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.

Exemplary embodiments relate to methods, mediums, and systems for associating information, including critical-to-quality (CTQ) information such as minimum or maximum part dimensions, with parts in a three-dimensional model of a product. The information may be identified by performing a failure mode effect analysis (FMEA) against the model. The information is stored with the model data (e.g., in the form of an annotation applied to a model feature corresponding to the part in question). The model data may be consulted by product lifecycle management (PLM) applications during various phases of the product's lifecycle. Among other possibilities, the information may be used to automatically generate regulatory compliance documentation, to ensure product quality standards are met during a manufacturing process, or to perform postproduction quality monitoring of the product.

The present utility model discloses a production defect and abnormality report and track record retrieval system for the textile industry, which is made up of a textile machine, an information appliance, a server host, and a production track record retrieval platform in the server host can collect, store, integrate, and submit the data in real time, so that the production track record of finished textile products can be retrieved through the production track record retrieval platform, and through the production track record retrieval platform, the user can review the whole production history and defect analysis remotely. Therefore, the production defect and abnormality report and track record retrieval system for the textile industry can significantly enhance work efficiency, reduce defect rate, and enables quick retrieval and transparency of the production track record.

A method for performing quality control on a diagnostic analyzer includes receiving control measurement values from each of a plurality of diagnostic analyzers. A quality control measurement value is received from a target diagnostic analyzer. The quality control measurement value is compared with statistical criteria associated with the plurality of quality control measurement values received from the plurality of diagnostic analyzers. A comparison result is communicated to a user interface associated with the target diagnostic analyzer.

A production system for producing products from raw materials by a production process with several steps has a number of production facilities that perform the steps and a control device. The control device determines a control target value by referring to information about group combinations specified in accordance with the relative merits of the manufacturing condition routes followed by respective lots during the production process. The routes are respectively set for a number of groups that are classified on the basis of raw material properties formed of a combination of property items of one or more types of raw materials. The relative merits of the routes are determined on the basis of quality items of the lots, classified for inter-step combinations of groups, which are classified on the basis of manufacturing conditions at the steps.

The present disclosure provides a system and a method for controlling a semiconductor manufacturing apparatus. The system includes an inspection unit capturing at least one image of a wafer, a sensor interface 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 tuning subsystem. The front-end subsystem receives the at least one input signal from the database server and performs a front-end process to generate a data signal. The calculation subsystem performs an artificial intelligence analytical process to determine, according to the data signal, whether damage marks have been caused by the semiconductor manufacturing apparatus and to generate an output signal. The message and tuning subsystem generates an alert signal and a feedback signal according to the output signal and transmits the alert signal to a user.

Example implementation described herein are directed to systems and methods for management of a factory, which can include intaking and storing streaming sensor data from a plurality of edge nodes of the factory in a database server, the database server managing historical data of the plurality of edge nodes of the factory; executing, at an edge server, a first machine learning process on the streaming sensor data from the plurality of edge nodes to determine short term analytics; controlling, at the edge server, the plurality of edge nodes according to the determined short term analytics; executing, at a cloud server, a second machine learning process on the streaming sensor data stored in the database server and the short term analytics to determine long term analytics; and instructing the edge server to control the plurality of edge nodes according to the determined long term analytics.

A method controls a folding box machine having a plurality of machining stations and a plurality of control devices for controlling the quality of folding boxes. Each control device controls different quality features of each folding box, and each control device transmits the result of the control to a control unit. Target values are stored in the control unit in order to classify the control results into results which meet the quality standard and results which do not meet the quality standard, and a folding box is discharged depending on an evaluation. The method advantageously reduces waste.

A method for performing quality control on a diagnostic analyzer includes receiving control measurement values from each of a plurality of diagnostic analyzers. A quality control measurement value is received from a target diagnostic analyzer. The quality control measurement value is compared with statistical criteria associated with the plurality of quality control measurement values received from the plurality of diagnostic analyzers. A comparison result is communicated to a user interface associated with the target diagnostic analyzer.

A machining machine system includes: a machining machine; a numerical control device which generates a command for driving an axis of the machining machine; an interior information acquisition unit which acquires interior information of the numerical control device; a tentative determination unit which determines acceptance/rejection of a workpiece as machined by the machining machine based on a comparison result between the interior information and a threshold value; a final determination unit which determines acceptance/rejection of workpieces which are targets of an accuracy inspection which include at least a workpiece as determined to be accepted/rejected by the tentative determination unit based on a measurement result with respect to an accuracy of the workpiece; and a threshold value update unit which updates the threshold value used for determination processing by the tentative determination unit based on determination results by the tentative determination unit and the final determination unit.