Chaque module vaut 3 ECTS. Vous sélectionnez 10 modules/30 ECTS parmi les catégories suivantes:
- 12-15 crédits ECTS en Modules technico-scientifiques (TSM)
Les modules TSM vous transmettent une compétence technique spécifique à votre orientation et complètent les modules de spécialisation décentralisés. - 9-12 crédits ECTS en Bases théoriques élargies (FTP)
Les modules FTP traitent de bases théoriques telles que les mathématiques élevées, la physique, la théorie de l’information, la chimie, etc., vous permettant d’étendre votre profondeur scientifique abstraite et de contribuer à créer le lien important entre l’abstraction et l’application dans le domaine de l’innovation. - 6-9 crédits ECTS en Modules contextuels (CM)
Les modules CM vous transmettent des compétences supplémentaires dans des domaines tels que la gestion des technologies, la gestion d’entreprise, la communication, la gestion de projets, le droit des brevets et des contrats, etc.
Le descriptif de module (download pdf) contient le détail des langues pour chaque module selon les catégories suivantes:
- leçons
- documentation
- examen
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
Compétences préalables
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
Objectifs d'apprentissage
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.
Contenu des modules
- 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%
Méthodes d'enseignement et d'apprentissage
- Lectures
- Exercises
- Case studies
- Self-study of SPC methodologies
Bibliographie
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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