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
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.