Course Title: Artificial Intelligence for Smart Manufacturing
Part A: Course Overview
Course Title: Artificial Intelligence for Smart Manufacturing
Credit Points: 12.00
Course Coordinator: Program Manager
Course Coordinator Phone: +61 3 9925 4468
Course Coordinator Email: vehs@rmit.edu.au
Course Coordinator Location: RMIT City Campus
Pre-requisite Courses and Assumed Knowledge and Capabilities
None
Course Description
Smart manufacturing was emerged from the blend of artificial intelligence and advanced modern data science technique which provides enhanced productivity, economic optimisation and sustainability. Artificial Intelligence for Smart Manufacturing drastically improves efficiency, eliminating challenges in the traditional manufacturing technologies. This course is designed to introduce you to the industrial manufacturing applications of artificial intelligence, with a focus on machine learning, data analytics and modeling in a manufacturing environment. This course provides a thorough understanding about the cutting-edge technology transforming the manufacturing sector using artificial intelligence techniques such as machine learning, stream data mining, simulation and modelling, automated mine planning and scheduling, decision analytics, remote sensing and real options analysis to efficiently and optimally use the available resources for process improvement.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following program learning outcomes:
1.3. In depth practical knowledge and skills within specialist sub-disciplines of the practice area.
1.4. Discernment of engineering developments within the practice area.
1.6. Understanding of the scope, principles, norms, accountabilities and bounds of contemporary engineering practice in the area of practice.
2.1. Application of established technical and practical methods to the solution of well-defined engineering problems.
2.2. Application of technical and practical techniques, tools and resources to well defined engineering problems.
3.1. Ethical conduct and professional accountability
3.2. Effective oral and written communication in professional and lay domains.
3.3. Creative, innovative and pro-active demeanour.
3.6. Effective team membership and team leadership.
On completion of this course you should be able to:
1. Demonstrate an understanding of advanced artificial intelligence concepts and smart manufacturing applications
2. Identify and apply appropriate artificial intelligence techniques to solve problems in manufacturing operations
3. Apply artificial intelligence algorithms to optimise manufacturing process
4. Evaluate productivity gains offered by the deployment of artificial intelligence solutions
5. Examine ethical implications arising from the development and implementation of artificial intelligence technologies
Overview of Learning Activities
The learning activities included in this course are:
This is a blend of theoretical and practical based course. It is delivered through lectures, tutorials and practical lab sessions in the Computer Laboratory and the Advanced Manufacturing Laboratory. You will get an opportunity to practice some of the artificial intelligence techniques on real and synthetic data and evaluate the strengths and limits of the approaches. You need to attend lectures/tutorials/labs where syllabus material will be presented with examples and practical demonstrations. By completing the tutorial questions, assignment, and laboratory exercises, you will gain further practical knowledges and skills used in smart manufacturing process using artificial intelligence. You will also be working in teams on practical projects which involve both machine learning and data analytics.
Overview of Learning Resources
You will be able to access course information and learning materials through the course CANVAS. Your course CANVAS will give you access to important course-related information such as announcements, staff contact details, online lecture notes and exercises, tutorials, assignment, and other learning resources. Access to CANVAS will be instructed in detail during the course introduction session. Lists of relevant reference books and digitalized materials at RMIT libraries will be available as well. You will also use laboratory equipment and computer software within the School for the project work.
You are advised to check your student e-mail account daily for important announcements.
Overview of Assessment
☒ This course has no hurdle requirements.
☐ All hurdle requirements for this course are indicated clearly in the assessment regime that follows, against the relevant assessment task(s) and all have been approved by the College Deputy Pro Vice-Chancellor (Learning & Teaching).
Assessments
Assessment 1: Assignment
Weighting towards final grade (%): 30
this task assesses the following course learning outcomes:
PLO 1.4, 1.6, 2.1, 3.1, 3.2
CLO 1, 2, 4, 5
Assessment 2: Practical Reports
Weighting towards final grade (%): 30
this task assesses the following course learning outcomes:
PLO 1.3, 1.6, 2.1, 2.2, 3.1, 3.2, 3.3, 3.6
CLO 1, 2, 3, 4, 5
Assessment 3: Project and Presentation
Weighting towards final grade (%): 40
this task assesses the following course learning outcomes:
PLO 1.3, 1.6, 2.1, 2.2, 3.1, 3.2, 3.3, 3.6
CLO 1, 2, 3, 4, 5