This subject focuses the student on developing knowledge, know-how, skill and competence in more advanced approaches to Quality & Reliability Engineering. It equips students with the capability to design, select, apply and interpret a variety of statistical techniques for process analysis and testing, undertaking engineering experiments, six-sigma problem solving and reliability analysis. It introduces students to predictive modelling in the context of process optimisation.
1. Process Characterization and Experimental Design: Descriptive Statistics & Graphical Presentation of same, Correlation and Simple Linear Regression, Hypothesis tests applied to Engineering Experiments, ANOVA Analysis, 1FAAT, Full Factorial experiments, Fractional Factorial/Taguchi Orthogonal Arrays, Graphical Analysis of factors, Prediction equations. Practical experiment. The application of Gage R&R Analysis to Measurement Systems, Use of Minitab software
2. Six Sigma Quality: Analysis, Improvement and Control Stages in DMAIC, Cause and Effect Matrices, Control Plans. Six Sigma case studies
3. Reliability Engineering: Reliability Test Planning and Testing, HASS, HALT, FRACAS, Accelerated Life Tests, Sample Size Determination, Test Environments, Environmental Stress Screening: Definition, Types of Stress Screening, Case Histories, Implementing An ESS Program, Weibull Analysis & Probability Plotting, Case Study On Reliability Engineering in Design
4. Quality Assurance Models/Philosophies: The TQM journey, Effective practical quality principles outlined by Juran, Deming and other Quality gurus. TQM case studies
5. Quality Management: Strategic Quality Management, Organization for Quality, Developing a continuous improvement culture, Competitive benchmarking, World-Class methodologies for change management, Balanced Scorecard
6. Validation Overview
7. Introduction to Predictive Modelling: Classification & Regression Technique (CART) & Random Forest
Approaches to Process Optimisation
