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?
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