QUALITY ENGINEERINGAcademic Year 2022/2023 - Teacher: GIOVANNI CELANO
Expected Learning Outcomes
At the end of the course the students should be able:
- to know the main quality principles and definitions;
- to join a quality assurance team implementing the ISO 9000 standards;
- to implement a process improvement project according to DMAIC metholodogy and Six Sigma principles;
- to perform a preliminary data analysis aimed at identifying quality issues in manufacturing/service processes;
- to implement statistical process monitoring tools;
- to perform process and measurement system capability analyses;
- to use the Minitab® software quality tools and to know the R software basics.Learning is based on theory classes and practice sessions. Practice sessions are scheduled once per week and consist of solving numerical problems/case studies.
Learning is based on theory classes and practice sessions. Practice sessions are scheduled once per week and consist of solving numerical problems/case studies.
Detailed Course Content
This course introduces students to quality management principles and quality engineering tools commonly used either by industry or service organizations to maintain their Quality Management Systems. Emphasis is given to statistical tools for data monitoring and analysis to guarantee quality control and to achieve continuous improvement.
During the QE course students are introduced to the Minitab® software suite for smart quality data analysis and to the basics of R software environment.
1. D.C. Montgomery, “Statistical Quality Control”, 6th edn or successive, Wiley. MAIN TEXT. The most widely used textbook about SQC in Universities teaching courses on Quality Engineering and in Companies implementing SQC tools.
2. C. Cochran, “ISO 9001: 2015 in plain English”, 2015, Paton Professional, ISBN: 978-1-932828-72-6. A recent guide to the implementation of the new ISO 9000: 2015.
3. K. Magnusson, D. Kroslid, B. Bergman, 2003, “Six Sigma: The Pragmatic Approach, Studentilitteratur, Sweden, ISBN: 9-789-144-028033. A professional text presenting the Six Sigma approach.
4. Q. Brook, “Lean Six Sigma & Minitab”, 4th edn., 2014, OPEX Resources Ltd, ISBN-13: 978-0954681388. A very good text for professional use of Minitab in a lean Six Sigma context.
|1||1. QUALITY IMPROVEMENT IN MODERN BUSINESS ENVIRONMENT. The Meaning of Quality and its Dimensions. Quality Improvement (QI). Quality Engineering Terminology: Quality Characteristics (CTQs). A Brief History of Quality Control and Improvement (QCI)*. Statistical Methods for QCI. Management Aspects of Quality Improvement: Quality Planning, Quality Assurance, Quality Control and Improvement. PDCA cycle. The Link between Quality and Productivity: first-time yield (FTY), first-pass yield (FPY). Supply Chain Quality Management*, Quality Costs*.Reference files: ch01_LinkQualProdExSol.pdf||Text 1:Ch.1, Section 1.1 to 1.3, 1.4.3|
|2||2. ISO 9000 QUALITY STANDARDS AND SIX SIGMA. ISO 9000 quality standards and their evolution. Certification and Accreditation, Audits. The ISO 9000-2015 standard: fundamental concepts, quality management principles, terms and vocabulary. Documented Information. Nonconforming and defective products: rework, repair, scrap. The ISO 9001-2015 standard: Structure, Process approach, PDCA cycle in ISO 9001, Risk-based thinking, Context of the Organization, Quality policy and objectives, Organizational Knowledge. Performance evaluation. Improvement. The Six Sigma Philosophy: The Six Sigma Roles and hierarchy, Meaning of Six Sigma, Short term and long term variability, The DMAIC Process Steps for improvement projects. Lean principles and Six Sigma. Case Studies*.Reference files: QMS_ISO9000.pdf, ISO_9001_2015_Description.pdf, ISO9001_ExampleQualityPolicy.pdf, ISO9001_ExampleQualityObjectives, ISO9001_ImprovementCycle.pdf, QM_SixSigma.pdf, SixSigmaProcessImprovAndCaseStudies.pdf.||Text 2(ISO 9001):Ch.1, Ch.2, Ch.4 (p.53-61), Ch.9 (only reading)Text 1(Six Sigma):Ch. 1, 1.4.1 p.28-to endCh. 2 (only reading)Text 3(Six Sigma):Ch.8Ch.9 (only reading)|
