The present invention relates to a method for testing a component, in particular an aircraft engine, comprising the steps of: determining (S40) a value of a first toleranced parameter (A1; A2) of the component; determining (S50) a value of a second toleranced parameter (E1; ...; E4) of the component; and classifying (S70) the component in a predefined quality class if this value pair lies outside of a predefined tolerance range, the upper and/or lower limit (G) of which for the second parameter depends on the first parameter, in particular linearly, in at least one first permissible value range (Ta1,1) of the first parameter.

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.

A coating production line system for coating work pieces comprises a coating powder, a coating apparatus, an inspection unit to measure the thickness of the applied coating, a conveyor unit to move the work pieces through the system, and a control unit to use thickness requirements and coating parameters to control the coating apparatus based on said coating parameters with a machine learning instance. A database comprises coating powder characteristics parameter as input vector for the machine learning instance for generating an output vector to control the coating apparatus being a first additional part vector. The control unit determines the coating quality based on a comparison between the thickness data acquired from the inspection unit and the retrieved thickness requirement data as second additional part vector. The first and second additional part vectors are fed back as additional parts to the next input vector for the machine learning instance.

A system for inspecting and validating processes performed on a continuous web of fabric in an automated apparel manufacturing environment. The continuous web of fabric can move in a step wise fashion across a work area where tooling can perform one or more processes on the continuous web of fabric. At least one projector is provided to display an image onto the continuous web of fabric the image including a first image related to an article to be manufactured and a second image related to a reference grid. The continuous web of fabric and the first and second images are viewed by a camera, and data related to the viewed first and second images and the continuous web of fabric can be sent to a computer implemented control center which can analyze the data to determine whether a deviation or error exists regarding the manufacturing process.

A production system includes a cell control apparatus that is connected to a machine control apparatus and an inspection control apparatus. The cell control apparatus includes a storage part that stores data on a state of the manufacturing machine, data on an environment state, and data on an inspection result of a product. The cell control apparatus includes a correlation analysis part that selects a variable relating to an abnormality based on a correlation between the inspection result of the product, and the data on the state of the manufacturing machine and the data on the environment state when the abnormality occurs in the inspection result, and a determination value setting part that sets a determination value of the variable relating to the abnormality.

A device for computing correction for control parameter in a manufacturing process executed on a manufacturing apparatus includes circuitry which acquires an index representing fluctuation in a manufacturing apparatus, acquires an apparatus model and a process model, acquires an output from a sensor in the manufacturing apparatus, transforms the output into first fluctuation for a process element, transforms the index into second fluctuation for the process element based on the apparatus model, computes fluctuation for performance indicator from the first and second fluctuation based on the process model, computes correction for the performance indicator from control range for the performance indicator and the fluctuation for the performance indicator, and converts the correction for the performance indicator into correction for each process element based on the process model such that correction for control parameter in process executed on the manufacturing apparatus is computed from the correction converted for each process element.

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 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.

A system and method autonomously determines the impact of respective tool parameters on tool performance in a semiconductor manufacturing system. A parameter impact identification system receives tool parameter and performance data for one or more process runs of the semiconductor fabrication system and generates a separate function for each tool parameter characterizing the behavior of a tool performance indicator in terms of a single one of the tool parameters. Each function is then scored according to how well the function predicts the behavior of the tool performance indicator, or based on a determined sensitivity of the tool performance indicator to changes in the single tool parameter. The tool parameters are then ranked based on these scores, and a reduced set of critical tool parameters is derived based on the ranking. The tool performance indicator can then be modeled based on this reduced set of tool parameters.

A workpiece data processing method and apparatus are for accurately determining a relationship between production equipment processing parameters/ambient condition data and workpiece quality inspection results. A workpiece data method includes acquiring processing condition data, a quality attribute value and quality inspection result data of each of multiple workpieces processed by a piece of production equipment, the processing condition data of one workpiece including a processing parameter used by the production equipment when processing the workpiece and ambient condition data of the production equipment when processing the workpiece; determining a first relationship between the quality attribute value of the workpiece processed by the production equipment and the ambient condition data of the production equipment when processing the workpiece and the processing parameter of the production equipment; and determining a second relationship between the quality inspection result data and quality attribute value of the workpiece processed by the production equipment.