ISyE 575: Introduction to Quality Engineering

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ISyE 575 - Introduction to Quality Engineering

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ISyE 575 Introduction to Quality Engineering (Mann) Sections: 303, 302, 301, 001

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ISYE 575: Introduction to Quality Engineering - Fall 2007, Fall 2006 (Steudel)

ISYE 575: Introduction to Quality Engineering, Fall 2005 (Steudel), formerly Course Homepage of Steudel

ISYE 575: Introduction to Quality Engineering, Fall 2005 (Steudel)

ISYE 575: Introduction to Quality Engineering, Fall 2004 (Steudel)

ISYE 575: Introduction to Quality Engineering, Fall 2004 (Steudel)

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Catalog Description

575 Introduction to Quality Engineering. I; 3 cr. Introduction to statistically based quality improvement methods useful in industrial settings; observational methods and design of experiments; experimentation to discover influential factors and to analyze sources of variation; robust products. P: One intro crse in statistical methods, or cons inst.

Course Prerequisite(s)

Stats 312

Prerequisite knowledge and/or skills

A background in statistical methods is required. A formal undergraduate course in engineering statistics is the official prerequisite. Students need to know how to compute and interpret the sample mean and standard deviation, use the normal distribution, test a hypothesis (the t-test, for example), construct and interpret a confidence interval, fit a model using the method of least squares.

Textbook(s) and/or other required material

Textbook: Statistics for Experimenters, by George E. P. Box, W. G. Hunter, and J. S. Hunter (BH2). Published by John Wiley & Sons, New York, 1978.

Course objectives

Understand how to design experiments and analyze the resulting data in various phases of engineering work, including new product design and development, process development, manufacturing process improvement, and health systems design and improvement.
Apply your increased quantitative literacy and new data analysis skills in statistical methodologies, graphical displays, and visual and inferential interpretations to get experimental results faster, easier, and with fewer resources.
Work more effectively in a team-based experiential project focused on applying appropriate statistical tools and techniques within the Six Sigma Breakthrough Strategy.
Understand and describe the role of the indsutrial engineer as an innovator and manager of continuous improvement in complex systems composed of people, information, materials, equipment, and financial resources.
Explain the eight steps of the Six Sigma Breakthrough Strategy and apply these steps to a wide range of actual situations in any organization.

Topics covered

Introduction to Course: Objectives & Policies; Introduction to Six Sigma & Class Exercise
Six Sigma Methodology, Case Study & Workshop
Basic Statistics Review and Foundations
Confidence Intervals and Prediction Intervals
Hypothesis Testing of Means
Hypothesis Testing for Differences Between Population Means (Paired t-test and Independent t-test)
Review Homeworks, Correlated Data, and Discussion of Projects
One-way ANOVA
Decomposition of ANOVA Testing for Model Adequacy using Residuals Analysis
Two-Way ANOVA (Randomized Block Designs)
Three-Way ANOVA (Latin Squares)
Review RBD Homework, Compare t-squared = F
Introduction to Design of Experiments (DOE)
Two Level, Three Factor DOE
ANOVA for 2k Factorial Designs, Matrix Estimation
Two Level, Four Factor DOE
Fractional Factorial Designs
Fractional Factorial Designs Part 2
Nested and Split-Plot Designs
Nested and Split-Plot Designs Part 2
Response Surface Methodology (RSM)
RSM and Discussion of Projects
Project Presentations

Class/laboratory schedule

Class meets twice per week for 1 hour and 15 minutes. Lab meets once per week for 50 minutes. For more detailed information, please point your browser to http://www.engr.wisc.edu/cgi/courses/list/ie/575/nembhard/ and click on "Course Calendar"

Contribution of course to meeting the professional component

This course contributes primarily to the students' knowledge of engineering topics, and does provide design experience.

The following statement indicates which of the following considerations are included in this course: economic, environmental, ethical, political, societal, health and safety, manufacturability, sustainability.

This course considers manufacturability and touches on economic issues.

Relationship of course to undergraduate degree program objectives and outcomes

This course primarily serves students in the department. The information below describes how the course contributes to the undergraduate program objectives.

This course will help students learn to plan, design, conduct and analyze experiments efficiently and effectively. This requires previous knowledge of statistics and also develops new statistical concepts. We use MINITAB statistical software to implement the methods presented and students have opportunities to use it for homework and lab assignments and the term project. This course also stresses the need for and develops skills for lifelong learning. The classes are conducted using cooperative learning lectures which encourage the students to actively engage and analyze new material. Students work in teams and have responsibility for their own and each other's learning.

Assessment of student progress toward course objectives

Grades in the course are be determined by class participation (5%), homework and lab exercises (30%), mid-term exam (20%), last exam (25%), and a term project (20%).
Homework and Lab Exercises - Weekly assignments will be given to reinforce the use of methods introduced in class. Self-study and independent learning is critical for comprehending the material in this course.
Exams - All exams are comprehensive (up to the point of study) and given in class.
Term Project - The term project is performed in teams of up to four people. The project consists of planning, designing, conducting and analyzing an experiment, using appropriate DOE principles. A written project proposal is required, along with a final class presentation and written project report. The major requirement is that the experiment must involve at least three factors. The proposal report requires information about the problem, the factors, the responses that will be observed, and the specific details of the design that will be used.

Person(s) who prepared this description

Harold J. Steudel

Copyright 2006 The Board of Regents of the University of Wisconsin System
Date last modified: Thursday, 21-Aug-2008 14:08:54 CDT
Content by: prpeters@engr.wisc.edu
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