Design of experiment
Common Challenges
- Many R&D organizations rely on trial-and-error methodology in their projects for developing new materials and products.
- Each experimentation cycle may take days or weeks and requires much effort.
- Such inefficient methodology results in a large amount of iterations (experiments) until reaching the desired objectives. In other words, it is a waste of time and resources.
- Researchers and engineers find themselves struggling to decide about the R&D direction, and more practically, which set of experiments they should do next.
The value proposition of MaterialsZone
- Reduces the number of experiments and iterations to optimize the material performance through design of experiments. This results in reduced R&D costs and faster time to market.
- Easily applied AI/ML visualizations, insights and predictability.
- Easy detection of data gaps and model prediction gaps provide necessary insights.
- No loss of knowledge. Every piece of recorded data counts.
- Focus on parameters (variables) that really matter.
- Rapid accumulation of knowledge and predictability.
- Easy application of AI/ML visualizations, insights and predictions.
- One platform, all the data, all the insights, all the stakeholders (R&D, scale-up, manufacturing QC, supply chain alternatives selection).
What does it take from a Materials Informatics Platform (MIP)?
- Flexible data model that supports multiple dimensions, multiple types of data and any hierarchical nesting and association inherent in materials data.
- Flexibility to accumulate data incrementally and continuously while the AI/ML insights and visualizations update as well.
- Easily applied AI/ML visualizations, insights and predictability.
- Simple workflow (figure 1) to obtain the desired results in the shortest way possible.
MaterialsZone Solution for Design of Experiments
Laboratory experiments are very common in R&D organizations. Wherever experiments exist, design of experiments is in crucial need to keep the R&D activity more lean, agile and efficient.
Design of experiments allows organizations to utilize their resources properly and reach the market before competition.
MaterialsZone has developed a design of experiments module to help researchers and engineers do their jobs efficiently. The module utilizes the use of an existing dataset containing the experimental parameters, such as: ingredients, ingredients composition, processing details, etc., and also contains the performance measures. The dataset is used to build a model correlating the above experimental parameters with the performance measures.
Design of experiments workflow:

Such design of experiments workflow will allow the researchers and engineers to significantly reduce the number of experiments they execute to reach a desired target, and get rid of the trial-and-error habit.
Other MaterialsZone solutions include:
- Innovative Plastics
- Materials data management
- Materials development
- Formulations development
- Materials selection – supply chain
- Manufacturing scale-up
- Materials manufacturing QC (six-sigma)
- Digital materials/product catalog