A system for characterizing a specimen is disclosed. In one embodiment, the system includes a characterization sub-system configured to acquire one or more images a specimen, and a controller communicatively coupled to the characterization sub-system. The controller may be configured to: receive training images of one or more features of a specimen from the characterization sub-system; receive training three-dimensional (3D) design images corresponding to the one or more features of the specimen; generate a deep learning predictive model based on the training images and the training 3D design images; receive product 3D design images of one or more features of a specimen; generate simulated images of the one or more features of the specimen based on the product 3D design images with the deep learning predictive model; and determine one or more characteristics of the specimen based on the one or more simulated images.

A pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.

The present invention provides methods and systems for manufacturing process control of photovoltaic products. Some embodiments relate to a method for tracking wafers for photovoltaic products with respect to which production tool processed them and their position within that production tool. Some embodiments relate to measuring and characterizing the critical-to-quality parameters of the partially-finished photovoltaic products emerging from the production tool in question. Some embodiments relate to display and visualization of the measured parameters on a computer screen, such that the parameters of each production unit can be directly observed in the context of which production tools processed them, which location within a specific production tool they were located in during processing, and which batch, or in the case of continuous processing, what time, the unit(s) was/where processed.

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.

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, the first sample data including 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 relating to a location of the device, 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, 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, to obtain a product performance prediction model.

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.

Computer-implemented methods and systems for feeding workpieces to a manufacturing line are provided. An example method involves operating at least one processor to: receive, from at least one image device proximal to a bowl feeder, a sequence of images of workpieces within the bowl feeder; determine a flow velocity of the workpieces within the bowl feeder; generate bowl feeder control settings by applying the flow velocity to a predictive model; and automatically apply the bowl feeder control settings to the bowl feeder. Computer-implemented methods and systems for predicting anomalies in a manufacturing line are also provided. An example method involves operating at least one processor to: receive a sequence of images of workpieces in the manufacturing line; extract feature data from the sequence of images; apply the feature data to a predictive model to detect anomalies in the manufacturing line; and generate annotations to locate the anomalies within the images.

Computer-implemented methods and systems for feeding workpieces to a manufacturing line are provided. An example method involves operating at least one processor to: receive, from at least one image device proximal to a bowl feeder, a sequence of images of workpieces within the bowl feeder; determine a flow velocity of the workpieces within the bowl feeder; generate bowl feeder control settings by applying the flow velocity to a predictive model; and automatically apply the bowl feeder control settings to the bowl feeder. Computer-implemented methods and systems for predicting anomalies in a manufacturing line are also provided. An example method involves operating at least one processor to: receive a sequence of images of workpieces in the manufacturing line; extract feature data from the sequence of images; apply the feature data to a predictive model to detect anomalies in the manufacturing line; and generate annotations to locate the anomalies within the images.

A manufacturing condition optimization apparatus includes a yield estimator for estimating a yield of a product having a quality that passes inspection by inspection equipment when a manufacturing condition of the product that is manufactured by manufacturing equipment is changed and an optimization processor for calculating an amount of change in the manufacturing condition at which the yield is maximized.

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system. The apparatus, engine, system and method may include a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor associated with a computing memory and suitable for executing code from the computing memory. The code may comprise an adaptor capable of pushing the received raw data to one or more databases to processed data; an extractor capable of extracting the processed data from the one or more databases; predictive analytics capable of receiving the extracted processed data and applying thereto at least one predictive model comprised of target data for the at least one machine, and capable of providing feedback to the at least one machine to modify performance of the at least one machine based on the application of the at least one predictive model; and a visualizer capable of providing at least a visualization of the feedback and of the performance.