An AI enhanced self-correcting and closed loop SMT manufacturing system for fabricating PCBAs. The system includes a screen printer for depositing solder paste on solder pads on a RGB, an SRI sub-system for inspecting the solder paste deposited on the PCB to identify defects, a pick-and-place machine for placing circuit components on the solder paste, an AOI sub-system for inspecting the PCB after the circuit components are placed on the PCB, and a reflow soldering oven for bonding component leads both electrically and mechanically to the pads on the PCB. An AI/ML analysis engine is responsive to process data and variables from each of the screen printer, the SPI sub-system, the pick-and-place machine, the AOI sub-system and the reflow soldering oven and provides downstream feedback signals to each of the screen printer, the SPI sub-system, the pick-and-place machine, the AOI sub-system and the reflow soldering oven for self-correction purposes.

The disclosure provides systems and methods of use in the analysis, collection, resource allocation, and tracking associated with the sale of commodities. Embodiments include a vibratory-and-optical analysis and collection system that may be incorporated into a collection and storage machine. The analysis and collection system and/or the collection and storage machine may be associated with a consumption-based resource-allocation system that determines a payment price for a batch of commodity sold and then digitally allocates all transaction resources to the relevant stakeholders to the commodity sales transaction occurring at the analysis and collection system and/or the collection and storage machine. A commodity-to-consumer tracking system may be provided to track the batch of commodity sold from the point of harvest and sale through to the end consumer. Other embodiments are disclosed.

Aspects of the disclosed technology encompass the use of a deep-learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.

A machine learning module is provided trained to generate from a design data record specifying a design variant, a predictive performance distribution and a constraint compliance distribution of the design variant. A predictive performance distribution and a constraint compliance distribution are generated by the machine learning module. The predictive performance distribution is compared with performance values of previously evaluated design data records. A simulation of the corresponding design variant is either run or skipped. A design evaluation record is output which includes a performance value and constraint compliance data each derived from the simulation if the simulation is run or, otherwise, each derived from the predictive performance distribution and the constraint compliance distribution. Depending on the design evaluation records, a performance-optimizing and constraint-compliant design data record is selected from the variety of design data records. The selected design data record is then output for controlling the production system.

Embodiments of this application provide an assembly verification method and an electronic device, and are applied to the field of computer technologies. The method includes: determining coordinates of a high-risk assembly position of each test point in a to-be-mounted component when the to-be-mounted component and a mounted component are assembled; determining, based on a three-dimensional tolerance dimension chain and the coordinates of a high-risk assembly position of each test point, a gap value distribution interval when the to-be-mounted component and the mounted component are assembled; and determining, based on the gap value distribution interval, whether there is an interference when the to-be-mounted component and the mounted component are assembled.

A machining dimension prediction apparatus includes a trend acquirer that acquires, for each workpiece, trend information indicating a trend of a state of a machining tool during a machining period of machining performed by the machining tool, a feature calculator that calculates, based on the trend information, a feature using the trend of the state in each of sections included in the machining period, a measurement value acquirer that acquires a measurement value of a dimension of each workpiece after being machined, a section specifier that specifies, as a specific section of the sections, a section including a calculated feature having a greatest degree of relevance to the measurement value, and a predictor that predicts, when a new-target workpiece is machined, a dimension of the new-target workpiece after being machined based on the feature calculated using the trend of the state in the specific section.

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.

Described are systems and methods for advanced process control and monitoring. Systems and methods may be associated with a data processing module configured to receive and process a plurality of data types and datasets from a plurality of different sources for generating training data; a training and optimization module configured to provide the training data to a machine learning pipeline for training and optimizing a model; and an inference module configured to use the model for generating one or more predicted metrics substantially in real-time, wherein the one or more predicted metrics are useable to characterize an output of a process performed by a process equipment.

An event processing system identifies a process event associated with an identified defect in a manufacturing process. The event processing system selects a plurality of data elements from a manufacturing data source based on the process event. The manufacturing data source is associated with the manufacturing process during execution of the manufacturing process. During execution of the manufacturing process, the event processing system applies an event rule to the plurality of data elements to determine whether the event rule is satisfied. During execution of the manufacturing process, the event processing system performs a predefined action upon determining that the event rule is satisfied and selects additional data elements from the manufacturing data source upon determining that the event rule is not satisfied.

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.