Statistical Quality Control
Learning Objective:To help students understand the concepts underlying statistical quality control and to develop their ability to apply those concepts to the design and management of quality control processes in industries. Major topics include history and overview of the state of the art of quality control methodologies, tools for descriptive and predictive statistical analysis, design and use of various control charts for quality control, process characterization and capability analysis, R&R gauge capability studies, design of experiments, acceptance sampling and continuous improvement. The emphasis will be on ensuring that the students gain both a broad perspective of quality control as well as the technical skills necessary to implement quality control in any industrial setting.
** 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.
Topics Covered:History of quality control, modern quality control philosophy, Design-Measure-Analyze-Improve-Control paradigm, methods for describing variation including histograms, stem-and-leaf plots, box plots, discrete and continuous random variables, probability plots, statistical inference methods, design of control charts for variable and attribute data including X-bar, R, S, CUSUM, MA and EWMA charts, sensitizing rules including Western Electric guidelines, average run length, process characterization and capability analysis, gauge R&R studies, design of experiments with emphasis on factorial design, sampling inspection, attribute and variable acceptance plans, six-sigma and TQM.
Prerequisites:IE 230 and 330 or equivalent courses. Elementary probability theory.
Applied / Theory:80 / 20
Web Content:Syllabus, grades, homework assignments, solutions, chat room, and message board.
Homework:Students will be required to complete seven homework assignments. The worst score among the seven assignments will be disregarded. The aggregate of the six best scores in homework assignments will contribute 40% to the course grade.
Projects:Students will be given the option to complete a final project instead of taking the final exam. Students will be allowed to choose a project of their choice and determine its scope in consultation with the instructor.
Exams:There will be a midterm exam that will contribute 30% to the course grade. The final exam/project will contribute 30% to the course grade.
Textbooks:Montgomery D.C. (2013). Introduction to Statistical Quality Control (7th ed.). John Wiley & Sons, Inc.
(Note: this is the required textbook of this course. Please use the correct edition, which is the basis of the lectures and homework problems.)