Ats, Inc.

Ats Automation Tooling Systems Inc.

Stanley Kleinikkink

Nick Foisy

Masoud Kheradmandi

Yang Wang

Mehdi Sadeghzadeh

Bo Yuan Chang

Nicholas Willison

Stephen Bacso

Ian Cameron

Javan Taylor

Daniel Blair De Verteuil

Imran Ahmed

Steven Alan Christmann

Stephen Fisher Aikens

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.

Systems and methods for calibrating a leak test machine for a manufacturing line

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.

System and method for diagnosing a manufacturing line using tagged data

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.

Systems and methods for modeling a manufacturing assembly line

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.

System and method for conveyor system configuration, testing and diagnostics

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.

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Cambridge, Canada

Stanley Kleinikkink