Course

Course Number:ENGRG 7850
Course Name:Taguchi Method of Designing Experiments (Online)
Course Description:This course will provide experience in applying Taguchi Methods for designing robust products and processes. Taguchi Methods may be considered as "cookbook" approaches to designing and analyzing industrial experiments. Students will learn to plan a project and develop strategies for experiments. Definition of controllable factors, noise factors, responses, and quality characteristics (both dynamic and static) in a project will be discussed. Applications of orthogonal arrays, signal-to-noise ratio, mean-squared deviation, loss function, ANOVA, and related topics will be covered. P: MATH 4030 or MATH 6030 or ENGRG 6050, or consent of instructor.
Prerequisites:
Level:Graduate
Credits:3
Format:Online
Program:Masters of Science in Engineering
Masters of Science in Integrated Supply Chain Management
Masters of Science in Project Management
Registration Instructions

NOTE: The information below is representative of the course and is subject to change. The specific details of the course will be available in the Desire2Learn course instance for the course in which a student registers.

Additional Information

Learning Outcomes
Students will be able to complete successfully all the following robust process and product design activities after completing this course:
    • Define the controllable factors, noise factors, responses, and quality characteristics.
    • Differentiate between offline and online quality engineering methods.
    • Apply orthogonal arrays, signal-to-noise ratio, mean-squared deviation, loss function and ANOVA for data analysis.
    • Utilize Minitab and Excel for statistical analysis, confidence interval estimation, test of hypothesis, ANOVA, and other applications.
    • Distinguish between static and dynamic characteristics. Select the type or model that is appropriate for a specific application.
    • Conduct Six-Sigma quality improvement projects using the above tools.

Unit Descriptions
Unit 1 Overview of Statistics and Quality
In this unit, there are two lessons that will provide you with a basic review of statistics and quality engineering.
    • Lesson 1.1 reviews fundamental statistical concepts including data types, descriptive statistics, and inferential statistics.
    • Lesson 1.2 reviews the basic concepts on quality.
The content in this unit will allow you to be successful in this course as it is the foundation for the advanced statistical Taguchi approach we will use to solve quality problems.
The outcomes of this unit map to the course outcomes:
    1. Utilize Minitab and Excel for statistical analysis, confidence interval estimation, test of hypothesis, ANOVA, and other applications.
    2. Differentiate between offline and online quality engineering methods.
Unit 2 Quality Metrics
In this unit, there are two lessons that provide you with the fundamental metrics used to analyze experimental data using Taguchi’s methods.
    • Lesson 2.1 discusses the concepts on quality loss function.
    • Lesson 2.2 discusses the concepts on signal-to-noise (S/N) ratio.
The outcomes of this unit map to the course outcomes:
    1. Define the controllable factors, noise factors, responses, and quality characteristics
    2. Distinguish between static and dynamic characteristics; select the type or model that is appropriate for a specific application.
Unit 3 Parameter Design Process
The third unit of this course is on parameter design process. In this unit, we discuss the design of experiments, the process to select the quality characteristics, noise levels, and the use of appropriate arithmetic transformations such as S/N ratio to optimize the experiment.
Unit 4 Quality Improvement Project
The objective of this unit is to provide you with a hands-on experience in quality engineering a project within the framework of Six Sigma methodologies. The Design for Six-Sigma methodology is emphasized in this unit, as the Taguchi methods are more relevant to the offline quality. There are five lessons related to the Define, Measure, Analyze, Design, and Verify phases, and the last lesson will emphasize professional report writing.

Grading Criteria
Exams      2
Criteria                                                           Percentage
Assignments (9)                                                    20
Quizzes (7)                                                              10
Discussion (5)                                                        10
Exams (2)                                                                 30
Project (3)                                                               30
Total                                                                        100
Grading Scale:
Grade                    Percentage
A                              92.00-100
A-                            90.00-91.99
B+                            87.00 - 89.99
B                              83.00 - 86.99
B-                            80.00 - 82.99
C+                           77.00 -79.99
C                             73.00 -76.99
C-                           70.00 - 72.99
D                             65.00 - 69.99
F                                0.00 - 64.99
Only the final course grade will be normalized using the class average.

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