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 over 10 courses.
Examines the processes and the techniques used to ensure quality of an item, a system, a process or an engineering endeavor. The topics of total quality management, statistical process control and quality systems are explored. Also, the historical development and current trends in quality are examined.
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.
Industry 4.0 (I4.0) is characterized by: Increased automation, bridging of the physical and digital world through cyber-physical systems, Industrial IoT, access to data and use of that data to drive decisions and AI and machine learning to improve processes. Industry 5.0 is not another revolution but a complementary approach that includes a focus on implementing strategies that enable sustainability and resilience. Additionally, the call to elevate people and culture has become a focal point for organizations and policymakers alike. Implementing Industry 4.0 technologies and processes is a daunting task, and many businesses can get stuck due to the overwhelming nature of the required expertise and the fear of failure. Effective implementation requires not only technical expertise but also a reinvention of leadership models and company culture.
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.
Yes. The vast majority of our students work throughout their time in their academic program. It is important to assess course load and financial aid to understand how to balance school and work.
If possible, students may cut down on their work hours during a clinical portion of a program.
Most of our programs do not require onsite visits, but there is one exception. If you are interested in pursuing the Master of Science in Nursing-Nurse Midwifery, you will have 1 skills intensives that takes place on campus.
Yes. Many of our students qualify for some type of financial aid.
Sources of aid:
Additional resources to support you from start to finish.
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