Design of Experiment

Design of Experiments (DOE) is also referred to as Designed Experiments or Experimental Design – are defined as the systematic procedure carried out under controlled conditions in order to discover an unknown effect, to test or establish a hypothesis, or to illustrate a known effect. It involves determining the relationship between input factors affecting a process and the output of that process. It helps to manage process inputs in order to optimize the output.

A simple example of DOE:

While doing interior design of a new house, the final effect of interior design will depend on various factors such as colour of walls, lights, floors, placements of various objects in the house, sizes and shapes of the objects and many more. Each of these factors will have an impact on the final outcome of interior decoration. While variation in each factor alone can impact, a variation in a combination of these factors at the same time also will impact the final outcome. 

Hence it needs to be studied how each of these factors impact the final outcome, which are the critical factors impacting the most, which are the most important combination of these factors impacting the final outcome significantly.

The interior designer can plan and conduct some experiments. Get to know more about  DOE with our PMP online training.

Basics of DOE

The method was coined by Sir Ronald A. Fisher in the 1920s and 1930s. Design of Experiment is a powerful data collection and analysis tool that can be used in a variety of experimental situations.

 It allows manipulating multiple input factors and determining their effect on a desired output (response). By changing multiple inputs at the same time, DOE helps to identify important interactions that may be missed when experimenting with only one factor at a time. We can investigate all possible combinations (full factorial) or only a portion of the possible combinations (fractional factorial).

A well planned and executed experiment may provide a great deal of information about the effect on a response variable due to one or more factors. Many experiments involve holding certain factors constant and altering the levels of another variable. This "one factor at a time" (OFAT) approach to process knowledge is, however, inefficient when compared with changing multiple factor levels simultaneously.

A well-performed experiment may provide answers to the following such as:

• What are the key factors in a process? (both controllable and uncontrollable)
• At what settings would the process deliver acceptable performance?
• What are the key, main and interaction effects in the process?
• What settings would bring about less variation in the output?

A repetitive approach to gaining knowledge should be taken up, typically involving these consecutive steps:

• A screening design that narrows the field of variables under assessment.
• A “full factorial” design that studies the response of every combination of factors and factor levels, and an attempt to zero in on a region of values where the process is close to optimization.

A basic approach to a Design of Experiment

We need to follow the below steps in sequence for conducting a DOE.

1. Define the problem(s)
2. Determine objective(s)
3. Brainstorm
4. Design experiments 
5. Conduct experiments and collect data
6. Analyse data
7. Interpret results
8. Verify predicted results

Conclusion

DOE has been in use for many years in manufacturing industry. Below are some of the benefits/improvements we can expect from conducting DOEs:

• reduce time to design/develop new products & processes
• improve performance of existing processes
• improve reliability and performance of products
• achieve product & process robustness
• evaluation of materials, design alternatives, setting component & system tolerances, etc.