Ing Ind - Inf (Mag.)(ord. 270) - BV (477) ENERGY ENGINEERING - INGEGNERIA ENERGETICA
096086 - QUALITY ENGINEERING
Ing Ind - Inf (Mag.)(ord. 270) - BV (479) MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE
096086 - QUALITY ENGINEERING
Ing Ind - Inf (Mag.)(ord. 270) - BV (483) MECHANICAL ENGINEERING - INGEGNERIA MECCANICA
051131 - QUALITY ENGINEERING ME
Nowadays, an impressive amount of data can be collected in real industrial scenarios (Industry 4.0). The course presents a set of quantitative tools and methods for managing, modeling, monitoring data in industrial and business scenarios. Specific attention is given to quality data, i.e., all the key indicators of products and processes which play a relevant role in creating added value for the company.
After successfully completing the course, students should be able to do the following:
1. Understand the philosophy and basic concepts of quality data modelling, monitoring and improvement.
2. Extract relevant information from complex, high-dimensional data
3. Identify models to predict the expected pattern of quality and performance indicators
4. Design and use appropriate tools to design and manage data in industrial and service scenarios.
The course fits into the overall program curriculum pursuing some of the defined general learning goals. In particular, the course contributes to the development of the following capabilities:
Understand context, functions, processes in a business and industrial environment and the impact of those factors on business performance
Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modeling capability deriving from a solid and rigorous multidisciplinary background) to face problems and opportunities in a business and industrial environment
Develop new ideas and solutions in business and industrial scenarios evolving over time
Risultati di apprendimento attesi
Lectures will provide the basic tools to understand functions and processes, using appropriate quantitative tools for data analysis. Recitations in computer labs will show how the learned tools can be effectively used to design new solutions using a scientific approach to face the problems at hand (applying knowledge and understanding).
The lab project will foster an additional insight to develop new ideas and solutions in business and industrial scenarios (making judgements and learning skills).
Quality engineering & Industry 4.0: data analysis as a basic tool for modeling, monitoring, control and improve.
Approaches for quality data modeling:
Standard assumptions and related tests;
Modeling patterns via linear models;
Modeling autocorrelated data via time series analysis;
Modeling multivariate data and dimensional reduction
Modeling survey data: Categorical and ordinal data
Quality monitoring of continuous variables
Traditional statistical process control (SPC) for the mean and the variance
SPC for autocorrelated data: Problems of traditional control charts for autocorrelated data; Model based and model-free approaches for quality control of autocorrelated data.
Multivariate SPC: Design and managing multivariate control chart for the mean and the variance.
Toward zero-defect manufacturing: process quality and product specifications. Capability analysis. Univariate and multivariate control charts for small shifts (EWMA, CUSUM).
Quality monitoring of discrete, categorical and ordinal data (for management engineering only).
SPC for Binomial and multinomial data
Logistic and ordinal regression
Acceptance Sampling and Quality control – Lot-by-lot acceptance plans for univariate and multivariate data (for management engineering only - hints)
Quality Improvement – The role of improvement in the six-sigma roadmap. Quality improvement via empirical model building (for management engineering only- hints).
A five-credit course in Statistics is required.
Students should mainly know:
- basics of statistical distributions (normal, poisson, binomial. Chi-square, Chi)
- Confidence intervals and Hypothesis tests
A short recap will be provided in the first few classes of the course.
Modalità di valutazione
The written exam is aimed at verifying if the students have suucessfully acquired knowledge of the basic tools to understand functions and processes. One of the test in the written exam is aimed at testing if students are able to use the newly acquired knowldge to design solutions for qualitative data analysis, using the data and information at hand.
The final mark in the written exam can range from "fail" to "30".
An optional (highly suggested) project work is possible. In this case the students will be facing a real industrial problems using complex data sets to be analysed and monitored. The project work allows students to enhance their skills to develop new ideas and solutions in real industrial scenarios.
The oral exam is optional and can be undertaken only if the written exam is sufficient (mark higher than >18). A maximum increase of the final grade of 3/30 can be achieved with the oral exam
L. C. Alwan, "Statistical Process Analysis" , Editore: Irwin Mc Graw Hill
D.C. Montgomery, "Introduction to Statistical Quality Control", Editore: Wiley
E. del Castillo:, "Statistical Process Adjustment for Quality Control" , Editore: Wiley
A. Agresti, "Analysis of Ordinal Categorical Data" , Editore: Wiley
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Tipo Forma Didattica
Ore di attività svolte in aula
Ore di studio autonome
Laboratorio Di Progetto
Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua
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