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A Measurable Impact

Case Study: SICK Industrial Demo Units

Industry Partner: SICK Ltd:
SICK | Sensor Intelligence is one of the world's largest producers and supplier of sensors and sensing technologies. SICK provides solutions for industrial automation, all of which are Industry 4.0 accredited. SICK is also a founding member of the Barrett Centre for Technology Innovation's Advanced Manufacturing Skills Consortium.

To design, build and program several small-scale portable demonstration units that are able to exhibit new technologies. This was the inception of 3 capstone projects, where final year electromechanical students were provided the opportunity to collaborate closely with technical expertise from SICK.

Robot guidance:
The objective of this project was to introduce state-of-the-art sensing technologies. SICK technologies including bin picking cameras, safety laser scanners and safety controllers were integrated with a robotic arm. Cameras and sensors were able to track parts and send coordinates to the robot for pick and place functions.

Industrial 4.0:
The goal of the Industry 4.0 project was to demonstrate the abilities of Industry 4.0 devices developed by SICK by gathering and transmitting data to networked devices and the Internet. This project presents SICK Sensor Intelligence Industry 4.0 data collection technology through device interconnectivity and wireless capacities. To accomplish this goal, sensors measured various sized boxes carried by the conveyor belt and transmit collected data to Microsoft Azure, a local Oracle database, SICK Dynamic Data Display and SICK LiveConnect.

Machine Vision and Auto ID:
The objective of the Machine Vision and Auto ID project was to demonstrate the machine learning, AI and auto ID capabilities of SICK Sensors. These technologies were responsible for sending and receiving data and had deep learning and artificial Intelligence capabilities to detect patterns and abnormalities. The device trains itself to recognize photos through pattern recognition and AI learning to produce an evaluation which is then deployed to the device. This project also included sensors that can scan for different types of data like barcodes and QR codes.

The projects were a success for all involved. Industry, faculty and students worked collaboratively to solve industrial challenges at hand while demonstrating cutting edge applications of the SICK technologies. This will potentially give them a competitive edge in the marketplace. The students involved gained knowledge and hands-on skills while being prepared to integrate into the automation industry.

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