Courses

Introduction to Biomaterials

Major topics include information on the structure and organization of hard tissues (bone, cartilage, ligament) and soft tissues (muscle, skin etc.), information about the function and organization of organs such as the heart or the eye, as well as a description of the main materials that are generally used in the biomedical industry in relationship to implants (metals, metal alloys, ceramics, polymers). Issues related to biocompatibility, carcinogenesis, the complement, or drug delivery systems will also be covered.

Materials Processing in Manufacturing

A review will be presented of basic probability theory and statistical analysis, with particular emphasis on terms and definitions of a microstructure. The properties accessible to quantification, the basic stereological relationships and the mathematical foundations, and the microstructural tools needed to quantify the structure will be emphasized. The last one-third of the course will cover applications of quantitative metallography to problems in failure analysis, solidification, heat treatment, phase equilibria, and deformation behavior.

Additive Manufacturing of Materials

The course will take a materials science and engineering approach to additive manufacturing (AM), following the structure of the general materials processing series (MSE 512 Powder Processing, MSE536 Solidification Processing and MSE 548 Deposition Processing) taught in the School of Materials Engineering. The overarching goal is to learn how microstructure development is controlled by the interaction of physical, chemical, thermal, and mechanical phenomena in the shaping of materials by additive processing. All major classes of materials and AM processes will be included. Other objectives are to develop the ability to quantitatively analyze the capabilities and limitations of AM process relative to current commercial processes; and critically analyze the AM research literature. The course will also provide opportunities for students to explore AM topic area(s) of their own interest.

Lean Manufacturing

Lean Manufacturing is about creating value. The Lean process starts with creating value for the ultimate customer which requires providing the right product at the right time for the specified price. While all manufacturing attempts to do this, what makes Lean Manufacturing distinct is the relentless pursuit and elimination of waste. Students will learn the concepts and tools of Lean which include types of waste, visual management, value stream analysis, flow, Just in Time, pull, and Kaizen.

Mechanical Properties and Behaviors of Polymers

This course will focus on the mechanical properties and behaviors of polymeric materials. The course will utilize fundamental solid and fluid mechanics to understand the response of bulk polymers (solid and liquid, above and below Tg). The impact of deformation rate and temperature on the mechanical response of polymers will be covered in detail. The course will start with an overview of linear elastic mechanics, move to rubber elasticity, and then viscoelasticity (concentrating on time-temperature superposition). We will also cover fluid dynamics and the rheology of non-Newtonian fluids. We will conclude with a section on deformation, yield, and fracture mechanisms (focusing on those phenomena that are unique to polymers such as rubber cavitation and crazing). Time permitting, we will turn to a brief discussion of filled polymer systems (polymer matrix composites).

Steel and Aluminum Alloys

Steel and aluminum processing will be studied to understand fundamentals such as the impact of impurities and phase transformations. The study of processing will provide an understanding of how the final properties are influenced by the sequence of processes from the extraction of metal from ore, through casting, hot deformation and heat treating. This understanding will enable the student to go beyond comparisons of standard handbook values and to recognize and understand how the fundamental metallurgical phenomena lead to different performance among the various steels and aluminum alloys. By examining the relationships among processes, microstructure, and properties, the course will provide the "know-how" for better design with steel, aluminum and competing materials.

Superalloys - High Temperature

Superalloys are Fe-, Ni-, and Co-based alloys possessing an exceptional balance of properties that typically include high-temperature strength, oxidation/corrosion resistance, toughness, and microstructure stability. These alloys are used in industries ranging from chemical processing, to nuclear power, to aerospace. This course will cover the fundamentals of the physical metallurgy, processing routes and manufacturing, high temperature deformation mechanisms, and corrosion/oxidation mechanisms related to superalloys. Basic principles of lifing and identification of failure mechanisms of superalloy components will be covered. Material design and selection strategies for practical industrial applications will be presented. Beyond superalloys, we will study emerging structural alloys that include ultra-high-temperature refractory silicides and borosilicides, refractory alloys, and intermetallics.

