A cyber-physical system type machining system includes: a machine tool disposed in a real world and including a machine body and a control device; and a computer device connected to communicate with the control device and including a processor and a memory storing a program for generating, in a virtual world, a virtual machining phenomenon corresponding to an actual machining phenomenon with regard to a workpiece and the machine body. The program, when executed by the processor, causes the computer device to perform: acquiring a command value in synchronization with the control device, the command value for controlling the machine body by the control device; generating a future virtual machining phenomenon, which is the virtual machining phenomenon in a future, based on the command value; and outputting, to the control device, an optimal command value for correcting the command value based on the future virtual machining phenomenon.

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.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a process of manufacturing a product. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of input settings for manufacturing a product, based on a causal model that measures causal relationships between input settings and a measure of a quality of the product; ii) determining the measure of the quality of the product manufactured using the configuration of input settings; and iii) adjusting, based on the measure of the quality of the product manufactured using the configuration of input settings, the causal model.

A substrate processing condition setting method includes acquiring, causing, and setting. In the acquiring, a plurality of estimation processing results are acquired by inputting a plurality of processing conditions to a trained model that is subjected to machine training based on a training processing condition and a processing result obtained by processing a substrate under the training processing condition. In the causing, a display section is caused to display an image based on the estimation processing results. In the setting, one processing condition corresponding to one estimation processing result of the estimation processing results is set, as an actual processing condition in substrate processing, based on the image displayed on the display section.

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.

This calculation device for predicting the surface roughness of a processed product from a physical quantity includes: a measurement data acquisition unit which acquires measurement data of surface roughness measured by a surface roughness measuring device; a physical quantity acquisition unit which acquires a physical quantity indicating a factor that causes surface roughness; a first amplitude spectrum conversion unit which converts the measurement data into a first amplitude spectrum; a second amplitude spectrum conversion unit which converts the physical quantity into a second amplitude spectrum; and a coefficient calculation unit which calculates a coefficient on the basis of a specific frequency, the second amplitude spectrum, and the first amplitude spectrum.

A system may include a first semiconductor processing station configured to deposit a material on a first semiconductor wafer, a second semiconductor processing station configured perform measurements indicative of a thickness of the material after the material has been deposited on the first semiconductor wafer, and a controller. The controller may be configured to receive the measurements from the second station; provide an input based on the measurements to a trained model that is configured to generate an output that adjusts an operating parameter of the first station such that the thickness of the material is closer to a target thickness; and causing the first station to deposit the material on a second wafer using the operating parameter as adjusted by the output.

A cloud-based analytics engine that analyzes data relating to an industrial automation system(s) to facilitate enhancing operation of the industrial automation system(s) is presented. The analytics engine can interface with the industrial automation system(s) via a cloud gateway(s) and can analyze industrial-related data obtained from the industrial automation system(s). The analytics engine can determine correlations between respective portions or aspects of the system(s), between a portion(s) or aspect(s) of the system(s) and extrinsic events or conditions, or between an employee(s) and the system(s). The analytics engine can determine and provide recommendations or instructions in connection with the industrial automation system(s) to enhance system performance based on the determined correlations. The analytics engine also can determine when there is a deviation or potential of deviation from desired system performance by an industrial asset or employee, and provide a notification, a recommendation, or an instruction to rectify or avoid the deviation.

Broadly speaking, the present techniques provide a method and system for controlling a wafer production process in real-time using a trained machine learning, ML, model. Advantageously, the ML model uses multiple sensed parameters to determine a state of a plasma used in the wafer production process, and this can be used to adjust at least one control parameter of a plasma reactor used in the wafer production process to reduce process variability.

A system for manufacturing a product having predetermined specifications includes a working station for manufacturing the product, which has a plurality of working operating parameters; first sensors for detecting first data regarding the working environment; second sensors for detecting second data regarding the plant where the system is installed; and a first control device operatively connectable to the working station and to the first and the second sensors, which has a storage unit containing a plurality of optimal operating parameters. During operation of the working station, the control device detects the first and the second data and compares the working operating parameters with the corresponding optimal operating parameters so as to detect deviations. A second device determines the optimal operating parameters during operation of the working station.