A process control system according to one embodiment of the present invention comprises: a first system for generating thickness information about an internal defect layer included in a carbon steel product; and a second system which receives the thickness information about the internal defect layer from the first system through a network, and which controls an etching process for removing at least a part of the internal defect layer from the carbon steel product by using the thickness information about the internal defect layer, wherein the first system provides the second system with a calculation module necessary for the second system to control the etching process, and the second system provides the first system with the information necessary for the first system to update the calculation module.

An additive manufacturing system comprises an apparatus arranged to distribute layer of metallic powder across a build plane and a power source arranged to emit a beam of energy at the build plane and fuse the metallic powder into a portion of a part. The system includes a processor configured to steer the beam of energy across the build plane and receive data generated by one or more sensors that detect electromagnetic energy emitted from the build plane when the beam of energy fuses the metallic powder. The received data is converted into one or more parameters that indicate one or more conditions at the build plane while the beam of energy fuses the metallic powder. The one or more parameters are used as input into a machine learning algorithm to detect one or more defects in the fused metallic powder.

The present disclosure relates to a surface inspection method using a mold surface inspection device, and more specifically, to a surface inspection method using a mold surface inspection device including a setting part in which an inspection object is set, a light source part configured to irradiate the inspection object with irradiated light so that a reflective highlight is generated on a surface of the inspection object, an imaging part configured to image the surface of the inspection object so that a highlight region where a reflective highlight is generated is included, and an image processing part configured to process an image imaged in the imaging part to provide the image to a worker so that the worker determines whether defects are generated on the surface of the inspection object on the basis of the image.

An additive manufacturing system comprises an apparatus arranged to distribute layer of metallic powder across a build plane and a power source arranged to emit a beam of energy at the build plane and fuse the metallic powder into a portion of a part. The system includes a processor configured to steer the beam of energy across the build plane and receive data generated by one or more sensors that detect electromagnetic energy emitted from the build plane when the beam of energy fuses the metallic powder. The received data is converted into one or more parameters that indicate one or more conditions at the build plane while the beam of energy fuses the metallic powder. The one or more parameters are used as input into a machine learning algorithm to detect one or more defects in the fused metallic powder.

A method for monitoring a manufacturing of a metal product, the metal product being manufactured according to a manufacturing process, is implemented by an electronic monitoring device. This method includes acquiring (100) a measured value of at least one representative parameter, each representative parameter being a parameter relating to the metal product or a parameter relating to the manufacturing process, determining (130) a status of the metal product among a compliant status and an analysis status, depending on the at least one acquired value and on at least one target, and when the determined status is the analysis status, computing (150) a corrective action to be applied to the product, among a set of corrective actions and depending on the at least one acquired value, the set of corrective actions including a product repair, a product downgrading, a product expertise and a product acceptance.

A method of inspection and repair of a robotic operation is provided. The robotic operation preferably includes dispensing material onto a surface of a component. A camera is used to capture an image of the robotic operation and identify defects in the robotic operation in the captured image. The locations of the defects are then transformed from the image coordinate system to the robot coordinate system. The robot may then be moved to the defect locations in the robot coordinate system to repair the defects, e.g., by dispensing additional material at the defect.

Embodiments of present disclosure relate to adjusting a robot motion path. In the method for adjusting a robot motion path, a first processing procedure may be performed on a first workpiece to obtain a first product. Then, first process data may be obtained, where the first process data describes an attribute of the first processing procedure for obtaining the first product from the first workpiece. Next, based on the obtained first process data, a robot motion path of a second processing procedure that is to be performed on the first product by a robot may be adjusted. Further, embodiments of present disclosure provide apparatuses, systems, and computer readable media for adjusting a robot motion path.

An additive manufacturing system comprises an apparatus arranged to distribute layer of metallic powder across a build plane and a power source arranged to emit a beam of energy at the build plane and fuse the metallic powder into a portion of a part. The system includes a processor configured to steer the beam of energy across the build plane and receive data generated by one or more sensors that detect electromagnetic energy emitted from the build plane when the beam of energy fuses the metallic powder. The received data is converted into one or more parameters that indicate one or more conditions at the build plane while the beam of energy fuses the metallic powder. The one or more parameters are used as input into a machine learning algorithm to detect one or more defects in the fused metallic powder.

In order to provide a checking facility for checking workpieces and also a treatment facility for treating workpieces, which enable efficient and reliable quality optimisation, it is proposed that workpiece parameters are detected, for example by means of an automatic checking station, and a workpiece-specific data set is created on this basis and/or from facility parameters.

A surface data acquisition, storage, and assessment system for detecting and quantifying similarities or differences between stored data and data collected from a scan. The system operates to scan features of an object and weighs various parameters and utilizing fuzzy logic makes a recommendation.