QUALITY ENGINEERING
Anno accademico 2019/2020 - 2° annoCrediti: 9
SSD: ING-IND/16 - TECNOLOGIE E SISTEMI DI LAVORAZIONE
Organizzazione didattica: 225 ore d'impegno totale, 138 di studio individuale, 42 di lezione frontale, 45 di esercitazione
Semestre: 2°
Obiettivi formativi
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 analysis;
- to use the Minitab® software quality tools.
Modalità di svolgimento dell'insegnamento
Learning is based on theory classes and practice sessions. Practice sessions are scheduled once per week and consist of solving numerical problems/case studies.
Prerequisiti richiesti
Passing the course: Programmazione e Controllo della Produzione
|
It is required to have good knowledge about i) the manufacturing systems classification and their performance metrics; ii) basic principles about the probability theory; iii) continuous (normal, exponential) and discrete (poisson) distributions. |
Frequenza lezioni
Class attendance is mandatory.
Contenuti del corso
This course introduces students to quality management principles and quality engineering means commonly used either by industry or service organization to maintain their Quality Management Systems. Particular emphasis is given to statistical tools for monitoring quality data and to pursue continuous improvement.
During the course students are introduced to the Minitab® software suite for smart quality data analysis
Testi di riferimento
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.
Programmazione del corso
| Argomenti | Riferimenti testi | |
|---|---|---|
| 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, Supply Chain Quality Management, Quality Costs. | Text 1: Ch.1, Section 1.1 to 1.4, 1.4.1 (p.21)-1.4.2, 1.4.3, 1.4.4. |
| 2 | 2. QUALITY MANAGEMENT STANDARDS AND METHODS. ISO 9000 quality standards. ISO 9000 and 9001-2015, Fund. Concepts, Structure, Context of the Organiz., Quality policy and objectives, Risk-based thinking. Docum. Information. Nonconform. and defect. products. Certific. and Accredit. Audits. The 6 Sigma Philosophy and Roles1, The DMAIC Process Steps. DFSS, Lean principles and 6 Sigma. Case Studies. Reference files: 1Ch01_SupplMaterial.pdf, QM_Strategies_ISO9000_1920.pdf, ISO_9001_2015_Description.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 end Ch. 2 Text 3(Six Sigma): Ch.8 and Ch.9 (only read.) |
| 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 test. The Normal Approximation to the Binomial. Reference files: Ch03_SupplMaterial.pdf. Suggested exercises: 3.1-3.26, 3.29-3.50. | Text 1, Ch. 1, Text 3, Ch. 8 |
| 4 | 4.1. 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), Confid. Intervals. The p-value approach. Infer. on the Variance of a Norm. Distrib., (chi^2-test). Infer. on a Population Proportion and Confidence Interval. The OC curve for the z-test: Choice of the Sample size. | Text 1: Ch.4, Sections 4.1 to 4.5 |
| 5 | 4.2. STATISTICAL INFERENCE IN QCI. Statistical Infer. for Two Samples. Inference for a Difference in Means, (z-test and t-test), Confidence Intervals. Inference on the Variances of a Normal Distribution, (F-test), Confidence Interval. Comparing two populations3. Inference on Two Population Proportions and Confidence Interval. Infer. on More Than Two Populations: Analysis of Variance (ANOVA). Reference files: Ch04_SupplMaterial.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 |
| 6 | 5. HOW STATISTICAL PROCESS CONTROL WORKS. Chance and Assign. Causes. Stat. Basis of the Control Chart. The Run Length of a control chart4. Choice of Control Limits. Sample Size and Sampling Frequency. 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: Ch05_SupplMaterial.pdf. Suggested exercises: 5.1-5.33. | Text 1, Ch. 5 |
| 7 | 6.1. VARIABLES CONTROL CHARTS. Introduction. Control Charts for Xbar and R. The op. characteristic curve. Computation of the performance for the Xbar control chart. Control Charts for Xbar and S: Construction and Operation of and Charts; the Xbar and R Control Charts with Variable Sample Size. The s^2 control chart. The Shewhart Control Chart for Individual Measurements. Reference files: Ch06_SupplMaterial.pdf. Suggested exercises: 6.1-6.10,6.15-6.61. | Text 1: Ch.6, Sections 6.1 to 6.3.2, 6.4 (to p.262) |
| 8 | 6.2. ATTRIBUTES CONTROL CHARTS. 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 number of nonconforming (np Charts). Computation of type II error probability for the p and np charts. Control Charts for Nonconformities (only c Charts). Procedures with variable sample size. Reference files: Ch07_SupplMaterial.pdf. Suggested exercises: 7.1-7.31,7.36 | Text 1, Ch. 7, Ch.7, Sections 7.1, 7.2, 7.3.1 (to p.314) |
| 9 | 7.1. CAPABILITY ANALYSIS OF PROCESSES. Introduction. Process Capability Ratios: Cp, Cpk, Cpm. Process Capability Analysis with Control Charts. Confidence intervals (only Cp) and tests on process capability ratios. Short term and long term variability. Reference files: Ch08_SupplMaterial.pdf, CapabilityAnalysisWithMINITAB.pdf, Suggested exercises: 8.1-8.16. | Text 1: Ch.8, Sections 8.1, 8.3 to 8.3.5. |
| 10 | 7.2. CAPABILITY ANALYSIS OF MEASUREMENT SYSTEMS. Introduction. Process Capability Ratios: Cp, Cpk, Cpm. Process Capability Analysis with Control Charts. Confidence intervals (only Cp) and tests on process capability ratios*. Short term and long term variability. Measurement system analysis: reference value and resolution; location variation: bias, linearity, stability, testing for location variation. | Text 1: Ch.8, Sections 8.4, 8.6, 8.7.1. Text 4: p.56-69, p.77-92. |
| 11 | 7.3. CAPABILITY ANALYSIS OF MEASUREMENT SYSTEMS. testing for width variation, Gauge R&R study: Xbar and R method with Excel*. Part variation, Total variation, Number of distinct categories. Gauge R&R Analysis with Minitab. Measurement error models | Class Material |
Verifica dell'apprendimento
Modalità di verifica dell'apprendimento
Grading for this course is determined by a written exam consisting of quantitative exercises and open questions.
Esempi di domande e/o esercizi frequenti
Examples of questions and frequent exercises are provided in the studium area of the course