Advanced Manufacturing - (2021-2022 Catalog) 1

Graduate Certificate in Additive Advanced Manufacturing (12 credit hours) Manufacturing The Graduate Certificate in Additive Manufacturing provides students the knowledge and skills needed to design, fabricate, and implement engineered components made using additive manufacturing techniques. Degrees Offered The Graduate Certificate in Additive Manufacturing is offered fully online • Graduate Certificate in Smart Manufacturing to accommodate working professionals outside the immediate geographic • Graduate Certificate in Additive Manufacturing area. These courses are also available as elective courses in the current • Master of Science in Advanced Manufacturing (Non-Thesis) Advanced Manufacturing Masters (Non-Thesis) and Graduate Certificate in Smart Manufacturing. Program Description The core courses in the Additive Manufacturing program explore the The Advanced Manufacturing Program provides graduates and process, design, and material aspects of additive manufacturing. professional students with the practical, interdisciplinary skills to apply Students enrolled in the Graduate Certificate program will complete the cutting-edge manufacturing techniques to a wide range of industries, three core courses found below along with an elective course from the including aerospace, biomedical, defense and energy, among others. Advanced Manufacturing list. To achieve the Graduate Certificate, only This program highlights the process, design, materials, data aspects 3 of the 12 required credit hours can be coursework at the 400-level or and operational efficiency aspects of advanced manufacturing with an lower. emphases on additive manufacturing of structural materials and smart Core Requirements manufacturing operations. AMFG401 ADDITIVE MANUFACTURING 3.0 Program Requirements or AMFG501 ADDITIVE MANUFACTURING Graduate Certificate in Smart Manufacturing AMFG521 DESIGN FOR ADDITIVE MANUFACTURING 3.0 AMFG531 MATERIALS FOR ADDITIVE MANUFACTURING 3.0 (12 credit hours) ELECT Select electives from the Advanced Manufacturing 3.0 The Graduate Certificate in Smart Manufacturing provides students with list below the knowledge and skills needed to improve manufacturing operations by Total Semester Hrs 12.0 implementing efficiency and optimization techniques.

The Graduate Certificate in Smart Manufacturing is offered fully online to Master of Science in Advanced accommodate working professionals outside the immediate geographic Manufacturing (Non-Thesis) (30 credit hours) area. These courses are also available as elective courses in the current The Master of Science in Advanced Manufacturing (Non-Thesis) provides Advanced Manufacturing Masters (Non-Thesis) and Graduate Certificate students the opportunity to explore a wide range of manufacturing in Additive Manufacturing programs. technologies and methodologies necessary to fabricate engineered The four-core courses in the Smart Manufacturing program explore products in current and emerging markets. the emerging skillsets of lean manufacturing, life cycle assessment, The core courses in the Advanced Manufacturing program give students and operations research for creating the next generation of optimized a foundation in additive manufacturing along with options to explore manufacturing facilities, saving companies both time and money. topics across various fabrication and manufacturing efficiency areas. Students enrolled in the Graduate Certificate program will complete Students enrolled in the Master of Science in Advanced Manufacturing the three core courses listed below, along with an elective course from (Non-Thesis) program will complete 12 credits of core courses found the Advanced Manufacturing elective list. To achieve the Graduate below along with 18 credits of elective courses from the Advanced Certificate, only 3 of the 12 required credit hours can be coursework at Manufacturing list. The elective list is broken into focus areas to aid the 400-level or lower. students interested in a specific area of advanced manufacturing. Students are not required to select a focus area but can choose electives AMFG422 LEAN MANUFACTURING 3.0 across the entire list. To achieve the Master of Science (Non-Thesis), or AMFG522 LEAN MANUFACTURING only 9 of the 30 required credit hours can be coursework at the 400-level AMFG581/ OPTIMIZATION MODELS IN MANUFACTURING 3.0 or lower. ORWE581 CEEN401/501 LIFE CYCLE ASSESSMENT 3.0 Core Requirements: ELECT Select electives from the list of Advanced 3.0 AMFG401 ADDITIVE MANUFACTURING 3.0 Manufacturing Electives or AMFG501 ADDITIVE MANUFACTURING Total Semester Hrs 12.0 AMFG Electives Select additional AMFG-prefix courses totaling 6 6.0 credits Additional Electives: 2 Advanced Manufacturing - (2021-2022 Catalog)

