| Course Name |
(中) 計算材料科學(4140) |
| (Eng.) Computational Materials Science |
| Offering Dept |
Department of Materials Science and Engineering |
| Course Type |
Elective |
Credits |
3 |
Teacher |
Chya-Yan Liaw |
| Department |
Department of Materials Science and Engineering/Undergraduate |
Language |
English |
Semester |
2026-SPRING |
| Course Description |
This course incorporates the Engineering Design Process (EDP) and statistical thinking into engineering education to cultivate students’ systematic problem-solving abilities. Students will learn how to define engineering problems, identify possible causes, formulate hypotheses, and develop solution strategies through experimentation and data analysis. The course also introduces fundamental statistics and Design of Experiment (DoE) concepts using JMP software (developed by SAS), allowing students to understand efficient and systematic approaches to experimental planning and optimization. Through interdisciplinary learning and hands-on practice, students will develop skills in experiment planning, data interpretation, engineering decision-making, and prototype development, which are essential for future research, interdisciplinary projects, and industrial applications.
本課程導入工程設計流程（Engineering Design Process, EDP）概念，培養學生在面對工程問題時之系統性思考能力。學生將學習如何進行問題定義、分析可能原因、建立假設、規劃解決策略，並透過實驗與數據分析驗證結果，建立工程問題分析與決策能力。此外，本課程將統計思維融入工程教育，並使用 JMP 軟體（由 SAS 公司開發之統計分析軟體）進行數據分析與實驗設計。學生將學習基礎統計概念與實驗設計（Design of Experiment, DoE）方法，了解 DoE 為何是一種高效率且具系統性的實驗策略，並接觸不同類型實驗設計之原理與應用。完成課程後，學生將具備實驗規劃、數據分析與結果驗證能力，並能應用於未來研究、跨領域專題與產業實務中。 |
| Prerequisites |
|
self-directed learning in the course |
Y |
| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
|
|
|
| topic Discussion/Production |
| Networking/Distance Education |
| Discussion |
| Lecturing |
|
| Written Presentation |
| Oral Presentation |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Introduction to Engineering Design Process (EDP) and Design of Experiment (DoE) |
| Week 2 |
Selecting Response, Factors, and Level |
| Week 3 |
How Does DOE Design Experiments? |
| Week 4 |
Overall Steps in Conducting DoE |
| Week 5 |
Understanding Basic Statistics – Part 1
|
| Week 6 |
Understanding Basic Statistics – Part 2 |
| Week 7 |
Understanding Basic Statistics – Part 3
|
| Week 8 |
Understanding Basic Statistics – Part 4
|
| Week 9 |
Midterm |
| Week 10 |
Understanding Statistics in DoE |
| Week 11 |
Evaluating DoE Designs |
| Week 12 |
Full Factorial Design |
| Week 13 |
Screening Design |
| Week 14 |
Response Surface Methodology (RSM) |
| Week 15 |
Final Presentation |
| Week 16 |
Final Presentaiton |
self-directed
learning |
02.Viewing multimedia materials related to industry and academia.
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
Mid-term exam 40%
Final presentation 40%
Participation 20%
|
| Textbook & other References |
Douglas C. Montgomery, Design and Analysis of Experiments, Wiley, 10th edition. (The electronic version can be accessed through the university library, 電子版本可以從學校圖書館取得)
|
| Teaching Aids & Teacher's Website |
|
| Office Hours |
|
| Sustainable Development Goals, SDGs(Link URL) |
| 04.Quality Education 05.Gender Equality 08.Decent Work and Economic Growth 09.Industry, Innovation and Infrastructure | include experience courses：N |
|