products.

Technical results of the claimed invention consist in ensuring the continuous and uninterrupted collection of objective data from all technological sections of the production line and optimization of the whole production process on the basis of their analysis.

Technical results are achieved in that

Technical results are achieved due to the method for placing control units and communication units on a production line, which makes it possible to collect data about parameters and to transmit for their control unit with the possibility to receive the command data back.

A method for manufacturing a plurality of components, each of the plurality of components including at least one electrical feedthrough, in which a functional element is fastened in a feedthrough opening in a base body by way of an electrically insulating material, an information being acquired in association with each of the plurality of components, the method comprising the steps of: providing, in one of a plurality of manufacturing steps of the method, each of the plurality of components or one of a plurality of pre-stages of each of the plurality of components with an information store, at least one of (i) the information being stored in the information store, and (ii) an identifier is stored in the information store and the information is stored in a database in association with the identifier.

An information processing apparatus includes a detection result acquisition unit to acquire, for each of processes performed on a plurality of workpieces, a detection result of a physical quantity that changes according to processing applied to a workpiece; an identification information acquisition unit to acquire process identification information identifying one of the processes performed on the same workpiece and workpiece identification information identifying one of the workpieces subjected to the same process; and a display control unit to display, on a display, the process identification information and the workpiece identification information; and display, on the display, abnormality likelihood information indicating likelihood of abnormality, determined based on the detection result, of the particular process or the particular workpiece, in association with at least one of the process identification information and the workpiece identification information.

The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies in the second production lot using the predictive distribution of the product characteristics data and the process quality inspection data from the second production lot; if no anomalies are detected in the second production lot, updating the Gaussian process regression model using the process quality inspection data from the second production lot; setting target values for one or more values in the process quality inspection data based on the predictive distribution of the product characteristic; and adjusting settings of one or more manufacturing steps based on the target values.

A production equipment monitoring system 20 including: an anomaly index determination unit 23 determining an anomaly index a of a production equipment 10, based on a feature quantity obtained from an equipment information on the production equipment 10; a relevance determination unit 24 determining a degree of relevance D between each of anomalous conditions predicable to occur in the production equipment 10 and an observed condition of the production equipment 10; a detection threshold determination unit 25 determining a single detection threshold th for detecting a degree of anomaly A of the production equipment 10, based on anomaly thresholds at which are thresholds of the anomaly index a corresponding respectively to the anomalous conditions, and on the degree of relevance D; and an anomaly degree detection unit 26 detecting the degree of anomaly A of the production equipment 10, based on the anomaly index a and the detection threshold th.

The invention relates to a device (10) for at least one industrial automated process (110), in particular a weighing and/or packing and/or arranging and/or packing process, comprising: at least one sensor (30) for determining at least one process data value which is relevant to the entire process by detecting at least one partial process parameter of the device (10), which parameter is relevant to the entire process, a processing device (40), which is electrically connected to the sensor (30) for processing the process data value, an interface device (50) for data connection and for forwarding the process data value to at least one individual machine (2a, 2b, 2c) located upstream and/or downstream in the process (110), wherein the total process (110) can be evaluated with reference to the process data value by comparison with a pre-defined process instruction (130).

An information processing device includes: a memory; and a processor coupled to the memory and configured to: learn a classification rule that classifies an abnormal degree of a production facility from a text feature amount based on the text feature amount obtained from a number of texts included in a plurality of pieces of log data obtained in a predetermined process of the production facility and production history information of the production facility; extract a text feature amount of log data to be monitored obtained in the predetermined process of the production facility; and determine an abnormal degree of the production facility when the log data to be monitored is obtained based on the text feature amount and the classification rule.