ELECT Select electives from the Advanced Manufacturing21.0 MEGN481 MACHINE DESIGN 3.0 Up to 6 hours may be replaced with project-based list below ELECT Electives As Approved By Advisor independent study Manufacturing Controls and Data Science Focus Area Total Semester Hrs 30.0 AMFG511 DATA DRIVEN ADVANCED MANUFACTURING 3.0 Mines' Combined Undergraduate / Graduate CSCI507 INTRODUCTION TO COMPUTER VISION 3.0 CSCI534 ROBOT PLANNING AND MANIPULATION 3.0 Degree Program CSCI562 APPLIED ALGORITHMS AND DATA 3.0 Students enrolled in Mines' combined undergraduate/graduate program STRUCTURES (with uninterrupted registration from the time the student earns a Mines CSCI575 ADVANCED MACHINE LEARNING 3.0 undergraduate degree to the time the student begins a Mines graduate CSCI587 CYBER PHYSICAL SYSTEMS SECURITY 3.0 degree) may double count up to six hours of credits which were used in DSCI530/ INTRODUCTION TO STATISTICAL METHODS 3.0 fulfilling the requirements of their undergraduate degree at Mines, toward MATH530 their MS Non-Thesis graduate degree. Mechanical Engineering majors may double count any 400-level or above course on the approved ME EENG509 SPARSE SIGNAL PROCESSING 3.0 Elective list provided in the Undergraduate Catalog. Non-ME majors may EENG515 MATHEMATICAL METHODS FOR SIGNALS AND 3.0 double count any 400-level or above course on the approved ME Elective SYSTEMS list and/or any MEGN 400-level or above course, at the discretion of their EENG517 THEORY AND DESIGN OF ADVANCED 3.0 advisor (MS Non-Thesis) or thesis committee (MS Thesis or PhD). These CONTROL SYSTEMS courses must have been passed with a "B-" or better and meet all other MATH551 COMPUTATIONAL LINEAR ALGEBRA 3.0 University, Department, Division, and Program requirements for graduate MEGN441 INTRODUCTION TO ROBOTICS 3.0 credit. MEGN540 MECHATRONICS 3.0 Advanced Manufacturing Electives MEGN544 ROBOT MECHANICS: KINEMATICS, 3.0 DYNAMICS, AND CONTROL Note that, while the listed electives are grouped into focus areas, students are not required to select a specific focus area for their degree. MEGN545 ADVANCED ROBOT CONTROL 3.0 MEGN587 NONLINEAR OPTIMIZATION 3.0 Materials Manufacturing Focus Area MEGN588 INTEGER OPTIMIZATION 3.0 AMFG531 MATERIALS FOR ADDITIVE MANUFACTURING 3.0 ELECT Electives As Approved By Advisor MEGN511 FATIGUE AND FRACTURE 3.0 Manufacturing Optimization and Business Focus Area MEGN515 COMPUTATIONAL MECHANICS 3.0 AMFG422/522 LEAN MANUFACTURING 3.0 MTGN414 ADVANCED PROCESSING AND SINTERING OF 3.0 AMFG423/523 DESIGN AND ANALYSIS OF EXPERIMENTS 3.0 CERAMICS AMFG581/ OPTIMIZATION MODELS IN MANUFACTURING 3.0 MTGN442 ENGINEERING ALLOYS 3.0 ORWE581 MTGN445 MECHANICAL PROPERTIES OF MATERIALS 3.0 CEEN401/501 LIFE CYCLE ASSESSMENT 3.0 MTGN464 FORGING AND FORMING 2.0 EBGN559 SUPPLY CHAIN MANAGEMENT 3.0 MTGN514 DEFECT CHEMISTRY AND TRANSPORT 3.0 EBGN563 MANAGEMENT OF TECHNOLOGY 3.0 PROCESSES IN CERAMIC SYSTEMS EBGN576 MANAGING AND MARKETING NEW PRODUCT 3.0 MTGN531 THERMODYNAMICS OF METALLURGICAL AND 3.0 DEVELOPMENTS MATERIALS PROCESSING MEGN592 RISK AND RELIABILITY ENGINEERING 3.0 MTGN536 OPTIMIZATION AND CONTROL OF 3.0 ANALYSIS AND DESIGN METALLURGICAL SYSTEMS MTGN557 SOLIDIFICATION 3.0 Courses MTGN560 ANALYSIS OF METALLURGICAL FAILURES 3.0 AMFG501. ADDITIVE MANUFACTURING. 3.0 Semester Hrs. MTGN564 ADVANCED FORGING AND FORMING 3.0 This course gives students a broad understanding of additive MTGN565 MECHANICAL PROPERTIES OF CERAMICS 3.0 manufacturing (AM) techniques (popularly known as 3d printing) and how AND COMPOSITES these techniques are applied to make engineered products. The course ELECT Electives As Approved By Advisor covers the seven standard classifications of AM processes and compares and contrasts each technique alongside legacy fabrication methods such Design Manufacturing Focus Area as milling. Students will also get a high-level view of design, material, and AMFG421/521 DESIGN FOR ADDITIVE MANUFACTURING 3.0 pre/post-processing requirements for AM produced parts along with a AMFG592 ADDITIVE MANUFACTURING BUILD 1.0 fundamental understanding of the cost drivers that make AM competitive PREPARATION over legacy fabrication methods. Prerequisites: MEGN200 and MEGN201 FEGN525 ADVANCED FEA THEORY & PRACTICE 3.0 or equivalent project classes. FEGN526 STATIC AND DYNAMIC APPLICATIONS IN FEA 3.0 FEGN527 NONLINEAR APPLICATIONS IN FEA 3.0 FEGN528 FEA FOR ADVANCED DESIGN APPLICATIONS 3.0 MEGN413 AEROSPACE STRUCTURES 3.0 Advanced Manufacturing - (2021-2022 Catalog) 3

AMFG511. DATA DRIVEN ADVANCED MANUFACTURING. 3.0 AMFG531. MATERIALS FOR ADDITIVE MANUFACTURING. 3.0 Semester Hrs. Semester Hrs. (I) Although focused on materials manufacturing, this course is intended (II) This course will cover various structural materials used in additive for all students interested in experimental design and data informatics. manufacturing (AM) processes. Focus will be on polymer, ceramic, and It will include both directed assignments to reinforce the concepts and metallic compositions. General chemistry of each material will be covered algorithms discussed in class and a term project that will encourage with additional focus on the behavior of these materials when processed students to apply these concepts to a problem of their choosing. Some using AM. The course will span the entire AM lifecycle from feedstock programming background would be beneficial but is not necessary; the fabrication to fabrication by AM to post processing and inspection of basics of python and the sklearn machine learning toolkit will be covered as-fabricated material. Students will have hands-on exposure to AM in the first weeks of the course. 