MF2024 Robust and Probabilistic Design 6.0 credits

Robust konstruktion

Probabilistic design is an engineering design methodology with the aim to produce high-quality products, by systematically studying the effects of variations in the design parameters on product performance. Robust design is a methodology for optimising this quality by making the performance of the product insensitive to variations in the manufacturing, material, operational, and environmental properties.

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Course information

Content and learning outcomes

Course contents *

Engineering statistics; distributions, Normal, exponetial, Weibull, confidence intervals

Design of experiments: physical and simulation experiments, suspended or censured tests

Probabilistic design; Monte-Carlo simulation (Matlab, Ansys) of performance variations caused by variations in design (manufacturing tolerances, material properies, geometric configuration), user (anthropometric data), and environmental parameters (humidity, electromagnetic fields, temperature, dust)

Robust design; minimizing performance variation due to variation in design parameters, human properties and environmental conditions

Intended learning outcomes *

After passong the course, the student should be able to:

• describe the characteristic properies of various design characteristics in statistical terms,

• assess the confidence interval of the assessed reliability of a technical system,

• find the type of probability distribution for a given set of data,

• describe the purpose for, the methodology of, and the output from Design of Experiments,

• define a testplan for a set of physical and numerical experiments,

• describe the purpose and steps for performing a Monte-Carlo simulation.

• use Monte Carlo simulations to analyse how the uncertainties in a models input variables affects the results from the model;

•  describe the purpose of Robust design and how it relates to optimization approaches,

• use the Robust design methodology to minimize the sensitivity of a technical response parameter to variations in a set of component design parameters,

• use the Robust design methodology to minimize the sensitivity of a technical response parameter to variations i a set of technical interaction parameters,

• use the Robust design methodology to minimize the sensitivity of an interactive response parameter to variations in a set of ergonomic parameters.

Course Disposition

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Literature and preparations

Specific prerequisites *

Bachelor of Science degree in Mechanical Engineering or equivalent.

Recommended prerequisites

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Equipment

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Literature

• Published course material

• Magnus Arnere "Statistical Robust Design - An Industrial Perspective", Wiley 2014


• Published course material

• Magnus Arnere "Statistical Robust Design - An Industrial Perspective", Wiley 2014

Examination and completion

Grading scale *

A, B, C, D, E, FX, F

Examination *

  • INL1 - Assignments, 3.0 credits, Grading scale: P, F
  • TEN1 - Examination, 3.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Examiner

Ulf Olofsson

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web MF2024

Offered by

ITM/Machine Design

Main field of study *

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Education cycle *

Second cycle

Add-on studies

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Contact

Ulf Olofsson, ulfo@md.kth.se

Ethical approach *

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.