In order to provide a method for analysing quality deficiencies of workpieces, preferably vehicle bodies and/or vehicle attachment parts, in particular after and/or whilst passing through a production process in industrial-method plants, preferably after and/or whilst passing through a painting process in painting plants, by means of which method quality deficiencies can be avoided and/or by means of which method quality deficiency causes in the production process can be determined, avoided and/or remedied, it is proposed in accordance with the invention that the method comprises the following steps: creating a workpiece-specific data set, uniquely assigned to a workpiece, at the start of a production process, in particular at the start of a painting process and/or creating a workpiece-carrier-specific data set, uniquely assigned to a workpiece carrier, at the start of a production process, in particular at the start of a painting process; supplementing the workpiece-specific data set while a workpiece is passing through the production process, in particular the painting process, with in particular quality-relevant process data and/or supplementing the workpiece-carrier-specific data set while a workpiece carrier is passing through the production process, in particular the painting process, with in particular quality-relevant process data; storing the workpiece-specific data set in a database and/or storing the workpiece-carrier-specific data set in a database.

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.

An abnormality score calculation apparatus according to an embodiment includes a processing circuit configured to: acquire first data concerning a status of a product or a manufacturing process; calculate based on the first data an abnormality score for a respective one of a plurality of abnormality modes or for a respective one of a plurality of pieces of the first data of various types; and convert a scale of a respective one of a plurality of abnormality scores including the abnormality score in such a manner that the abnormality scores become substantially equal in occurrence degree.

A manufacturing condition output apparatus of an embodiment is a manufacturing condition output apparatus which outputs a manufacturing condition of a product. The manufacturing condition output apparatus outputs change degree information which is information regarding degrees of change of values regarding defect probabilities for a plurality of variables relating to manufacturing of the product from model information of a model generated through machine learning on a basis of manufacturing data of the product and inspection result data of the product, as a manufacturing condition.

Wafer quality is determined by modeling equipment history as a sequence of events, then evaluating anomalous results for individual events. Identifying an event that generates bad wafers narrows the list of possible root causes.

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.

A method determines at least one parameter for a process quality during a processing process. The method includes: processing a workpiece while moving a processing tool and the workpiece relative to one another; monitoring a region on the workpiece; determining the at least one parameter for the process quality based on the monitored region; and determining at least one position-dependent parameter for the process quality based on a plurality of measured values of the at least one parameter at a same processing position, or determining at least one direction-dependent parameter for the process quality based on the plurality of measured values of the at least one parameter in a same processing direction.

The present invention includes: (a) a method for improving data to be processed for bottleneck detection, by cleaning corrupt or outlier data; (b) a method for improved analysis of bottleneck data using a plurality of rules for categorization; and (c) a method for improved display and/or allowing improved user feedback for bottleneck data using multivariate analysis and display. These methods can be used alone, or preferably be combined in whole or in part together to improve performance of an industrial process. A system is also provided.

A method and a system for sensing fine changes in processing/equipment measurement data are provided. A data change sensing method according to an embodiment of the present invention extracts a part on the basis of a statistical distribution of reference data and comparison data, calculates a target range on the basis of a specification, and discriminates data, included in the target range, among the extracted reference data and comparison data so as to determine data changes. Therefore, fine changes in measurement data for processing or equipment can be sensed in a manufacturing process, thereby enabling pre-estimation of potential quality variability of products and quick preemptive actions for preventing quality degradation.

A system for monitoring manufacturing includes one or more sensors and a controller in communication with the one or more sensors. The controller may include one or more processors that determine a quality metric represented by machine data collected from one or more machine data sensors and identify a correlation value between the machine data and environmental data collected from one or more environmental data sensors. The controller may further include determine if the correlation value exceeds a predetermined threshold value, and if the correlation value exceeds the predetermined threshold value, report at least one of the correlation value and the quality metric.