Rubber Compound Optimization with Soybean Oil Using Design of Experiments

A dataset from a paper detailing the use of a statistical design of experiments (DoE) strategy to optimize a rubber compound formulation. The work by Pablo Ernesto Salvatori focuses on maximizing soybean oil content while meeting tire tread performance targets. It includes fitted models and response surfaces for properties like glass transition temperature and Mooney viscosity.

Use Cases

• Optimizing rubber compound formulations based on the described response surfaces for properties like glass transition temperature and tanδ.
• Applying the desirability function methodology to multi-objective optimization problems in material science.
• Validating statistical models for predicting material properties from ingredient mixtures, as demonstrated with petrochemical and soybean oils.

Strengths

• The description provides specific, validated optimal oil composition predictions (e.g., 7.86 pHR soybean oil).
• The experimental validation of the proposed models is explicitly mentioned, indicating tested methodology.

Limitations

• Row count, column definitions, and sample data are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Pablo Ernesto Salvatori via paperswithcode

Collection Method

Likely contains experimental data and model outputs from a designed study on rubber compound formulation.