Curriculum: Master of Engineering in Mechanical Engineering

Introduction to sensor principles, design and implementation, signal conditioning, instrumentation and networking, and applications in engineering practice. Designed for CEAS students and students with STEM backgrounds.

Industry 4.0 describes the evolution of industry toward inter-connectivity, automation, machine learning, and basing decisions on real-time data acquisition. The course provides students a broad understanding of the industrial internet of things (IIOT) which encompasses physical production and operations with smart digital technology, machine learning, and big data analysis to create a connected ecosystem for organizations that focus on manufacturing and supply chain management.

This course examines the non-technical factors that enable engineers and other technical professionals to maximize their contribution to organizational effectiveness. The course covers communication processes and impediments to effective communication including written communication, presentations, and meeting facilitation. Models of motivation as regards technical professionals are presented and their application to the work setting are examined. Leadership models and the interaction of leaders and followers are also presented. Conflict management and appropriate methods for constructively dealing with this are discussed. Students develop personal development plans for continued learning and performance improvement.

Industrial big data includes all types of data generated from industry applications consisting of machine operation, manufacturing process and maintenance events, etc. In today’s competitive business environments, companies have urgent needs to use advanced analytical tools to manage their industrial data to gain more insights of their operations. This course will introduce students to advanced technologies—such as advanced signal processing, pattern recognition & machine learning and predictive analytics—that ultimately enable the conversion of industrial big data into actionable information that can be used to improve the design, the productivity and the efficiency of manufacturing operations.

This is a course for students in their first or second year of their graduate studies and for undergraduate students in the senior year. This course introduces and analyzes intelligent systems used in flexible manufacturing systems. The coursework includes expert systems, fuzzy logic, neural networks and applications with intelligent systems in manufacturing and material handling. The student's understanding gained from this course will be evaluated by a midterm and final exams, homework assignments and a course project.

The course introduces students to the fundamentalsand technological aspects of robotics. It presentsthe industrial and advanced applications of robot manipulators and wheeled mobile robots. It concerns the theory of manipulator structures including kinematics, statics and trajectory panning, and the technology of robot actuators, sensors and control units.

In this course students will be introduced to additive manufacturing technology or, as it’s more widely known, 3D printing. This will include addressing the benefits and drawbacks of additive as compared to subtractive manufacturing, the various modalities and materials available, the different components of the production cycle, part and support structure design considerations, cutting edge AM techniques, and monitoring, inspection, and surface modification techniques. This lecture-based content will be complemented by two main hands-on project components using fused deposition modeling (FDM) and laser powder bed fusion (L-PBF) techniques where students will design and print parts.

Interdisciplinary Innovation for Engineers provides students in technical disciplines a practical and focused understanding of innovation competencies and processes needed to add high-order value to all organizations in a dynamic economy. This interdisciplinary course will enable students to develop foundational attitudes, skills, and knowledge for both core business and engineering innovation functions. This course is designed as a practical, systems-oriented, meaningful experience for learning future-oriented competencies to contribute to all types of organizations while accelerating career enhancement

This course is designed for professionals who already have basic knowledge of Python programming and basic algorithms, but are looking to refresh their knowledge and expand their hands-on skills on recent Machine Learning tools. Students will be introduced to popular AI techniques conceptually and will learn to evaluate the performance of the algorithms themselves, taking advantage of the latest tools available. Furthermore, they will learn how to adapt and customize the algorithms on new problems reviewing examples. Supervised learning techniques (regression, classification, neural networks and SVM), unsupervised learning techniques (clustering, SOM, PCA) and anomaly detection algorithms will be covered. This course is not approved for use in EECS Graduate programs.

Individual projects under supervision of departmental faculty in partial fulfillment of the Master of Engineering degree. Students will conduct a project under the direction of a program faculty, provide a formal report on the project, and present the project to faculty and peers.