Optimization Strategy for Process Design in Rubber Injection Molding: A Simulation-Based Approach Allowing for the...

Area of Science:

• Materials Science
• Polymer Engineering
• Computational Modeling

Background:

• Optimizing rubber production settings is time-consuming and resource-intensive.
• Process simulations are emerging as a method to predict rubber part cure and mechanical properties.
• Existing models, like second-order polynomials, show limitations in predicting mechanical characteristics for injection-molded parts due to narrow property distributions.

Purpose of the Study:

• To develop a more precise simulation approach for predicting mechanical properties of injection-molded rubber parts.
• To address the predictive failures of second-order models in rubber processing simulations.
• To combine logistic growth functions with second-order polynomials for enhanced approximation accuracy.

Main Methods:

• Designed an experimental plan for producing injection-molded parts from a Styrene-Butadiene Rubber (SBR) compound.
• Varied vulcanization temperatures and degrees of cure during part production.
• Mechanically characterized the produced parts and used the data to calculate model coefficients for the combined approximation approach.

Main Results:

• A combined model using logistic growth functions and second-order polynomials was developed.
• The new model enables simulation-based calculations of mechanical part quality.
• Simulations demonstrated reliable predictions, with results falling within the natural deviation of real-world measurements.

Conclusions:

• The proposed combined approximation approach significantly improves the prediction accuracy of mechanical properties for injection-molded rubber parts.
• This enhanced simulation method offers a reliable tool for optimizing rubber processing.
• The findings contribute to more efficient and accurate rubber goods production through advanced computational modeling.