SICK and Humber College collaborate for sixth year to improve manufacturing skills of Canadian employees and students
In its sixth year, the partnership between Humber College’s Barrett Centre for Technology Innovation (BCTI) and SICK continues to transcend boundaries and drive innovation.
Through the Manufacturing Skills Consortium, of which SICK is a proud founding partner, we collaborate with nine global industry leaders, including Festo, SEW EuroDrive, KUKA Robotics, and others, to enhance the skills of Canadian employees and students.
This ongoing collaboration between SICK Sensor Intelligence and Humber College represents a visionary partnership. It builds upon SICK’s original commitment, supporting research related to Industry 4.0, the Internet of Things, and Industrial Automation. Moreover, it aligns with the vision for the future, shaping the workforce of tomorrow and nurturing innovation.
The SICK Sensor Lab
The establishment of the SICK Sensor Lab at the BCTI and the introduction of scholarships underscore our dedication to nurturing talent and fostering leadership. Together, SICK and Humber are paving the way for the next generation of innovators in Canada.
Our collaboration has yielded customer and employee training sessions, technology days, student projects involving AGVs and Robotics, and three impressive in-house demos for SICK.
Recently, eight Electromechanical Engineering Technology students embraced the challenge of three capstone projects in partnership with SICK. These projects not only showcased SICK’s cutting-edge sensor technology but also transformed innovative ideas into reality.
Robotic Collaborations
Fei Geng, Application Engineer, and Jose Murillo, Sr. Industrial Technician, devoted their time and expertise to nurture the next generation of professionals through knowledge exchange on a wide range of SICK Sensors. Their dedication was instrumental in fostering a true sense of learning.
The Capstone Projects, spearheaded by students, showcased remarkable ingenuity. From the UR3 Robot Guidance System, utilizing SICK sensors for precise item placement, to Industry 4.0 exploration integrating deep learning and digital transformation, lastly a Machine Vision system with Auto ID.
Each project demonstrated the versatility and innovation of SICK technology all equipped with safety applications which were integrated into all systems, showcasing a full solution ecosystem and SICK's commitment to excellence.
