A method evaluates a sample of measurement data from measuring multiple workpieces by at least one coordinate measuring machine. A system of statistical distributions describes a frequency of measurement data values. The distributions are distinguishable based on skewness and kurtosis. The method includes defining a set of statistical distributions that are able to describe a frequency of measurement data values in the entire value interval from the system of statistical distributions for a value interval of the measurement data, which is a specified value interval or a value interval of the measurement data actually arising in the sample. The method includes ascertaining the skewness and the kurtosis from the sample of measurement data corresponding to a first statistical distribution. The method includes checking, using the ascertained moment values, whether the defined set contains a statistical distribution that has the ascertained skewness and kurtosis, and producing a corresponding test result.

A device and method for analyzing a sequential process, the sequential process including at least one repeating subprocess, and the method comprising the following steps: Recording process data of the sequential process over a reference time period; Automatically determining phase limits, based on the recorded process data; Identifying at least one repeating subprocess, the duration of which is limited in time by two adjacent phase limits; Determining at least one reference variable for each identified repeating subprocess from the process data recorded in the time period; Recording process data of the sequential process over a time period following the reference time period, and repeating steps b. and c. for the purpose of detecting the recurrence of an identified subprocess; Comparing the recorded process data of the detected subprocess with the at least one reference variable of the corresponding identified subprocess to establish deviations from a normal operation.

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.

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.

Disclosed is a method for monitoring a production process using limit values for a technical parameter of units produced by the production process or for a technical parameter of the production process. The values of the technical parameter of units produced by a production process or of a technical parameter of a production process are distributed according to an underlying distribution. The distribution is characterized by an asymmetric probability density function (700) or by an asymmetric cumulative distribution function (CDF). The method comprises: comparing at least one value of the technical parameter with an interval that is asymmetric with regard to a characteristic value of the distribution, and monitoring the production process based on the result of the comparison.

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 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 control module is adapted to control technical equipment by processing batch-run data from the technical equipment. The control module operates according to parameters that are obtained by a parameter module. The module receives a reference plurality of multi-variate reference time series with data values from sources that are related to the equipment. There are time series with measurement values and time series with data that describes particular manufacturing operations during a batch-run time interval. The module splits the time interval into phases by determining transitions between the particular manufacturing operations, and divides the time series into particular phase-specific partial series. For each phase separately, and for the phase-specific partial series in combination, the module differentiates phase-specific time series into relevant partial time series or non-relevant partial time series and set the parameters accordingly.

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.

Disclosed is a method for automatically determining quality of a self-piercing riveting process, including the following operations: inputting standard values, acquiring data in real-time, and comparing data and determining quality of riveting. Riveting parameters and process curves are obtained in real time by a data acquisition system, the measured values for determining quality of riveting is calculated according to the real-time change of the riveting force curve and information of the riveted plates, the quality of the riveting process can be automatically determined by comparing the measured values and the standard values, the efficiency of monitoring quality is improved, inspection of all riveting points can be realized, abandonment of white vehicle bodies due to poor riveting quality is greatly reduced, and the problem that a large number of white vehicle bodies with defective quality cannot be found is avoided, and the riveting quality of the white vehicle bodies is guaranteed.