(Course in progress - new lectures added weekly)
Welcome!
What is DOE? And how can I use it to make better pizza!
Quiz 1
Assignment 1
DOE Vs. OFAT.
FREE PREVIEWAssignment 2
Steps to apply DOE
Introduction to the P-Value
What is P-Value ?
Assignment 3
Quiz 2
FREE PREVIEWSummery for Section one
Welcome to Section 2
Defining the factors and the output.
Exercise
Assignment 1
Introduction to the DOE Designs and factor levels
Quiz 1
Quiz 2
Assignment 2
The concept of the full factorial design
Assignment 3
Example (Replication and Center point method)
Quiz 3
Assignment 4 (Bonus)
Summery for section 2
Th Fractional factorial design
The concept of the Fractional factorial design
Quiz 1
Assignment 1
Quiz 2
Fractional factorial design (The Resolution)
Quiz 3
Quiz 4
The trade-off table
Assignment 2
Practical example for the resolution
Quiz 5
Exercise 2
Assignment 3
Optimization methods (Response Surface method) (The Section is in Progress)
Introduction to the response surface methodology (RSM)
FREE PREVIEWCentral Composite Design (CCD)
Quiz 1
Assignment 1
Practical example using CCD
Randomizing & Blocking
Assignment 2
Section 5 is coming soon...
Case Study for Minitab
Download Minitab
Minitab (Part 1)
Minitab (Part 2)
Minitab (Part 3)
course description
The design of experiments (DoE) includes all statistical procedures that should be applied before the start of the experiment. These include:
- Determination of the minimum number of experiments required to comply with precision specifications
- The chosen Values for the different factors
- Take into consideration all the other
- using different designs, like Full factorial plans, fractional plans, and the response surface method
- Sequential experimental planning and evaluation (sequential analysis); here data acquisition and evaluation alternate until a predetermined accuracy is reached
Since experiments require resources (staff, time, equipment, etc. ), the person responsible for the experiment finds himself in a conflict between the accuracy and reliability of his expected results on the one hand, and the necessary effort on the other. Unlike one factor at a time, with the statistical design of experiments, the interaction between influencing factors (= independent variables) and target variables (= dependent variables) is determined as precisely as possible with as few experiments as. This by using different doe designs or design tools, like the full factorial plan, fractional factorial plan, central composite design, response surface method, and a lot of other experiments.
An important part of the statistical design of the experiment is the determination of the experimental scope in relation to precision specifications such as the risks of statistical tests. Also to determine the levels of the factors to get precise results.
The intuitive procedures in experiments, such as changing one factor at a time (one factor at a time) or according to the principle of trial and error (trial and error), only produce an optimal test result by chance. The individual effects and interactions of influencing factors are not recognized.
In contrast, the statistical design of experiments is a method for the systematic planning and statistical evaluation of experiments. The functional relationship between influencing parameters and the results is determined and mathematically described with little effort. The resources required for this, such as personnel, time, and costs, are known and quantifiable before the tests are carried out.
By using the Randomization and Blocking methods, we will be able to reduce the effect of the other factors, that affect our experiments, but we can’t control them.
To get a robust experimental design we use a lot of statical methods like analysis of variance, ANOVA, regression models or equations. These methods are precise and help us to understand the process that we examine and support the examiner with analyzing the data.
To conduct and analyze the data, special software is available to make it easier for statistically less experienced users to carry out planning and evaluation, but this is done at the expense of flexibility. Suitable programs include Design-Expert, GlobalOptimize, Modde, and STAVEX; broader tools with special DoE modules include Cornerstone, JMP, Minitab, STATISTICA, or Visual-XSel, In addition, various simulation packages often contain specially tailored programs or modules for statistical design of experiments.
and prove your skills
All the steps to apply a doe method.
How to choose the suitable factors for doe, and what the factor levels are.
The different doe designs (full factorial design, fractional factorial design, center point method and Central Composite Designs (CCD))
How to calculate the effect of the factors by using simple mathematical equations
Effect diagram, regression equation, alias structure.
What response surface method is, and how we use it to optimize a process.
How to carry out an experiment and what randomization and blocking are.
Some of the statistical basics, for example what P-Value is.
Using Program Minitab (Appling what we learned on a project from real life).
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