Bruno Giuseppe Rüttimann: Catalogue data in Spring Semester 2021 |
| Name | Mr Bruno Giuseppe Rüttimann |
| URL | http://www.brunoruettimann.de |
| Department | Mechanical and Process Engineering |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 151-0034-10L | Engineering Tool: Introduction to Design of Experiments (DOE)  The Engineering Tools courses are for MAVT Bachelor’s degree students only. Number of participants limited to 36. | 0.4 credits | 1K | B. G. Rüttimann | |
| Abstract | The course introduces to linear and non-linear modelling of processes via "Design of Experiments". DOE is an actively generated regression analysis for fast and economic determination of input parameters to achieve an optimal output with a reduced number of experiments. | ||||
| Learning objective | The students gain insight into theory and practice of DOE. They learn the most important terms, DOE types, full and fractional-factorial modelling and what has to be respected during the factor selection and investigational procedure, everything enriched by a practical exercise. The course provides indispensable basic knowledge for target-oriented scientific experimentation. | ||||
| Content | 1. Einführung - T&E, OFAT, DOE, Vorteile von DOE - Auffrischung Multiple Regression - Multiple Regression vs DOE - DOE Typen: Screening, Refining, Optimizing 2. Theoretische Grundlagen - Vertiefung refining DOE - Voll-, teilfaktorielle DOE, confounding - Design generator, design resolution, factor levels, blocking - Beta-Risiko, Power, Replicates, Repeats, Mid-Points, Lack-of-fit 3. Versuchsplanung und -durchführung, Resultatanalyse - CNX Variablen - Experiment set-up mittels Software - Main effects, interaction plots - Modellreduzierung, Residualanalyse - Response optimizer - Einblick in die nicht-lineare Modellierung 4. Praktische Übung "Katapultschiessen" - Prozessverständnis - Versuchsdurchführung - Auswertung, Modellbildung, Wettbewerb | ||||
| Lecture notes | wird bereitgestellt und kann von den Kursteilnehmer heruntergeladen werden | ||||
| Prerequisites / Notice | Voraussetzung für die Kursteilnahme: Studenten des Maschinenbaus, der Betriebswirtschaft o.ä.; Kenntnisse der Statistikgrundlagen sind von Vorteil aber nicht zwingend (kurze Einführung in die inferentielle Statistik und multiple Regression wird vermittelt) | ||||