Quality Control and Reliability Engineering
Quality Control and Reliability Engineering Program Area
This area of study prepares you to apply data-driven modeling, simulation, quality control, and reliability techniques for making cost-effective quality improvement and maintenance decisions in the context of a broad range of service and manufacturing enterprises. Develop the skills to cope with uncertainty and variations in the design and operation of all types of engineering systems.
Key Topics: Design of Experiments, Fault Diagnosis, Process Control and Reliability, Prognostics Statistical Monitoring, Quality Control, and Time Series Modeling.
Area Lead: Judy Jin
IOE 466 (MFG 466) Statistical Quality Control
Prerequisites: IOE 366 or Stats 401 (C- or better) or graduate standing. (3 credits)
Quality improvement philosophies; modeling process quality, statistical process control, control charts for variables and attributes, CUSUM and EWMA, short production runs, multivariate quality control, auto correlation, engineering process control, economic design of charts, fill control, pre-control, adaptive schemes, process capability, specifications and tolerances, gage capability studies, acceptance sampling by attributes and variables, and international quality standards.
IOE 561 (ISD 523) Risk Analysis I
Advisory prerequisites: Graduate level introductory probability course or permission of instructor. (3 credits)
This course provides a graduate-level introduction to the interdisciplinary field and methods of risk analysis. The course covers the foundations of the field – the meaning of risk and uncertainty; risk perception, communication, and governance; semi-quantitative risk analysis methods; fault trees and event trees; Bayesian belief networks; probability elicitation. It also covers more domain-specific analysis methods from project risk management; terrorism risk analysis, infrastructure risk analysis, and environmental health and safety risk assessment. The focus is on providing a strong foundation for both further study and practice in the field of risk analysis.
Stats 570 (IOE 570) Experimental Design
Advisory prerequisites: Stats 500 or background in regression. (3 credits)
Basic design principles, review of analysis of variance, block designs, two-level and three-level factorial and fractional factorial experiments, designs with complex aliasing, data analysis techniques, and case studies, basic response surface methodology, variation reduction, and introductory robust parameter designs.
IOE 500 IOE Master’s Seminar
Advisory prerequisites: IOE master’s student or permission of instructor. (1 credit)
Seminars presented by academic speakers and industry leaders to describe contemporary applications of industrial and operations engineering models and techniques to master’s students in IOE. The focus is on applications but research challenges are addressed as needed. Many speakers also address potential career opportunities for MS students in IOE.
Suggested courses to learn more about the quality control and reliability engineering program area
Statistical and machine learning
IOE 551 Benchmarking, Productivity Analysis, and Performance
IOE 561 Risk Analysis I
IOE 565 Time Series Modeling, Analysis, Forecasting
IOE 568 Statistical Learning & Applications in Quality Engineering
IOE 691 IOE 691 Modern Bayesian Data Science
STATS 503 Statistical Learning II: Multivariate Analysis
STATS 600 Linear Models
EECS 545 Machine Learning (CSE) or EECS 553. Machine Learning (ECE)
EECS 505 Computational Data Science and Machine Learning
EECS 556 Image Processing
IOE 510 Linear Programming I
IOE 511 Continuous Optimization Methods
IOE 512 Dynamic Programming
IOE 515 Stochastic Processes I
IOE 516 Stochastic Processes II
IOE 545 Stochastic Networks and Operations
IOE 574 Simulation
IOE 517 Game Theory and Operations Applications
IOE 525 Lean Principles and Scientific Thinking in Organizations
IOE 543 Scheduling
IOE 552 Financial Engineering I
IOE 553 Financial Engineering II