DoE (Design of Experiments)

Course objectives

DoE is an intelligent, structured and efficient methodology for designing and implementing experimental plans and analysing experimental results. The course covers the basics of designing experiments to examine the factors affecting the overall variability in the process and to evaluate the data obtained from these experiments. The objective is to identify those components of variability that are most significant. The basic models of experiments and their corresponding evaluation procedures based on analysis of variance (ANOVA) will be presented. Individual models will be demonstrated on specific examples, so that participants can use these model examples in their own practice.

Course content

• Introduction to DoE
• Basic concepts of experimental design, problem formulation, general linear model, analysis of variance, least squares method
• Classical experimental strategies
• The principles of DoE
• Understanding experimental analysis - main and interactive effects, experimental error, graphs with normal probability, residual analysis
• Identification of the variables that have the greatest impact on product quality
• One-factor proposals, two-factor, repetitive, non-repetitive factors, general factor proposals, random blocks
• Partial factor proposals, 2k and 3k proposals, 2k-p, 3k-p proposals, creation of these proposals
• Analysis of optimization proposals: main effects, interactions, square effects, response area ANOVA
• Practical examples and DoE training

Designed for

• Process Engineer
• Production Manager

Additional information

* UTC +1 time zone (DE, BE, DK, NL, NO, SE, AT, ES, FR, IT, SI, SK, CZ, PL, HU, ...)