Smart Manufacturing and Systems
The leading-edge professional graduate degree program in Smart Manufacturing and Systems offers unique hands-on learning and training in new Industry 4.0 technologies (artificial intelligence, the Internet of Things, and digital twins combined with industrial automation and robotic systems), which are driving digital transformation in the manufacturing industry. The program is paired with real-world experience from an industrial internship program of at least four months; this way, it prepares graduates for working in the new digital manufacturing sector related to Industry 4.0 and expedites their absorption into the job market and their impact on the future of manufacturing.
Applicants must satisfy the University admission requirements as stated in Graduate General Regulation 1.3 in the SFU calendar, and hold a bachelor’s degree, or equivalent in mechatronic engineering, computer science, or a related field.
The School’s Graduate Admissions Committee may recommend, at its discretion, admission to the Professional Master’s program to exceptional students without an undergraduate degree in mechatronic engineering, mechanical engineering, manufacturing engineering or a related field. Students who do not meet the minimum university requirements may be recommended as conditional or qualifying students as per Graduate General Regulation (GGR) 1.3.8 or 1.3.9. For further information on conditional or qualifying admission requirements, please contact the Program Coordinator.
This program consists of course work and an industrial internship for a minimum of 30 units.
Students must complete all of
The Internet of Things (IoT) looks at its application to industrial systems and digital transformation technologies. Study of data collection, visualization, analysis, security, privacy, and optimization in IoT and Industrial IoT (IIoT). Implementation aspects of IoT devices in Industry 4.0 and digital twin technologies. Prerequisite: Recommended Prerequisite: MSE 310 or equivalent.
The smart factory is integral to Industry 4.0. Students will be provided with hands-on experience in main components of smart factory workcells. Students learn to design, install, maintain and troubleshoot key digital transformation components and automation equipment used in modern industrial production processes. A major component of the course is lab-based training using state-of-the-art industrial training equipment including programmable logic controllers, electro-pneumatics, and industrial robots. Prerequisite: Recommended Prerequisite: MSE 310, MSE 250, and MSE 352 (or equivalent).
Smart automation takes industrial automation to the next level. Smart automation components and their integration for the application and implementation of automation tasks in Industry 4.0 production systems are introduced. Students analyze and simulate a smart manufacturing facility in terms of production time, cycle time, scheduling tasks, materials, cost, quality, labour, etc. A major component of this course is lab-based training using state-of-the-art industrial equipment. Prerequisite: MSE 923. Recommended Prerequisite: MSE 353 (or equivalent).
Industry 4.0 is the future of manufacturing which is driven by artificial intelligence, the Internet of Things, and the resulting digital transformation technologies such as digital twins. A digital twin is a virtual model of an industrial process, product, service or system across its life-cycle using real-time data to enable analysis, learning and reasoning. In the Industry 4.0 future, smart factories using additive manufacturing such as 3D printing and other computer-aided manufacturing systems are able to adaptively manufacture parts on demand, direct from digital twin designs. This course provides a comprehensive coverage on, among others, the role of data, manufacturing systems, various Industry 4.0 technologies, applications and case studies. Prerequisite: Recommended Prerequisite: MSE 380 or equivalent.
and one of
Concepts and problem-solving techniques that are used in the design and analysis of efficient algorithms. Special consideration and adaptations for big data applications will be emphasized. Students with credit for CMPT 705 may not take this course for further credit.
The student will learn basic concepts and techniques of data mining. Unlike data management required in traditional database applications, data analysis aims to extract useful patterns, trends and knowledge from raw data for decision support. Such information are implicit in the data and must be mined to be useful.
We 1:30 PM – 2:20 PM
Fr 12:30 PM – 2:20 PM
AQ 3005, Burnaby
AQ 3005, Burnaby
Cybersecurity involves technology, people, information, and processes to enable assured operations in the existence of vulnerabilities, and adversaries who exploit them. Students will gain insight into the importance and landscape of cybersecurity, understand its career paths, and learn about cyber risk management, network and cloud security, system and software security, and cyber ethics and law.
Data is the lifeblood of the smart factory. Provides students with hands-on experience in big data analytics. Students in this course learn about life cycle of big data analytics for Industry 4.0 from data collection to data preparation to data mining. As a result, they are empowered with the skill of handling massive, heterogeneous manufacturing data in highly distributed environments of Industry 4.0.
and one of
Overview of manufacturing systems: industrial robotics, numerical control and metal cutting, manufacturing system components and definitions, material handling systems, production lines, assembly systems, robotic cell design, cellular manufacturing, flexible manufacturing systems, quality control, and manufacturing support systems. Students are required to complete a project.
Provides insight regarding advanced additive manufacturing technologies in various mechatronic applications. Comprehensive knowledge is presented relevant to advanced 3D printing technologies including direct writing, paste extrusion, and laser direct writing. Topics range from 3D printable material design to application-driven engineering design technology trends including state-of-the-art 3D printed applications. Students will learn the practical perspective of advanced additive manufacturing with various engineering materials: polymers, metals, composites, nano-materials, and biomaterials.
Hands-on practice with solid modeling, basic machine shop, measuring, and rapid prototyping tools. Knowledge and skills in geometric modeling, engineering materials, geometric dimensioning and tolerancing, and quality control. Students gain understanding of the process from product development to manufacturing. Prerequisite: Graduate standing in the Master of Engineering program in Smart Manufacturing and Systems.
and a minimum of three units chosen in consultation with the graduate program chair
and a minimum of one industrial internship
Internship in industry or a research environment for graduate research students. A final report will be submitted and graded by the student's supervisor. Graded on a satisfactory/unsatisfactory basis. Prerequisite: 12 units of MSE course work at the 700-level or higher with a minimum SFU CGPA of 3.0. Approval of supervisor and a GPC representative is required prior to applying for and accepting an internship.
Students are expected to complete the program requirements within four terms.
Academic Requirements within the Graduate General Regulations
All graduate students must satisfy the academic requirements that are specified in the Graduate General Regulations, as well as the specific requirements for the program in which they are enrolled.