AI Experiment Design

Experimental Design and Multiobjective Optimization

Translating experimental variables, constraints and target indicators into an implementable test matrix, recommending the next round of most verifiable experiments in conjunction with model iterative.

Experimental design and multi-purpose optimization display
01

Solution Overview

Focus on formulation development, process window exploration, material performance optimization and multifactor pilot design, combining DOE, Bayesian optimization, response analysis and multi-target decision-making, to pool limited experimental resources into the most informative combination. The R & D scenario applied to multiple variables, high costs, long experimental cycles and mutually constrained objectives.

Variable DesignIdentification of factors, levels, constraints and feasible ranges
Pilot programmeBuild a positive, responsive or self-adaptation test matrix
Multi-purpose optimizationIntegrated performance, cost, efficiency and stability indicators
Programme recommendationsOutput of priority and justification for the next round of experiments
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Applications

  • Process window exploration for response conditions, temperature, concentration, time, pH etc.
  • Material formulation, additive ratio, component content and treatment optimization
  • Multiple performance indicators constrain each other and need to look for Paretto's best option.
  • The cost of the experiment is high, and it is hoped that more information will be obtained with less.
  • Fewer historical data, need to update model recommendations by experiment
Experimental factors, binding scope and design spatial definition
Target constraints
Experimental variables and binding space
Variable Space
DOE test matrix and sample layout
Test Matrix
Multi-purpose optimization and programme recommendations
Optimization of recommendations
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Overall process

1. Definition of needsTarget indicators
Resource constraints
2. Incentive combingVariable Range
Experimental Boundary
3. Programme generationTest Matrix
Sample Policy
4. Modelling analysisResponse Noise
Projection models
5. Optimizing decision-makingParetto solves
Recommended Sorting
6. Validation of overlapsTurn it back in.
Programme update
04

Technical modules

  • Experimental factor modelling:Structure variables, levels, constraints and target indicators into calculable issues.
  • DOE and self-adaptation sampling:Selecting a positive design, Latin hypercube, response face, or Bayesian optimization according to the data base.
  • Multi-purpose optimization:Explanatory trade-offs between performance, cost, stability, time and security.
  • Active learning iterative:Update the model with the results of each round of experiments and continue to recommend a more valuable next round of experiments.
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Project value

Experimental savingsCover the more critical variable space with fewer experiments
Improved efficiencyMove from experience error to model driven recommendation
Enhanced interpretabilityDescription of the variable contributions and trade-offs behind each of the recommended options
Support for closed-ring research and developmentThe results of the experiment are sustainably refilled and follow-up recommendations updated
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