The design of experiments (DoE) is an essential tool for optimisation and improvement of processes in various fields. By applying statistical techniques, DoE enables the analysis of multiple variables to understand their influence on the results and thus to improve the quality, accuracy and efficiency of products and processes. This methodology not only facilitates the rigorous evaluation of experiments, but also supports informed decision-making.
We will now explore the key components of the DoE, its practical applications across different industries, and the steps required to develop a effective experimental design.
What is design of experiments (DoE)?
The design of experiments (DoE) It is a methodology that applies statistics to analyse and optimise processes associated with the creation of objects, designs or projects from which a certain level of quality, precision and efficiency is expected. To this end, the following are used: variables of various types and their responses are analysed to identify, in detail, the factors that have the greatest influence on the results.
In this regard, the DoE makes it possible to evaluate experiments on the basis of accurate data, which helps not only to study in detail a a large number of processes, but also to make informed decisions regarding the design of both products and processes. The analyses are, therefore, rigorous, as they require answers on the subject under investigation.
The DoE’s objectives can therefore be summarised as follows:
- Set parameters/conditions for a process
- Identify aspects to optimise resources
- Anticipating the effects regarding some changes to the process
- To reduce any form of variability during the process.
In view of this, experimental design is applied in a wide range of fields where a high level of accuracy. These include, in particular, the pharmaceutical industry, industrial design, consumer goods industry, amongst others.
Components of the experimental design
These components are:
Variable inputs
The input variables or parameters are the conditions under which the experiment is carried out. They are therefore manipulated to obtain specific results. These variables are classified into two types. On the one hand, the controllable variables are those that can be changed. On the other hand, the uncontrollable variables are those that cannot be changed. Analysing these variables provides an in-depth understanding of the extent to which they influence the final result.
Adjustment levels
These levels refer to the values applied to the input variables during the experiment. These levels are modified or manipulated in order to determine the effects of the modifications, which makes it easier to explore ranges within which the minimum or exact specifications of the experiment could be met, such as improving a product or optimising a process.
Answers
The responses are the results obtained after manipulating the variables, including the necessary adjustments. With these, the engineers and specialists Those in charge of the experiment are evaluating the results of the study; that is to say, are investigating the performance of the variables. This analysis is based on the objectivity of the data collected; furthermore, the data are statistically processed to ensure reliable measurements.
Applications of Design of Experiments (DoE)
Among the most common uses are:
Consider alternatives
One of the main applications of the DoE is the evaluation of multiple alternatives with regard to a design or process. In other words, this type of methodology is very useful for understanding how the combination of certain variables leads to a particular outcome, particularly in the development of processes or products, or in the optimisation of operating conditions.
Maximise the response
As we have seen, the response is as expected. In this respect, experimental design helps to improve this response. But what is the point of this? These testing and evaluation procedures They are used to identify the factors that lead to the best response, that is, the best performance.
Minimise the impact on quality
A key aspect of DoE is minimising the impact on quality. How is this achieved? Once analysed, the data obtained from the experiments provide insight into the areas where the factors need to be modified or adjusted. Quality is understood to mean the absence of waste and the improvement of performance.
Optimising processes
But one cannot fail to mention the process optimisation as part of the DoE. Thanks to this process, it is possible to increase production efficiency, reduce costs and, consequently, to increase productivity. The variables used make it easier to identify factors that have a negative impact on overall performance. This identification enables corrective measures to be taken.
Quality control
Design of experiments (DoE) is also essential for quality control. This ensures that processes remain strictly in line with the specified parameters or conditions to ensure that the specifications are met. The industrial engineers and other professionals involved monitor any variations closely so that changes can be made where necessary.
Steps for developing the experimental design
These steps are:
Step 1. Description
It begins with a description of the experimental problem and the objectives, both general and specific. The input variables are then estimated, as well as the hypothesis guiding the experiment. This first step is essential for proper planning of the procedure.
Step 2. Specification
Next, it is essential to define the input variables and their adjustment levels. It is necessary to define the the conditions under which the experiment will be carried out.
Step 3. Design
The design involves specifying the working methodology. Building on this, practical implementation requires consideration of materials, resources, activities and analytical work.
Step 4. Collection
At this stage, the experiment is carried out as planned. Data on the process are also collected, so that there is a thorough record-keeping of the tests carried out according to the input variables.
Step 5. Adjustment
The adjustment is made every time a search is carried out clarify any ambiguities or inconsistencies during the experiment. Furthermore, when adjustments are made, it is common to repeat the experimental tests in order to obtain new results as expected.
Step 6. Prediction
This is the final stage of design of experiments (DoE). With the appropriate statistical analysis, it is possible to determine which of the variables, or combination of variables, optimises the process or improves the response. This helps to improve future designs, processes or techniques.
How do you choose the most suitable DoE?
That said, How do you choose the most suitable design of experiments (DoE)? The following points should be taken into account:
- Please bear in mind the aim of the experiment.
- Consider the most functional design.
- Set the nature of the design (single or multifactorial).
- Specify the type of design, whether it is a control or experimental design.
- Answer the questions to which answers are being sought.
- Correctly define the working groups
