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Description: Quality control with statistical principles applied to problems in various production systems, including probability concepts, density and distribution functions, control chart concepts and sampling inspection plans; laboratory exercises for exposure to basic metrology and applied statistics for quality control applications in discrete-item manufacturing systems.
PREREQUISITES
STAT 211 or equivalent
COURSE OBJECTIVES
Introduce the quality control concept and techniques; demonstrate the procedures to implement the quality engineering tools in industrial applications; use laboratory exercises to expose students to basic metrology and applied statistics for quality control applications in discrete-item manufacturing systems.
TEXTBOOK AND ADDITIONAL COURSE MATERIAL
Montgomery, D.C, 2005, Introduction to Statistical Quality Control, 5th edition, John Wiley and Sons
TOPICAL OUTLINE
Concept of quality and quality control; probability distribution and histogram, inference about process quality, type-I error (a-error) and type II error (b-error), design of control chart, average run length for chart performance, control charts for variables, control charts for attributes, and control charts with memory.
CLASS SCHEDULE
Lecture: 104A ZEC, 11:30-12:20 MW
Lab: Session 501 301 ZEC, M: 4:10 - 6:40, Session 502 301 ZEC, W: 4:10 - 6:40PROFESSIONAL COMPONENT
Students are prepared for engineering practice through a curriculum based on knowledge and skills acquired in earlier course work and incorporating engineering standards.
PROGRAM OUTCOMES
A. To demonstrate that graduates have an ability to apply knowledge of mathematics, science, and engineering, students should be able to
determine an appropriate probabilistic model for the data and identify interval, point estimates for key process parameters, and draw conclusions regarding whether point estimates are sufficiently accurate;
given loosely stated objectives for drawing conclusions from the data, define and conduct appropriate statistical hypothesis tests;
conduct data analyses to ascertain whether a process is in a state of statistical control.
B. To demonstrate that graduates have an ability to design and conduct experiments as well as analyze and interpret data, students should be able to
translate imprecise verbal experimental objectives regarding the nature of a process into an appropriate set of hypotheses to be tested;
specify experimental conditions that will allow evidence to be collected regarding the truth of the hypotheses;
postulate and verify a probabilistic model for the experimental data;
conduct a formal statistical hypothesis test and compile and communicate convincing evidence (in statistical, visual, and verbal form) regarding the truth of the hypotheses.
C. To demonstrate that graduates have an ability to design a system, component, or process to meet desired needs, students should be able to
design a data collection and analyses process for understanding key quality-related parameters of an industrial process;
design a data collection and analyses process for comparing key quality-related parameters of two or more industrial processes;
design a process for rational sampling and control charting for the purpose of ascertaining whether an industrial process is in a state of statistical control.
D. To demonstrate that graduates have an ability to function on multidisciplinary teams, students should be able to
coordinate with team members during lab sessions for conducting experiments;
elaborate and interpret experiment results and physical phenomena based on team discussion.
E. To demonstrate that graduates have an ability to identify, formulate, and solve engineering problems, students should be able to
identify and communicate the implications of a process status;
formulate statistical measures related to whether a process satisfies or violates the above conditions;
design and conduct experiments, data collection processes, and statistical data analyses processes for understanding the statistical measures and their inherent uncertainty;
specify precise conditions for when action should be taken to remove assignable causes of variability and bring a process into a state of statistical control;
quantify the impact on process variability of bringing a process into a state of statistical control.
F. to demonstrate that graduates have an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice, students should be able to
all of the above bullets for outcomes (A), (B), (C), and (D);
use statistical software (such as Excel) to facilitate data analyses.
PREPARED BY: Yu Ding DATE: 9-19-03
