A method for managing nonconformances in laminates. The method comprises recording, by a sensor system, layup information about a layup of layers on a workpiece platform, wherein the layup of layers forms a workpiece and recording inspection information about the laminate on an inspection platform, wherein the laminate is formed from curing the workpiece. An analyzer in a computer system identifies a laminate nonconformance in the laminate using the inspection information and a user input verifies the laminate nonconformance in the laminate is present. An artificial intelligence system is trained by the computer system using the layup information, the inspection information, and the user input verifying the laminate nonconformance.

A control device of a substrate processing apparatus includes a reading unit, an estimation unit, a comparison unit, and a correction unit. The reading unit reads out a reference processing condition for processing a substrate. The estimation unit estimates an actual processing condition when the substrate is processed. The comparison unit compares the reference processing condition and the actual processing condition with each other. The correction unit corrects a processing condition for the substrate based on a comparison result in the comparison unit.

A manufacturing condition setting automating apparatus includes: a quality judging unit that computes a present process quality from facility data at predetermined time intervals, and judges whether or not it is in a quality tolerance range; a manufacturing condition candidate creating unit that computes a feature quantity, searches a database for condition change cases having similar feature quantities, tabulates condition change cases basis on whether the condition change cases are successes or failures, and outputs manufacturing condition candidates in descending order of rates of successes; an imbalance-preventing manufacturing condition candidate creating unit that changes scores that decide ranks of manufacturing condition candidates, and creates a ranking of manufacturing condition candidates; and a manufacturing condition output unit that outputs a set value of a condition change of a top manufacturing condition candidate to the manufacturing facility, and registers a new condition change in the condition change history.

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

High intensity multi-directional FDM 3D printing method for stereo vision monitoring involves intelligent control and computer vision technology. Specifically, it involves multi-directional 3D printing hardware platform construction, stereo vision detection, laser heating to enhance the connection strength between various parts of the model, so as to reduce the use of external support structure as much as possible on the premise of ensuring the printing accuracy, and make the various parts of the model can be well connected to enhance the integrity of the model.

A quality control apparatus includes: a test value acquisition unit that acquires test values of respective components that constitute a product; an assembling management information acquisition unit that acquires assembling management information that is information concerning assembling of the product and the components; a step-related information acquisition unit that acquires step-related information that is information related to a production step; and a display that displays a configuration diagram of the product that is hierarchically constituted by the components and shows a combination of elements that are assumed to be likely to have caused a quality defect of the product on the configuration diagram on a basis of the test values, the assembling management information, and the step-related information.

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.

A method and a device for analyzing an additive manufacturing process. In the method, measurement values for at least one process emission are detected during the manufacture of a workpiece. Standard deviations of the measurement values of predefined volume elements of the workpiece are calculated, and a distribution of the calculated standard deviations of multiple predefined volume elements is analyzed as a measure of the process quality.

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Provided are a system, method, and computer program product for optimizing a manufacturing process. The method includes receiving manufacturing data associated with a manufacturing process for manufacturing a product. The manufacturing data may include data from a plurality of data sources associated with a plurality of stages of the manufacturing process, and the manufacturing data may include values for a plurality of parameters including at least one process parameter value and at least one quality parameter value. The method includes generating a time-sequenced data structure including the manufacturing data and transforming the time-sequenced data structure to a positionally-dimensioned data structure based on timing data associated with the plurality of stages. The method includes determining a new value for the at least one process parameter value based on the positionally-dimensioned data structure and at least one algorithm and optimizing the manufacturing process based on the new value.