Disclosed are an integrated inspection system for a 3D printing process using a thermal image and a laser ultrasound wave and a 3D printing system having the inspection system. The inspection system includes a thermal imaging camera for creating a thermal image of a molten pool formed in a printing object when a base material supplied to the printing object is melted by a laser beam irradiated from a 3D printing laser source, a laser ultrasonic device for receiving a laser ultrasonic wave included in the laser beam reflected from the printing object, and a control unit for estimating a physical property of the printing object and detecting a defect of the printing object based on the thermal image created by the thermal imaging camera and the laser ultrasound wave received by the laser ultrasonic device. The thermal imaging camera and the laser ultrasonic device are disposed coaxially with the 3D printing laser source.

A production system includes an information processing device that carries out the processes of: (i) generating one or more production condition candidates each of which is a candidate for a production condition under which the product is produced; (ii) determining, using a prediction model, a prediction of a production result of a case in which the product is produced under each of the one or more production condition candidates; and (iii) generates, by evaluating a result of the prediction based on a predetermined evaluation standard, an evaluation of each of the one or more production condition candidates. The information processing device repeats the process (ii) while changing between the one or more production condition candidates, and determines a production condition candidate, the evaluation of which in the process (iii) satisfies a predetermined standard, to be the production condition under which the product is produced.

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 product. The monitoring platform is configured to monitor progression of the product 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 product.

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.

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.

A quality control device that controls quality of a product manufactured through a plurality of processes, includes a prediction model generation unit that generates a prediction model to predict quality of a product with respect to unknown process data by performing learning using known process data obtained from the plurality of processes and a measured value of quality of the product with respect to the known process data as learning data; a quality prediction unit that derives a predictive value of quality of each of a plurality of products, which are manufactured after the prediction model is generated, on the basis of the prediction model using process data of the plurality of products as input data; and an inspection target decision unit that decides the product for which the predictive value having the smallest margin with respect to a preset standard is obtained as an inspection target, among the plurality of predictive values of quality obtained by the quality prediction unit.

Provided are a product performance prediction modeling method and apparatus, a product performance prediction method, a product performance prediction system, a computer device, and a storage medium. The product performance prediction modeling method includes: acquiring first sample data, wherein the first sample data includes device outlier data generated in a process of manufacturing a product by a device; acquiring a production line configuration simulation parameter of a production line where the device is located, and product information of the product manufactured by the production line; selecting a simulation model to perform simulation test on the performance of the product, so as to obtain product performance simulation data; and inputting the device outlier data, the production line configuration simulation parameter, the product information and the product performance simulation data into a machine learning model to perform machine learning training, so as to obtain a product performance prediction model. The foregoing product performance prediction modeling method and apparatus, product performance prediction method, product performance prediction system, computer device and storage medium can accurately predict product performances during device exception.

One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

An inspection information prediction apparatus includes an environment-information acquisition unit that acquires environment information of a routing step through which an inspection target has been routed before an inspection step of inspecting the inspection target, a manufacturing-information acquisition unit that acquires manufacturing information of the inspection target, and a prediction unit that predicts inspection information which indicates an inspection result of an inspection portion of the inspection target determined by the manufacturing information and is obtained by applying the environment information, based on the manufacturing information of the inspection target and the environment information of the routing step through which the inspection target has been routed.

A manufacturing system is disclosed herein. The manufacturing system may include one or more station, a monitoring platform, and a control module. Each station 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.