Course descriptions are provided for core courses in AM and breadth courses targeting AM.
39-601/24-632 Special Topics: Additive Manufacturing Processing and Product Development
Introduction to additive manufacturing (AM) processing fundamentals and applications using Solidworks 3-D CAD software and a variety of polymer and metal AM machines. Includes a brief history of AM processing, a review of and technical fundamentals of current AM processes, a study of the current AM market, and future directions of the technology. Lab Sessions will support an open-ended product development project. Lectures on metals AM will address current research impacting industry. Students will also perform a literature review of papers on the state of the art. Basic Solidworks knowledge required.
39-602/27-765 Materials Science for Additive Manufacturing
This course will develop the understanding required for materials science and engineering for additive manufacturing. The emphasis will be on powder bed machines for printing metal parts, reflecting the research emphasis at CMU. The full scope of methods in use, however, will also be covered. The topics are intended to enable students to understand which materials are feasible for 3D printing. Accordingly, high power density welding methods such as electron beam and laser welding will be discussed, along with the characteristic defects. Since metal powders are a key input, powder-making methods will be discussed. Components once printed must satisfy various property requirements hence microstructure-property relationships will be discussed because the microstructures that emerge from the inherently high cooling rates differ strongly from conventional materials. Defect structures are important to performance and therefore inspection. Porosity is a particularly important feature of 3D printed metals and its occurrence depends strongly on the input materials and on the processing conditions. The impact of data science on this area offers many possibilities such as the automatic recognition of materials origin and history. Finally, the context for the course will be discussed, i.e. the rapidly growing penetration of the technology and its anticipated impact on manufacturing.
39-603 Additive Manufacturing Laboratory
Hands-on laboratory projects will teach students about all aspects of metals additive manufacturing (AM). Students will learn how to use SOLIDWORKS for part design, create and transfer design files to the AM machines, run the machines to build parts, perform post-processing operations, and characterize AM parts. Student will work in teams and complete three separate lab projects, each utilizing a different material system, part design, AM process/machine, post-processing steps and characterization methods. A major lab report and presentation will be required for each of the three lab projects. The course includes weekly lectures to complement the laboratory component. Prerequisites: 39601/24632 and 39602/27765. Priority for enrollment will be given to students who have declared the Additive Manufacturing Minor.
24-680 Quantitative Entrepreneurship: Analysis for New Technology Commercialization
This course provides engineers with a multidisciplinary mathematical foundation for integrated modeling of engineering design and enterprise planning decisions in an uncertain, competitive market. Topics include economics in product design, manufacturing and operations modeling and accounting, consumer choice modeling, survey design, conjoint analysis, decision-tree analysis, optimization, model integration and interpretation, and professional communication skills. Students will apply theory and methods to a team project for a new product or emerging technology, developing a business plan to defend technical and economic competitiveness. This course assumes fluency with basic calculus, linear algebra, and probability theory. 4 hrs. lecture. Prerequisites: 21-259. Cross-listed as 19-670.
24-787 Artificial Intelligence and Machine Learning for Engineering Design
This course will cover fundamental artificial intelligence and machine learning techniques useful for developing intelligent software tools to support engineering design and other engineering activities. The computational techniques covered include: search, constraint satisfaction, probability, data mining, pattern recognition, neural networks, optimization, and evolutionary computation. The course will examine both the theory behind these techniques and the issues related to their efficient implementation. The application of the techniques to engineering tasks, such as design representation and automation will be explored. In addition to regular homework sets, the course includes individual paper presentations and a substantial term project in which the student will develop an intelligent software tool to support an engineering task. A basic working knowledge of a scientific programming language (C/C++, Java, Matlab) is highly recommended. 4 hrs. lec. Prerequisites: None
27-6XX Informatics and Data Analytics for Materials Engineering [Adapted from the description for 27-566]
Material Science and Engineering has traditionally been taught by emphasizing the development and application of technology. This course will present an alternative approach that combines data mining, data analytics, and material fundamentals (i.e. materials informatics). Students will be introduced to informatics techniques related to data mining and large database analysis. The topics will include Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), Neural Nets, Image Analysis and Support Vector Machines. There will be a project in which students will apply appropriate techniques to a data set of their choosing. Although the examples will be mainly taken from Additive Manufacturing and Material Science and Engineering, the approaches presented are applicable to all areas of Engineering. Pre-requisite: 36-220 Engineering Statistics and Quality Control, or equivalent.
12-709 Data Analytics for Engineered Systems
Use of analytics is rapidly transforming decision making as individuals and firms begin to leverage analytics in various functional areas to improve data-driven planning and decision making. This course will cover the underlying fundamental concepts and principles (qualitative and quantitative) behind data analytics, focusing on those a civil or environmental engineer needs to understand to both envision opportunities for business advantage, and work effectively with data scientists to realize those opportunities. In addition, the course will provide students skill development in the use of data visualization tools and techniques, using Microsoft Excel, coding, and existing software such as Tableau.
27-792 Solidification Processing
The goal of this course is to enable the student to solve practical solidification processing problems through the application of solidification theory. The objectives of this course are to: (1) Develop solidification theory so that the student can understand predict solidification structure; (2) Develop a strong understanding of the role of heat transfer in castings; (3) Develop an appreciation for the strengths and weaknesses of a variety of casting processes. The first half of the course will be theoretical, covering nucleation, growth, instability, solidification microstructure: cells, dendrites, eutectic and peritectic structures, solute redistribution, inclusion formation and separation, defects and heat transfer problems. The second part of the course will be process oriented and will include conventional and near net shape casting, investment casting, rapid solidification and spray casting where the emphasis will be on process design to avoid defects.
24-651 Material Selection for Engineers
This course provides a methodology for selecting materials for a given application. It aims to provide an overview of the different classes of materials (metal, ceramic, glass, polymer, elastomer or hybrid) and their properties including modulus, strength, ductility, toughness, thermal and electrical conductivity, and resistance to corrosion in various environments. Students will also learn how materials are processed and shaped (e.g., injection molding, casting, forging, extrusion, welding, grinding, and polishing), and will explore the origins of the properties, which vary by orders of magnitude. The course accomplishes the materials selection objective in part through example applications and in part through the use of CES EduPack software (a visual way to explore the world of materials and processes). Topics include: Materials selection by stiffness, weight, strength, fracture toughness, corrosion and oxidation, and thermal properties. Materials at high temperatures, materials shaping. Phase diagrams and phase transformations. 4 hours lecture.
42-612, 27-720 Tissue Engineering
This course will train students in advanced cellular and tissue engineering methods that apply physical, mechanical and chemical manipulation of materials in order to direct cell and tissue function. Students will learn the techniques and equipment of bench research including cell culture, immunofluorescent imaging, soft lithography, variable stiffness substrates, application/measurement of forces and other methods. Students will integrate classroom lectures and lab skills by applying the scientific method to develop a unique project while working in a team environment, keeping a detailed lab notebook and meeting mandated milestones. Emphasis will be placed on developing the written and oral communication skills required of the professional scientist. The class will culminate with a poster presentation session based on class projects. Pre-requisite: Knowledge in cell biology and biomaterials, or permission of instructor