Embodiments of systems and methods for supporting weld quality across a manufacturing environment are disclosed. One embodiment includes a manufacturing cell supporting welding of a sequence of welds to manufacture a workpiece. The manufacturing cell includes robotic welding equipment to make robotic welds as at least a portion of manufacturing a workpiece. The manufacturing cell also includes non-robotic welding equipment configured to allow a human operator to make non-robotic welds as at least a portion of manufacturing the workpiece. The manufacturing cell further includes a weld sequence controller configured to control timing associated with making the robotic welds and the non-robotic welds as a sequence of welds to manufacture the workpiece.

A computer-implemented method to control technical equipment that performs a production batch-run of a production process, the technical equipment providing data in a form of time-series from a set of data sources, the data sources being related to the technical equipment, includes: accessing a reference time-series with data from a previously performed batch-run of the production process, the reference time-series being related to a parameter for the technical equipment; and while the technical equipment performs the production batch-run: receiving a production time-series with data, identifying a sub-series of the reference time-series, and comparing the received time-series and the sub-series of the reference time-series, to provide an indication of similarity or non-similarity, in case of similarity, controlling the technical equipment during a continuation of the production batch-run, by using the parameter as control parameter.

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.

Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., to account for chamber-to-chamber variability) using machine learning techniques.

This invention teaches a multi-sensor quality inference system for additive manufacturing. This invention still further teaches a quality system that is capable of discerning and addressing three quality issues: i) process anomalies, or extreme unpredictable events uncorrelated to process inputs; ii) process variations, or difference between desired process parameters and actual operating conditions; and iii) material structure and properties, or the quality of the resultant material created by the Additive Manufacturing process. This invention further teaches experimental observations of the Additive Manufacturing process made only in a Lagrangian frame of reference. This invention even further teaches the use of the gathered sensor data to evaluate and control additive manufacturing operations in real time.

One variation of a method for predicting manufacturing defects includes: accessing a set of inspection images of a set of assembly units recorded by an optical inspection station; for each inspection image in the set of inspection images, detecting a set of features in the inspection image and generating a vector representing the set of features in a multi-dimensional feature space; grouping neighboring vectors in the multi-dimensional feature space into a set of vector groups; and, in response to receipt of a first inspection result indicting a defect in a first assembly unit, in the set of assembly units, associated with a first vector in a first vector group, in the set of vector groups, labeling the first vector group with the defect and flagging a second assembly unit associated with a second vector, in the first vector group, as exhibiting characteristics of the defect.

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.

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

Provided is a management system for performing quality management on production equipment. A management system that includes: an acquisition component that acquires status information for production equipment that is subject to management; a detection component that, on the basis of the acquired status information, detects the occurrence of some event; and a display component that displays, separated according to inclusion in the four perspectives Machine, Man, Material, and Method, a plurality of factors that could be presumed to have caused the detected event in a manner in which the contents thereof and a probability of having caused the event can be compared.

A system for process control of a composite fabrication process comprises an automated composite placement head, a vision system, and a computer system. The automated composite placement head is configured to lay down composite material. The vision system is connected to the automated composite placement head and configured to produce image data during an inspection of the composite material, wherein the inspection takes place at least one of during or after laying down the composite material. The computer system is configured to identify inconsistencies in the composite material visible within the image data, and make a number of metrology decisions based on the inconsistencies.