Design of experiment

Table of Contents

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:

Figure 1: 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:

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