B. Course Syllabi for Materials Engineering

1. Course Name: MatE 316 Computational Methods in Materials

2. Catalog Description: MatE 316. Computational Methods in Materials. (2-3) Cr. 3. S. Use of mathematical and statistical computer tools for materials design and analysis. Applications of statistical principles to problems concerned with materials. Computer-assisted design of experiments.

3. Prerequisites: Mat E 211.

3. Textbook/Materials: Introduction to Probability and Statistics for Scientists and Engineers by Walter A. Rosenkrantz. Additional texts and references available in the student room.

5. Course Learning Objectives:
An ability to

• Design experiments that improve the likelihood of statistically significant results that improve the performance of a material or system.
• In teams and individually, conduct experiments, gather data, analyze data, and report, both orally and written, on the experiment.
• Apply mathematical software packages and programming concepts (such as finite differencing and finite elements) to solve materials problems (such as diffusion).
• Use visualization tools in the design, selection, and analysis of materials.
• Perform and interpret the results of linear regressions, including general response surfaces (Kth order polynomials involving N control variables).
• Assess the appropriateness for modeling data with a given probability distribution such as Normal, Lognormal, or Weibull.
• Calculate confidence intervals for sample means and confidence bands for response surfaces.

6. Topics Covered: data analysis, probability, Random Variables, Probability Plots, Central Limit Theorem, confidence intervals, Linear Regression, Multiple Linear Regression, Computerized Design of Experiments (DOE), Quality Control, Mathematical Computations (finite differencing, finite elements, 3D visualization, matrices, tensors).

7. Class/Laboratory Schedule: Class MF 1:10-2:00, Lab W 1:10-4:00

8. Professional Component: Mat E 316 contributes 3 credits toward Engineering Topics and to the professional component of this program through work on materials-based statistics projects that involve Design of Experiments for industrially-motivated experiments.

9. Relationship of Course to Program Learning Outcomes and Program Educational Objectives: Objectives: A, D, E Outcomes: a, b, d, e, g, k, l, o (significant), i (moderate)

10. Prepared by: Larry Genalo, 1/8/00, rev. 5/24/00 KPC