Curriculum: Master of Engineering in Mechanical Engineering

November 15, 2020
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curriculum icon Curriculum at a Glance

The Master of Engineering in Mechanical Engineering online requires students to successfully complete a minimum of 27 semester credit hours of specific coursework and one capstone course (3 semester credit hours), for a total of 30 semester credit hours.

Industry 4.0 Domains of Knowledge

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.

Effectiveness in Technical Organizations

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.

Introduction to Industrial Artificial Intelligence

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.

Intelligent Systems Theory

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.

Introduction to Robotics

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.

Sensor and Data Acquisition

This course covers topics related to the selection and utilization of analog, digital and piezoelectric sensors in manufacturing and other industrial plants.  Methods for connecting these sensors to digital controllers and program the controllers for electrometrical device monitoring and protection.  Data generation from sensors and methods for storing and analyzing the data will be covered.

Design for Additive Manufacturing

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.

AI & Machine Learning

Students ae introduced to tools and methodologies to apply AI and machine learning concepts to various engineering problems.  Database and programming essentials are presented to provide the necessary foundational knowledge.  Concepts including supervised and unsupervised learning, self-organized maps, search problems, and Markov decision problems are presented then utilized to illustrate applications.

Master of Engineering Capstone Project

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.

Management of Innovation

The Management of Innovation course is a comprehensive review of the concepts of imagination, creativity, innovation, and entrepreneurship. This practical course focuses on the twelve elements of innovation and the twelve innovation competencies: innovative behaviors, thinking, problem solving, knowledge, creativity insights, culture building, innovation, entrepreneurship, strategy, leadership, ecosystems, and technology accelerators. This online course is designed for student-centered learning and integrates personalization, challenge-based learning teams that develop a meaningful value-added prototype, and student produced evidence-based research mini-documentary videos.

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The University of Cincinnati is one of the first institutions to offer online courses. Innovation in education is at the forefront of what we do. We have expanded the convenience and quality of our online learning to online degree programs. Today, we offer nearly 100 degrees from undergraduate to doctoral programs.

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