Abstract: 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.

Abstract: 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.

Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of faults, determine a priority level for each fault by applying the extracted feature data to a predictive model, determine at least one high priority fault, and generate at least one operator alert based on the at least one high priority fault.

Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.

Abstract: Systems and methods for evaluating configuration changes on a machine in a manufacturing line are provided. The machine is controlled by a PLC that stores a plurality of parameters associated with the machine. The method involves operating one or more processors to: obtain, from the PLC, a plurality of first parameter values corresponding to the plurality of parameters stored at the PLC at a first time and a plurality of second parameter values corresponding to the plurality of parameters stored at the PLC at a second time; determine a plurality of parameter value differences; identify a plurality of configuration parameters from the plurality of parameters based on the plurality of parameter value differences, each configuration parameter corresponding to a user-manipulatable parameter associated with controlling the machine; and store a plurality of first configuration parameter values in association with a first timestamp corresponding to the first time.

Abstract: A system and method for conveyor configuration and testing. The system is configured to execute the method, which includes: receive input data relating to configuration of a conveyor system; prepare a simulation of the configured conveyor system; operate the simulation of the conveyor system; determine at least one operational parameter related to the conveyor system to be monitored; monitor the at least one operational parameter during operation of the simulation of the conveyor system; determine if the configuration of the conveyor system needs to be adjusted based on the monitored operational parameter; if the configuration needs to be adjusted, automatically make an adjustment and return to operate the simulation of the conveyor system; and continue the simulation until otherwise terminated. In some cases, the monitoring operational parameters uses a machine learning model based on actual data from operating conveyors.

Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of cell configurations, determine an efficiency score by applying the feature data to a predictive model generated for predicting a production level of the manufacturing assembly line, determine at least one target cell configuration from the cell configurations based on the efficiency score, and apply the at least one target cell configuration to at least one cell by implementing each target cell configuration to a corresponding cell.

Abstract: Systems and methods of monitoring a production level of a vibratory feeder configured to process a workpiece are described herein. The methods include operating a processor to: receive device data associated with the vibratory feeder during operation of the vibratory feeder, the device data comprising at least one input state of the vibratory feeder; receive production data associated with the vibratory feeder, the production data being representative of a production level of the vibratory feeder; determine, based on the device data and/or the production data, one or more faults corresponding to the vibratory feeder when the production level falls below a threshold production level; and determine, based on the one or more faults, a corrective action to return the production level to or above the threshold production level.

Abstract: Systems and methods for calibrating a leak test machine for a manufacturing line are provided. The leak test machine is configured to detect leakage defects in workpieces produced by the manufacturing line. The method involves operating at least one processor to: receive, from the leak test machine, defect data; receive, from at least one sensor, environmental data; update at least one predictive model using the defect data and the environmental data; receive, from the at least one sensor, current environmental data; determine at least one calibration setting for the leak test machine based on the at least one predictive model and the current environmental data, the at least one calibration setting compensating for an effect of the at least one current environment condition on the leak test machine; and test, with the leak test machine using the at least one calibration setting, at least one workpiece.

Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.

Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of faults, determine a priority level for each fault by applying the extracted feature data to a predictive model, determine at least one high priority fault, and generate at least one operator alert based on the at least one high priority fault.

Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.

Abstract: Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of cell configurations, determine an efficiency score by applying the feature data to a predictive model generated for predicting a production level of the manufacturing assembly line, determine at least one target cell configuration from the cell configurations based on the efficiency score, and apply the at least one target cell configuration to at least one cell by implementing each target cell configuration to a corresponding cell.

Abstract: A system and method for managing automation stations having one or more pieces of automation equipment in an automation environment. The system includes: a plurality of data collection devices configured to collect data related to a plurality of actuations performed by at least one automation station based on data collection criteria; and at least one processing module in communication with the plurality of data collection devices and configured to aggregate and analyze the collected data to detect one or more statistical anomalies, wherein the processing module determines an adjustment to the at least one automation station or the automation environment to address the statistical anomaly and implements the adjustment.

Abstract: The disclosure is directed at a method and system for diagnosing automation elements. The method and system include the determination of a trigger condition or event within operation data received from the automation elements. Based on the trigger condition or event, feed-back data captured by data collection devices is tagged and stored and/or captured and stored for review by an end user. The feed-back data may be stored together with associated operation data to assist in identifying faults, the causes of faults, or the like in the automation elements, in the products produced, or in the manufacturing process or overall manufacturing system.