Advanced Manufacturing Degree Options

Masters Non-Thesis Requirements

The Master of Science degree requires a minimum of 30 semester hours of acceptable course work. This includes credit hours of core coursework and 18 credit hours of elective coursework.

Core Courses 12
Electives 18
Total semester Hrs: 30
Professional Graduate Certificate Requirements

The Professional Graduate Certificate will initially be offered on campus, with a plan (AY 2019–2020) to offer the four required core courses online to accommodate working professionals outside the immediate geographic area.

Core Courses 12
Total semester Hrs: 12
Undergraduate Minor and Area of Special Interest Requirements

If you have questions regarding the ASI or Minor options, reach out to the Advanced Manufacturing approved advisor in your department:

Approved Undergraduate Advisors:

Computer Science: Hua Wang Electrical
Engineering: Gongguo Tang
Mechanical Engineering: Anthony Petrella, Aaron Stebner
Metallurgical and Materials Engineering: Jeff King, Zhenzhen Yu
Physics: Charles Durfee

Undergraduate students have the following degree options:

Undergraduate Minor

AMFG401 Intro to Additive Manufacturing 3
Additional Core Course 3
Core or Elective Courses 12
Total semester Hrs: 18







Area of Special Interest

AMFG401 Intro to Additive Manufacturing 3
Core or Elective Courses 9
Total semester Hrs: 12


Additional Program Information

Core Courses

Advanced Manufacturing Core Course List with Descriptions

AMFG 401/501 Introduction to Additive Manufacturing* (previously MEGN 483/583) 3.0

Additive manufacturing (AM), also known as 3D printing in the popular press, is an emerging manufacturing technology that will see widespread adoption across a wide range of industries in the near term. Subtractive manufacturing (SM) technologies (CNCs, drill presses, lathes, etc.) have been an industry mainstay for over 100 years. The transition from SM to AM technologies, the blending of SM and AM technologies, and other developments in the manufacturing world have direct impact on how we design and manufacture products. This course will prepare students for the new design and manufacturing environment that AM is unlocking. The graduate section of this course differs from the undergraduate section in that graduate students perform AM-related research. While students in this course complete quizzes and homework, they do not take a midterm or final exam.

*This course is a co- or prerequisite to the other three core courses.

AMFG 598 Additive Manufacturing of Solid Materials 3.0
This course will cover existing structural materials in use in additive manufacturing (AM) and their forms, the physical models for processing them (both to create feedstock as well as the AM processes themselves), and the strengths and weaknesses of these different materials for AM. The course will end with a project in which students design and demonstrate a new material for AM that solves a major gap in today’s AM materials. These gaps could include chemistry/composition, processing/forms of feedstock materials, post-processing treatments of AM materials, and/or altering/modifying/creating new AM processes for desirable materials.
AMFG 598 Data-Driven Materials Manufacturing 3.0
In this course, students will first review foundational principles of statistical modeling/machine learning, including those needed for Regression, Classification, Data Dimensionality Reduction, Clustering, and Inverse Modeling applications. Additionally, fundamentals of materials manufacturing data management will be taught. With this background at hand, students will then learn how to apply these approaches through three different projects: 1. Accelerate the design of experiments and minimize cost for qualifying a new material manufacturing process. 2. Optimize a material chemistry for a specific manufacturing process. 3. Optimize a manufacturing process for a specific part. Students will use recommended published data sets for two of the projects, and will collect their own data (both in the lab and from the literature) for the third.
AMFG 498/598 Design for Additive Manufacturing 3.0

Most parts originally designed for traditional manufacturing techniques, such as machining, casting, or molding, cannot be successfully transitioned to additive production methods by simply “sending them to the 3D printer.” This course will introduce the most common considerations that must be addressed to successfully design or re-design parts for additive manufacturing methods. Hands-on, collaborative learning experiences, industry-leading software solutions, and emerging hardware platforms will be used to explore the following key topic areas:

a.       Design: topological optimization, re-parameterization of optimized topology (i.e., voxel to parametric geometry), local shape optimization, complex internal and external lattice layout

b.       Production Planning: slice thickness, laser/extruder path, part orientation, support structures

c.       Production Process: machine-specific characteristics, material/heat addition, thermal history, microstructure/phase transformations, multiscale material modeling, distortion

d.       Post-processing and In-service: post-production heat treatment, post-production machining, part distortion and how to compensate, in-service structural performance

e.       Integration: use of probabilistic methods, optimization, or parametric evaluation loops to understand interactions among phases a–d above

AMFG 401/501 Introduction to Additive Manufacturing must be taken as a prerequisite (not as a co-requisite).