|3||3. STATISTICAL MODELS FOR QCI. Describing variation and data: Histogram, Box Plot and Dot Plot. Discrete distributions: Hypergeometric, Binomial, Poisson Distributions. Continuous distributions: Normal, Lognormal, Exponential Distributions. Probability Plots. The Anderson Darling test3.1. The Normal Approximation to the Binomial. Reference files: 3.1Ch03_SupplMaterial.pdf. Suggested exercises: 3.1-3.26, 3.29-3.50.||Text 1:Ch.3, Sections 3.1.2 to 3.3.3, 3.4, 3.5.3|
|4||4. STATISTICAL INFERENCE IN QCI. Statistics and Sampling Distributions. Sampling from a Normal, Binomial and Poisson Distribution. Point Estimation of Process Parameters. Statistical Inference for a Single Sample. Inference on the Mean of a Normal Distribution, (z-test and t-test), Confidence Intervals. The p-value approach. Inference on the Variance of a Normal Distribution, (c2-test). The OC curve for the z-test: Choice of the Sample Size. Statistical Inference for Two Samples. Inference for a Difference in Means, (z-test and t-test), Confidence Intervals. The paired t-test4.1. Inference on the Variances of a Normal Distribution, (F-test), Confidence Interval. Comparing two populations4.2. Inference on More Than Two Populations: Analysis of Variance (ANOVA). Multiple comparisons: Tukey and LSD test4.3. Reference files: 4.1PairedTtest.pdf, 4.2Ch04_SupplMaterial.pdf, 4.3MultipleComparisonsTests.pdf. Suggested exercises: 4.1-4.21, 4.29, 4.31, 4.33-4.35, 4.37-4.40, 4.43-4.51.||Text 1:Ch.4, Sections 4.1 to 4.5|
|5||5. HOW STATISTICAL PROCESS CONTROL WORKS. Introduction. Chance and Assignable Causes. Statistical Basis of the Control Chart. Choice of Control Limits. Sample Size and Sampling Frequency. The Run Length of a control chart5.1. Rational Subgroups. Analysis of Patterns. Adding Sensitizing Rules to Control Charts. Phase I and II Implementation of Control Charts. The Rest of Magnificent Seven: Pareto Chart, Cause and Effect Diagram. Applications of SPC (reading). Reference files: 5.1Ch05_06_SupplMaterial.pdf. Suggested exercises: 5.1-5.33.||Text 1:Ch.5|
|6||6. VARIABLES AND ATTRIBUTES CONTROL CHARTS. Introduction. Control Charts for and . The operating characteristic curve. Computation of the performance for the control chart6.1. Control Charts for and : Construction and Operation of and Charts; the and Control Charts with Variable Sample Size. The control chart. Shewhart Control Charts for Attributes. Control Charts for Fraction Nonconforming (p Charts). Selection of the sample size for a p control chart. Control Charts for Nonconformities (only c Charts). Procedures with variable sample size. Reference files: 6.1Ch05_06_SupplMaterial.pdf, Suggested exercises: 6.1-6.10,6.15-6.61, 7.1-7.31,7.36-7.60.||Text 1:Ch.6, Sections 6.1 to 6.3.2,6.4 (to p.262)Ch.7, Sections 7.1, 7.2, 7.3.1 (to p.314)|
|7||7. CAPABILITY ANALYSIS OF PROCESS AND MEASUREMENT SYSTEMS. Introduction. Process Capability Ratios: Cp, Cpk. Process Capability Analysis with Control Charts. Confidence intervals (only Cp) and tests on process capability ratios*. Measurement system analysis: reference value and resolution7.1; location variation: bias, linearity, stability7.2, testing for location variation7.3; width variation: precision error, repeatability, reproducibility7.4, testing for width variation, Gauge R&R study: and method with Excel7.5,*. Part variation, Total variation. Gauge R&R Analysis with Minitab. Reference files: Ch08_SupplMaterial.pdf, AIAG_MSA_Reference_Manual_4th_Edition.pdf: 7.1(pp. 45-46), 7.2(pp. 50-54), 7.3(pp. 87-101), 7.4(pp. 54-57), 7.5(pp. 103-123)CapabilityAnalysisWithMINITAB.pdf, GaugeRandR_StudyWithMINITAB.pdf., Suggested exercises: 8.1-8.16, 8.23-8.27.||Text 1:Ch.8, Sections 8.1, 8.3 to 8.3.5, 8.4, 8.6, 8.7.1.Text 4:p.56-69, p.77-92.|