logo-polimi
Loading...
Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2017/2018
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 096086 - QUALITY ENGINEERING
Docente Colosimo Bianca Maria
Cfu 10.00 Tipo insegnamento Monodisciplinare

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - BV (477) ENERGY ENGINEERING - INGEGNERIA ENERGETICA*AZZZZ096086 - QUALITY ENGINEERING
Ing Ind - Inf (Mag.)(ord. 270) - BV (479) MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE*AZZZZ096086 - QUALITY ENGINEERING
Ing Ind - Inf (Mag.)(ord. 270) - BV (483) MECHANICAL ENGINEERING - INGEGNERIA MECCANICA*AZZZZ051131 - QUALITY ENGINEERING ME

Programma dettagliato e risultati di apprendimento attesi

Quality Engineering (10 CFU)

 

Course objectives and description:

The course focuses on quantitative methods for modeling, monitoring and improving quality of products and services in actual productive scenarios, characterized by multi-dimensional quality indicators. Techniques for both continuous and ordinal quality data (e.g., survey data) are presented.

After successfully completing the course, students should be able to do the following:

1. Understand the philosophy and basic concepts of quality monitoring and improvement.

2. Extract relevant information from complex, high-dimensional quality data

3. Identify models to predict the expected pattern of quality indicators

4. Design and use appropriate tools to design and manage quality in industrial and service scenarios.

 

 

Course content:

  1. Quality engineering for product and services: the Six-Sigma roadmap and related tools. Statistical Process Control (SPC) or Statistical Quality Monitoring: main assumptions and limits.
  1. Quality modeling
  • Standard assumptions (i.e., normality, independence) and related tests.
  • Modeling patterns via linear regression
  • Modeling autocorrelation via ARIMA models
  • Modeling multivariate data and summarize related information via principal component analysis
  • Modeling survey data: Categorical and ordinal data
  1. Quality monitoring of continuous variables
  • Traditional control charts for the mean and the variance
  • Control charts for autocorrelated data: Problems of traditional control charts for autocorrelated data; Model based and model-free approaches for quality control of autocorrelated data.
  • Design and managing multivariate control chart for the mean and the variance.
  1. Quality monitoring of discrete, categorical and ordinal data
  • Service Quality and customer satisfaction
  • Binomial and multinomial control charts
  • Control charts based on Logistic and ordinal regression
  1. Toward zero-defect manufacturing: process quality and product specifications. Capability analysis. Univariate and multivariate control charts for small shifts (EWMA, CUSUM) .
  1. Acceptance Sampling and Quality control – Lot-by-lot acceptance plans for univariate and multivariate data
  1. Quality Improvement – The role of improvement in the six-sigma roadmap. Quality improvement via empirical model building (hints).

 

Course activity:

-          60% Lectures

-          40% recitations (esercitazioni) using realistic data set and specific sw (Minitab, Matlab)

-          Team Project (optional)– students will be required to develop their own strategy for quality modeling, monitoring or improvement dealing with reference to a real case study.

 

  

Textbooks:

  • L. C. Alwan “Statistical Process Analysis” – Irwin Mc Graw Hill
  • D.C. Montgomery: Introduction to Statistical Quality Control - Wiley
  • E. del Castillo: “Statistical Process Adjustment for Quality Control” – Wiley
  • A. Agresti: “Analysis of Ordinal Categorical Data” - Wiley Series in Probability and Statistics

Additional scientific material will be provided

  


Note Sulla Modalità di valutazione

Exam:

  • Written exam using appropriate sw (minitab, matlab) - mark: 30/30
  • Project discussion (only if written exam >18 - required only if the team project has been selected): maximum increase of the final grade 3/30
  • Oral exam – optional (only if written exam >18): maximum increase of the final grade 3/30

Bibliografia
Risorsa bibliografica facoltativaL. C. Alwan, "Statistical Process Analysis" , Editore: Irwin Mc Graw Hill
Risorsa bibliografica facoltativaD.C. Montgomery, "Introduction to Statistical Quality Control", Editore: Wiley
Risorsa bibliografica facoltativaE. del Castillo:, "Statistical Process Adjustment for Quality Control" , Editore: Wiley
Risorsa bibliografica facoltativaA. Agresti, "Analysis of Ordinal Categorical Data" , Editore: Wiley

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
60.0
esercitazione
40.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
14/12/2019