Deciding which type of DOE design to use is an important decision in any DOE campaign - and one where many factors should be considered.
The screening and response mapping stages are pivotal components of a DOE campaign, each serving distinct purposes in the process of optimizing experimental outcomes.
The screening stage involves the use of carefully designed experiments to identify the most influential factors among a large set of potential variables, helping to narrow down the focus to a manageable subset. This stage is characterized by efficiency, aiming to quickly filter out less impactful factors and provide a preliminary understanding of the experimental landscape.
On the other hand, the response mapping stage occurs once the key factors have been identified. In this stage, a more detailed exploration of the relationships between these factors and the desired responses is conducted. This often involves refining the factor levels, fine-tuning experimental conditions, and comprehensively mapping out the response surface. While the screening stage prioritizes efficiency and factor identification, the response mapping stage delves deeper into the interactions between critical factors and their impact on outcomes, enabling researchers to optimize responses with greater precision and insight.
With this in mind, you might want to use different types of design for the different stages in your DOE. Therefore, choosing the right DOE design is crucial to get meaningful results while optimizing resources and time. In this article, we'll explore four common types of DOE designs—Screening, Space Filling, Optimal, and Full-Factorial—and discuss their strengths and weaknesses.
Pros and cons of different design types
1. Space-filling Designs:
Space-filling designs, also known as uniform designs, aim to evenly distribute experimental runs across the entire factor space. These designs are suitable when you want to explore a wide range of factor settings without focusing on specific factors of interest. Space-filling designs are ideally suited to identifying hot-spots or dead zones in your factor space when you have limited pre-existing knowledge about your design space. If you have sufficient resources it is also possible to use high-run-number space-filling designs to map responses in high resolution, and gain some insight into high-order effects, such as two-factor interactions.
Pros:
Coverage: Space-filling designs provide a comprehensive exploration of the factor space, ensuring a well-rounded understanding of the factor ranges
Interactions Included: These designs can help detect interactions between factors, providing a more holistic view of the system.
Cons:
Resource-Intensive: Space-filling designs can require a relatively large number of experiments, consuming more resources and time.
Potential Redundancy: Some factor combinations might be overrepresented, leading to redundant data points.
Low Power: Use caution when using space-filling designs to understand interactions between factors as they may lack the statistical power to do this.
2. Optimal Designs:
Optimal designs are tailored to maximize the precision of estimates for specific factors of interest or model parameters (i.e. interactions and non-linear terms). These designs are suitable when you need specific information about certain factors and interactions, or when you are dealing with limited resources.
Pros:
Precision: Optimal designs provide highly accurate estimates for the factors of interest, leading to more robust conclusions.
Efficiency: They can achieve precise results with a smaller number of experiments compared to other designs.
Tailored Solutions: Optimal designs are customised to the specific research goals, allowing researchers to focus on critical factors, interactions, and non-linear terms.
Understanding Interactions: Optimal designs can be used to detect and understand interactions and non-linearity. However, this depends heavily on the run number, factor type, and factor number. Terms are often confounded with each other so make sure to select a design with the appropriate statistical power.
Cons:
Complexity: Designing and implementing optimal designs can be challenging and can require more advanced statistical knowledge.
Limited Generalizability: Optimal designs are tailored to specific factors, which might limit their applicability to broader scenarios.
3. Full-Factorial Designs:
Full-factorial designs involve testing all possible combinations of factor levels. While they can be resource-intensive, they provide a complete picture of how factors interact.
Pros:
Comprehensive Insights: Full-factorial designs offer a detailed understanding of factor interactions, leaving no interactions unexplored.
Robust Conclusions: Results from full-factorial designs are highly reliable due to the exhaustive exploration of the factor space.
Cons:
Resource Demand: These designs require a large number of experiments, making them time-consuming and resource-intensive.
Complexity: The sheer volume of experiments can make data collection, analysis, and interpretation overwhelming.
Unnecessary Details: Not all factors may have substantial impacts, leading to exhaustive data collection for potentially minor insights.
Take a look at the articles below for more information on each of the design types in Synthace:
In addition to the three design types above, there are many other types of DOE design outside of those available by default in the Synthace platform. One commonly used design type are Screening Designs, such as fractional factorial designs.
4. Screening Designs:
Screening designs are particularly useful when there are numerous factors to consider and resources are limited. They help identify the most influential factors, while reducing the number of experiments required. Key features of screening designs include:
Pros:
Efficiency: Screening designs are economical as they minimize the number of experiments, saving time and resources.
Identifying Key Factors: These designs focus on identifying the most influential factors that significantly impact the outcome.
High-Level Insights: They provide a quick overview of which factors are likely to be significant, guiding researchers on where to concentrate their efforts.
Cons:
Limited Precision: Due to the reduced number of experiments, the estimates of factor effects may have limited precision.
Interactions Ignored: Screening designs often don't account for complex interactions among factors.
In conclusion, the choice of a DOE design depends on the research goals, available resources, and the complexity of the system under study. Each design type offers unique advantages and drawbacks. Screening designs are efficient for identifying key factors, space-filling designs provide comprehensive exploration, optimal designs offer precision, and full-factorial designs reveal complex interactions. By understanding the strengths and weaknesses of these designs, researchers can make informed decisions to achieve meaningful results, while optimizing their experiments.