3 hours lecture; 3 semester hours. processes and will conduct laboratory studies of AM material properties. AMFG521. DESIGN FOR ADDITIVE MANUFACTURING. 3.0 Semester Additionally, students will conduct a semester-long research project Hrs. exploring some aspect of AM materials. 3 hours lecture; 3 semester (II) Design for Additive Manufacturing (DAM) introduces common hours. considerations that must be addressed to successfully design or re- AMFG581. OPTIMIZATION MODELS IN MANUFACTURING. 3.0 design parts for additive manufacturing methods. Industry-leading Semester Hrs. hardware and FEA software will be used to explore all phases of the DAM This course explores the process of taking known inputs such as costs, workflow, including topology optimization, additive process simulation, supplies and demands, and determining values for unknown quantities distortion compensation, and in-service performance. 3 hours lecture; 3 (variables) so as to maximize or minimize some goal (objective function) semester hours. while satisfying a variety of restrictions (constraints). Such problems arise AMFG522. LEAN MANUFACTURING. 3.0 Semester Hrs. in manufacturing operations as personnel planning, product sequencing, Throughout the course, students will learn to apply skillsets to real and plant scheduling. We examine a variety of manufacturing settings, world problems, focusing on lean and six-sigma principles and e.g., flow shops, job shops, flexible manufacturing shops, and the methodologies. The course is taught with a focus on the DMAIC structure corresponding appropriate models to optimize operations. The course of implementation (define, measure, analyze, improve and control) for explores a mix of mathematical modeling, software use and case studies. improving and implementing process efficiencies in industry. The course Prerequisite: Junior standing in an engineering major, or instructor is split into three general subject areas; 1) lean manufacturing principles, consent. 2) six-sigma and statistical process control (SPC) methodologies and AMFG592. ADDITIVE MANUFACTURING BUILD PREPARATION. 1.0 3) implementation techniques focusing on graphical and numerical Semester Hr. representation of processes using R. Students will receive an in-depth This course covers practical aspects of additive manufacturing build overview of lean manufacturing principles and will perform case studies preparation, which include designing a part, part build orientation, at local industries to implement learned skill-sets. Next, students will step- and support structures. It distinguishes these concepts from those of through several hands-on activities using real products to investigate six- traditional manufacturing methods and addresses how they influence sigma and perform SPC analysis, identifying shifts in process data and final part outcome in regard to mechanical performance, dimensional learning how to shift processes into capable processes. Lastly, students accuracy, surface finish, and post processing requirements. Similarities will learn about various implementation techniques for industry and will and differences in these concepts are covered as they apply to various perform an in-depth analysis of the course topics based on the industry additive manufacturing technologies. These concepts are integrated to tours performed. ultimately provide students with the ability to holistically approach design AMFG523. DESIGN AND ANALYSIS OF EXPERIMENTS. 3.0 Semester for additive manufacturing. Prerequisite: AMFG401 or AMFG501. Hrs. AMFG598. SPECIAL TOPICS IN ADVANCED MANUFACTURING. 1-6 This course introduces effective experimental design and analysis Semester Hr. methodologies relevant to all engineering and scientific disciplines to (I, II) Pilot course or special topics course. Topics chosen from special maximize the information learned from every experiment (test case) interests of instructor(s) and student(s). Usually the course is offered only while minimizing the total number of tests. We will be using state-of- once. Prerequisite: none. Variable credit; 1 to 6 credit hours. Repeatable art methods steeped in statistics to effectively set up your experiments, for credit under different titles. understand what the results are telling you, and clearly communicate the results to peers and leadership. We apply a disciplined systems Director and Professor of Practice engineering approach across the four major experimental phases: Craig A. Brice plan, design, execute, and analyze. This hands-on class will focus on understanding concepts and practical applications while relying less on the statistical theoretical development. Completion of MATH201 is recommended, not required.