Elective Course Options

Graduate elective courses, listed by specialty area:

Additive Manufacturing of Solid Materials
MEGN511 Fatigue and Fracture 3.0
MEGN515 Computational Mechanics 3.0
MEGN598 Modeling Materials Processing 3.0
MLGN505 Mechanical Properties of Materials 3.0
MTGN514 Defect Chemistry and Transport Processes in Ceramic Systems 3.0
MTGN557 Solidification 3.0
MTGN560 Analysis of Metallurgical Failures 3.0
MTGN564 Advanced Forging and Forming 3.0
MTGN565 Mechanical Properties of Ceramics and Composites 3.0
MTGN580 Advanced Welding Metallurgy 3.0
PHGN585 Nonlinear Optics 3.0
Data-Driven Materials Manufacturing
CSCI507 Introduction to Computer Vision 3.0
CSCI508 Advanced Topics in Perception and Computer Vision 3.0
CSCI575 Machine Learning 3.0
EEGN509 Sparse Signal Processing 3.0
EEGN511 Convex Optimization and Its Engineering Applications 3.0
EEGN515 Mathematical Methods for Signals and Systems 3.0
EEGN517 Theory and Design of Advanced Control Systems 3.0
MATH530 Statistical Methods I 3.0
MATH551 Computational Linear Algebra 3.0
MEGN544 Robot Mechanics: Kinematics, Dynamics, and Control 3.0
MEGN545 Advanced Robot Control 3.0
MEGN586 Linear Optimization 3.0
MEGN587 Nonlinear Optimization 3.0
MEGN588 Integer Optimization 3.0
MEGN686 Advanced Linear Optimization 3.0
MEGN688 Advanced Integer Optimization 3.0

Undergraduate elective courses, listed by specialty area:

Additive Manufacturing of Solid Materials
MEGN381 Manufacturing Processes 3.0
MEGN412 Advanced Mechanics of Materials 3.0
MTGN348 Microstructural Development / Laboratory 3.0
MTGN414 Processing of Ceramics 3.0
MTGN442 Engineering Alloys 3.0
MTGN445/445L Mechanical Properties of Materials / Laboratory 3.0
MTGN463 Polymer Engineering 3.0
MTGN464/464L Forging and Forming / Laboratory 3.0
MTGN465 Mechanical Properties of Ceramics 3.0
MTGN475/475L Metallurgy of Welding / Laboratory 3.0
PHGN300 Modern Physics I 3.0
PHGN320 Modern Physics II: Basics of Quantum Mechanics 4.0
PHGN462 Electromagnetic Waves and Optical Physics 3.0
PHGN466 Modern Optical Engineering 3.0
PHGN480 Laser Physics, including lab 3.0
Data-Driven Materials Manufacturing
CSCI303 Introduction to Data Science 3.0
CSCI403 Database Management 3.0
CSCI404 Artificial Intelligence 3.0
CSCI406 Algorithms 3.0
CSCI437 Introduction to Computer Vision 3.0
CSCI470 Introduction to Machine Learning 3.0
EBGN325 Operations Research 3.0
EEGN307 Introduction to Feedback Control Systems 3.0
EEGN310 Information Systems Science I 3.0
EEGN311 Information Systems Science II 3.0
EEGN383 Microcomputer Architecture and Interfacing 4.0
EEGN411 Digital Signal Processing 3.0
EEGN417 Modern Control Design 3.0
MATH334 Intro to Probability 3.0
MATH335 Intro to Mathematical Statistics 3.0
MATH424 Intro to Applied Statistics 3.0
MATH432 Spatial Statistics 3.0
MATH436 Advanced Statistical Modeling 3.0
MEGN441 Introduction to Robotics 3.0
MEGN485 Manufacturing Optimization with Network Models 3.0
MEGN486 Linear Optimization 3.0