Materials Engineering Fundamentals

Fundamental relationships between the internal structure, properties and processing in all classes of engineering materials. Comprehensive coverage spanning physical, chemical, thermal, mechanical, electrical, magnetic, and optical responses. The course is intended for materials researchers from all backgrounds, as well as engineers working in product design, development and manufacturing who seek a deeper understanding of the full spectrum of engineering materials.

Nuclear Engineering Principles

A first course for graduate students desiring a nuclear engineering sequence and an elective for students in science or engineering. The course is structured in four parts: (1) Nuclear structure and radiation, biological effects and medical applications of radiation. (2) Basics of neutron and reactor physics, neutron diffusion and reactor criticality. (3) Nuclear materials and waste. (4) Reactor systems and safety.

Nuclear Engineering Systems

A second course for graduate students desiring a nuclear engineering sequence and an elective for students in science or engineering. Principles and practice of nuclear power plant systems with design applications, reactor kinetics, reactor control, radiation protection, shielding, nuclear fuels, fuel cycles, waste management, thermal cycles, heat transport, thermal hydraulics, reactor accidents, and safety analysis

Nuclear Reactor Theory I

Methodologies of neutron flux calculations, diffusion and slowing down theory, flux separation, material buckling, resonance absorption, Doppler effect, 2-group and multi-group theories, and reactivity balances for design and operation. Introduction to reactor kinetics, delayed neutrons, point reactor kinetics, transient behavior, load changes, reactivity feedback, and safety implications.

Mass, Momentum, And Energy Transfer In Energy Systems

Overview of reactor systems, reactor thermal-hydraulics fundamentals, single-phase flow and two-phase flow formulations, basics on single-phase laminar and turbulent flows, one-dimensional analysis of reactor systems, mixture and two-fluid formulations, basic two-phase flow phenomena pertinent to reactor thermal-hydraulics.

Fuzzy Approaches In Engineering

Neural Computing in Engineering

The course presents the mathematical fundamentals of computing with neural networks and a survey of engineering applications. Computational metaphors from biological neurons serve as the basis for artificial neural networks modeling complex, non-linear and ill-posed problems. Applications emphasize the engineering utilization of neural computing to diagnostics, control, safety and decision-making problems.

Statistical Methods

Descriptive statistics; elementary probability; sampling distributions; inference, testing hypotheses, and estimation; normal, binomial, Poisson, hypergeometric distributions; one-way analysis of variance; contingency tables; regression.

Applied Regression Analysis

This is an applied course in linear regression and analysis of variance (ANOVA). Topics include statistical inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data. We will also cover one-way and two-way analysis of variance, multiple comparisons, fixed and random factors, and analysis of covariance. This is not an advanced math course, but covers a large volume of material. Requires calculus, and simple matrix algebra is helpful. We will focus on the use of, and output from, the SAS statistical software package but any statistical software can be0 used on homeworks.

Statistical Quality Control

** This is the same course as IE53000 Quality Control ** The course will comprise a balanced blend of the statistical quality control concepts and hands-on training in the methods, standards and guidelines currently being used for industrial quality control. The course will not assume any prior knowledge other than previous exposure to elementary probability theory; the discussion will be self-contained and all of the topics will be developed from the fundamentals. The course will enable a practising engineer to gain a firm grasp of statistical quality control methods and enable him/her to not only analyze and improve existing quality control processes, but also design and implement new quality control processes in industrial settings.

Design of Experiments

A thorough and practical course in design and analysis of experiments for experimental workers and applied statisticians. SAS statistical software is used for analysis. Taken by graduate students from many fields.

Basic Probability and Applications

By the end of this course, students will be able to understand probability measure, random variables, and their distribution functions, master many of the distribution finding techniques, such as transformation methods, know a lot of special distributions such as Binomial, Poisson, normal, and understand order statistics and the law of large numbers and the central limit theorem.

Statistical Inference

A basic estimation including unbiased, maximum likelihood and moment estimation; testing hypotheses for standard distributions and contingency tables; confidence intervals and regions; introduction to nonparametric tests and linear regression.