Ogni modulo equivale a 3 crediti ECTS. È possibile scegliere un totale di 10 moduli/30 ECTS nelle seguenti categorie:
- 12-15 crediti ECTS in moduli tecnico-scientifici (TSM)
I moduli TSM trasmettono competenze tecniche specifiche del profilo e si integrano ai moduli di approfondimento decentralizzati. - 9-12 crediti ECTS in basi teoriche ampliate (FTP)
I moduli FTP trattano principalmente basi teoriche come la matematica, la fisica, la teoria dell’informazione, la chimica ecc. I moduli ampliano la competenza scientifica dello studente e contribuiscono a creare un importante sinergia tra i concetti astratti e l’applicazione fondamentale per l’innovazione - 6-9 crediti ECTS in moduli di contesto (CM)
I moduli CM trasmettono competenze supplementari in settori quali gestione delle tecnologie, economia aziendale, comunicazione, gestione dei progetti, diritto dei brevetti, diritto contrattuale ecc.
La descrizione del modulo (scarica il pdf) riporta le informazioni linguistiche per ogni modulo, suddivise nelle seguenti categorie:
- Insegnamento
- Documentazione
- Esame
Introduction:
- Introduction to Quality Control
- Statistics refresher: Probability distributions, sampling distributions, inference
- Overview of the most commonly used problem-solving tools: histogram, plots, Pareto chart
Problem-solving methodology:
- Quality management approaches: QRQC, A3, 8D, DMAIC, etc.
- Statistical analysis: t-tests, confidence intervals
Introduction to Statistical Process Control :
- Customer and supplier risk
- Acceptable Quality Level
- Acceptance Sampling plans
- Control charts: variable, attribute, multivariate
- Analysis of Variance (ANOVA)
- Process and Measurement System Capabilities
- Gage repeatability and reproducibility, GR&R studies
Design of experiments:
- Process optimization with designed experiments
- Factorial and fractional experimental designs
- Surface response methodology
Requisiti
Prior to joining the module, the students should be familiar with the basics of statistics (variance, standard deviation, probability density). The student should also understand the concepts of measurement uncertainty, repeatability and reproducibility.
Knowledge of design/mechanical drawing reading/tolerancing
Obiettivi di apprendimento
At the end of the module, the students should
- Understand the quality management approach
- Understand the functioning and apply the principles of statistical process control
- Be able to estimate the capability of a measuring device for the quality control
- Be able to make a conformity decision
- Be able to evaluate the resulting customer/supplier risks
- Be able to set up a sampling plan for a given Acceptable Quality Level
- Know the most commonly used SPC methods and understand their main limitations.
- Understand the different methods for problem solving
- Be able to setup and analyze a designed experiment for process improvement.
- Understand the main quality wordings.
Contenuti del modulo
- Introduction to quality management: 10%
- Problem solving methodology: 20%
- Introduction to Statistical Process Control: 20%
- Incoming quality control: 10%
- Metrologic performance and measurement capability: 10%
- Design of Experiments: 20%
- Examples and case studies: 10%
Metodologie di insegnamento e apprendimento
- Lectures
- Exercises
- Case studies
- Self-study of SPC methodologies
Bibliografia
D. Montgomery, "Introduction to Statistical Quality Control", 8th ed., Wiley & Sons (2020)
D. Montgomery, "Design and Analysis of Experiments", 10th ed., Wiley & Sons (2019)
Quality Trainer, qualitytrainer.minitab.com, Minitab
E.L. Cano, J.M. Moguerza and A. Redchuk. "Six Sigma with R. Statistical Engineering for Process Improvement", UseR ! series. Springer, (2012)
M. Pillet, « Six Sigma: Comment l'appliquer » , Ed. Eyrolles (2013)
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