Mechanical & Industrial Engrg 697MA - ST- Intelligent Manufacturing

Innovation in materials, IT, manufacturing and intelligent techniques is propelling the revolution of manufacturing industry. The integration of the innovative materials and artificial intelligent (AI) and sensing techniques in manufacturing processes, called Industry 4.0, is bringing lower cost and higher quality products to our life and changing our life. Hence this course is designed for students to know the theoretical underpinnings of the various intelligent techniques used in manufacturing, including supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks) and unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning), gain the practical know-how needed to quickly and powerfully program and apply these techniques to data and solve new manufacturing problems from numerous case studies, individual and team projects, and review some of best practices of intelligent techniques in broad areas including robotics, text recognition, computer vision, image processing, and medical informatics.
The course aims at a comprehensive introduction to the innovative AI techniques utilized in current manufacturing industry and engagement in hand-on programming of the AI techniques. The course will review the popular AI tools in advanced manufacturing systems, including basic knowledge of statistical learning, dimensionality reduction, support vector machine (SVM), neural networks, and knowledge-based or rule-based intelligent systems. The course will provide senior undergraduate and graduate students with the case studies for applying these methods to manufacturing systems and improve the cost, yield, productivity and reliability of these systems. For students from mechanical engineering, familiarizing them with these innovative AI techniques will prepare them well for the future work of using AI for manufacturing jobs, such as design of products, process planning and control, design of the manufacturing system, scheduling and production control, maintenance and repairing, and management of production quality. Meanwhile, this course is open to students from other engineering departments, such as materials, chemical engineering, IT and computer science, to learn many applications of those domains technology in manufacturing systems.
By the end of the course, the students are expected to understand the popular AI tools in advanced manufacturing systems, including basic knowledge of statistical learning, dimensionality reduction, SVM, neural networks, and knowledge-based or rule-based intelligent systems, can practice the programming of the AI tools in computer languages such as Matlab, can analyze the applications of these methods to manufacturing systems and improve the cost, yield, productivity and reliability of these systems, and can evaluate various AI techniques and their implementation toward addressing the problems that are important in manufacturing and for following the relevant science & engineering literature. The projects are designed for teams which will train students for a team spirit while allowing students showing their strengths in various engineering aspects.?

The 600 level students will need to 1) solve additional graduate-level problems in assignments, 2) more challenging programming in their mini- and final- project.