The present disclosure relates to the automatic generation of characterized image data. For this purpose, a visual representation of a component is computed on the basis of existent structure data for the component, wherein the surface properties of the visual representation of the component may be varied based on predefined characteristic properties. Because, in the computation of such visual representations, both the component itself and the underlying characteristic surface properties are known, this information may be used for characterizing the corresponding parts in the visual representation in order to achieve automatic characterization of the computed visual representation.

Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

A control method for a processing apparatus includes the steps of suspending a processing operation of a processing unit during the formation of a processed groove and then imaging the processed groove by using an imaging unit to obtain a detected image, inspecting the condition of the processed groove according to the detected image, inputting a selected one of plural parameters into an input area displayed on a touch panel from an operator in adjusting the parameters to optimize inspection conditions, moving an input cursor from the input area according to the input of the selected parameter, and executing the inspection of the processed groove by using the selected parameter input in the input area.

Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

An inspection management system having a plurality of processes and managing final inspection performed to inspect a completed product and one or more intermediate inspections performed to inspect an intermediate product manufactured in the processes earlier than a final process includes: an inspection content data acquisition unit that acquires inspection content data including an inspection standard for each inspection item of the product; an inspection content setting unit that sets inspection content based on the inspection content data acquired by the inspection content data acquisition unit; a simulation unit that simulates inspection in accordance with assumed inspection content; an inspection standard calculation unit that calculates an inspection standard more appropriate than a current inspection standard based on the simulation; and an output unit that outputs base information indicating that at least the inspection standard calculated by the inspection standard calculation unit is more appropriate than the current inspection standard.

An inspection management system that manages inspection of an inspection apparatus in a production line including a manufacturing apparatus of a product and the inspection apparatus includes: an acquisition unit configured to acquire inspection content data including an inspection standard for each component; an inspection content setting unit configured to set inspection content; a setting-related information acquisition unit configured to acquire setting-related information including at least a time at which new inspection content is set when the inspection content setting unit sets the new inspection content; a storage unit configured to retain the setting-related information in association with a history of the setting of the inspection content; a setting-related information reading unit configured to read the setting-related information retained in the storage unit; and an output unit configured to be able to output the setting-related information.

A control system configured to use a database and a data storage area includes a control device for controlling a process executed on an object; and an image processing device arranged in association with the control device for taking an image of the object and processing image data acquired from taking the image of the object. The control device and the image processing device may work independently or collaboratively to send the database at least one of an attribute value or results information, and designation information in association with each other for the same object, the attribute value managed by the control device and corresponding to any attribute defined in the database, the results information representing a process result from the image processing device, and the designation information specifying a storage destination in the data storage area for the image data acquired from taking an image of the object.

A method for controlling quality of screwing or drilling operations performed by means of a tool. The method includes machine learning of a statistical model of the unsupervised type.

Provided is a computer-implemented method for determining at least one quality attribute for at least one defect of interest, including the steps: a. providing an input data set including the at least one defect of interest; b. determining the at least one quality attribute for the at least one defect of interest using a classification algorithm based on the input data set; and c. providing the determined at least one quality attribute and/or additional output information as output. Further, a computing unit and a computer program product are provided.