MSE Master of Science in Engineering

The Swiss engineering master's degree


Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories: 

  • 12-15 ECTS in technical scientific modules (TSM)
    TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
  • 9-12 ECTS in fundamental theoretical principles modules (FTP)
    FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
  • 6-9 ECTS in context modules (CM)
    CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.

In the module description (download pdf) you find the entire language information per module divided into the following categories:

  • instruction
  • documentation
  • examination 
Quality Control (TSM_QCheck)

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

Prerequisites

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

Learning Objectives

 

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.

Contents of Module

 

  • 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% 

 

Teaching and Learning Methods

 

  • Lectures
  • Exercises
  • Case studies
  • Self-study of SPC methodologies


Literature

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)

Download full module description

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