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.

According to one embodiment, a manufacturing apparatus control system includes a defect rate detector, a significant difference tester and a defect determining unit. The defect rate detector extracts a first apparatus passage history having a first defect rate. The defect rate detector detects a third defect rate by excluding a second apparatus passage history having a second defect rate from the first apparatus passage history. The significant difference tester calculates a significant difference test value. The defect determining unit extracts a third apparatus passage history based on the third defect rate and the significant difference test value.

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 method for setting testing thresholds applied by a testing device to products being made includes obtaining an initial lower threshold for testing the products and counting, followed by manual review, first, second, third, and fourth type product qualities as being quantities under the initial lower threshold. The method adds a minimum product parameter of defective products, the initial lower threshold, and a number of values between the minimum product parameter and the initial lower threshold into a set, repeating the application of one selected element from the set as an experiment threshold. First to fourth type quantities of the current products are counted again under the experiment threshold, an effectiveness of each element of the set is calculated, and an element of the set with the maximum effectiveness is defined as a suggested lower threshold for testing the products.

Aspects of the disclosed technology provide a computational model that utilizes machine learning for detecting errors during a manual assembly process and determining a sequence of steps to complete the manual assembly process in order to mitigate the detected errors. In some implementations, the disclosed technology evaluates a target object at a step of an assembly process where an error is detected to a nominal object to obtain a comparison. Based on this comparison, a sequence of steps for completion of the assembly process of the target object is obtained. The assembly instructions for creating the target object are adjusted based on this sequence of steps.

A workpiece quality analysis method includes: selecting an initial algorithm and a corresponding algorithm parameter combination from a plurality of preset algorithms; clustering a workpiece data into groups according to the initial algorithm and the algorithm parameter combination to obtain an initial model of the initial algorithm and a corresponding clustering result; obtaining a corresponding initial model evaluation index value according to the clustering result; selecting at least one parameter combination of another algorithm corresponding to the initial algorithm. According to the initial calculation, the method corresponds to the other algorithm parameter combination to group the workpiece data to obtain at least one other model and at least one other clustering result.

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 method for quality control in industrial manufacturing for one or more production processes for producing at least one workpiece and/or product includes creating a learning model for at least one production process for the at least one workpiece and/or product. The learning model is trained and initialized using a meta-learning algorithm, and the learning model is calibrated using normalized data of the at least one production process for the at least one workpiece and/or product. Currently generated data of the at least one production process for at least one currently produced workpiece/product is forwarded to the learning model. The data is generated by sensors. The learning model compares the currently generated data with the normalized data and finds deviations. The learning model scales the deviations between the currently generated data and the normalized data, and the learning model communicates presence of an anomaly for the currently produced workpiece/product.

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 for processing previously detected identifications of apparatuses of an aircraft, wherein each apparatus has at least one communicative component. A reference dataset is used to compare the actual identifications with specified identifications, as a result of which a report signal based on the comparison result indicates whether the detected identifications correspond to the identifications represented by the reference dataset. If there is a positive match, this allows the conclusion that the apparatuses are correctly installed in the aircraft.