Master’s Non-Thesis

Master’s Non-Thesis: Overview

The Advanced Manufacturing Program provides graduates and professional students with the interdisciplinary skills needed to apply cutting-edge manufacturing techniques within a wide range of industries. Throughout the program, students work with state-of-the-art industrial equipment and open-platform fabrication systems with a focus on additive manufacturing.

The Advanced Manufacturing teaching lab is dedicated to the program allowing students to explore various equipment and systems. Within this lab, students have the option to work with polymers, metals, ceramics and biological materials, while optimizing structural design and capturing and interpreting important process data. Additionally, students will have the opportunity to work on industry relevant projects through internships and independent studies.

Master’s Non-Thesis: Outcomes

Upon completion of the Advanced Manufacturing Master’s Non-Thesis, graduates will have the skills to:

  • Design products for additive and advanced manufacturing processes
  • Drive innovation through the effective application of simulation tools for ideation and design verification
  • Leverage advanced manufacturing systems in industry to integrate state-of-the-art technologies within various fields
  • Exploit data and optimization to reveal more and better R&D solutions

Master’s Non-Thesis: Details

  • Earn the Advanced Manufacturing MSNT degree in as little as one academic year (late August–early May)
  • Enroll as a full-time student or as a working professional
  • Apply up to 6 credit hours of undergrad coursework for the MSNT program
  • Work in industry and experience advanced technologies firsthand while obtaining practicum credit
  • Complete coursework on your schedule from anywhere with online course options

CONTACT

To learn more, please contact:

Craig A. Brice
Program Director
craigabrice@mines.edu

Jenifer Blacklock
Assistant Director
jblacklo@mines.edu

AMFG 401/501 INTRODUCTION TO ADDITIVE MANUFACTURING* (3.0 CREDIT HOURS)

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 core courses.

AMFG 511 DATA-DRIVEN MATERIALS MANUFACTURING (3.0 CREDIT HOURS)

In this course, students 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 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 421/521 DESIGN FOR ADDITIVE MANUFACTURING (3.0 CREDIT HOURS)

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:

1. Design: topological optimization, re-parameterization of optimized topology (i.e., voxel to parametric geometry), local shape optimization, complex internal and external lattice layout
2. Production Planning: slice thickness, laser/extruder path, part orientation, support structures       
3. Production Process: machine-specific characteristics, material/heat addition, thermal history, microstructure/phase transformations, multiscale material modeling, distortion
4. Post-processing and In-service: post-production heat treatment, post-production machining, part distortion and how to compensate, in-service structural performance
5. 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).

AMFG 531 MATERIALS FOR ADDITIVE MANUFACTURING (3.0 CREDIT HOURS)

This course covers 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.

 

Graduate elective courses, listed by focus area:

Materials for Additive Manufacturing
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 focus area:

Materials for Additive Manufacturing
MEGN412 Advanced Mechanics of Materials 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
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
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
EEGN411 Digital Signal Processing 3.0
EEGN417 Modern Control Design 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

READY TO APPLY?

Click here to visit Mines graduate admissions.