TY - JOUR

T1 - Optimization Strategy for Process Design in Rubber Injection Molding

T2 - A Simulation-Based Approach Allowing for the Prediction of Mechanical Properties of Vulcanizates

AU - Traintinger, Martin

AU - Azevedo, Maurício

AU - Kerschbaumer, Roman Christopher

AU - Lechner, Bernhard

AU - Lucyshyn, Thomas

N1 - Publisher Copyright: © 2024 by the authors.

PY - 2024/7/17

Y1 - 2024/7/17

N2 - Selecting the optimal settings for the production of rubber goods can be a very time-consuming and resource-intensive process. A promising method for optimizing rubber processing in a short period of time is the use of simulation routines. However, process simulations have only recently enabled meaningful predictions of not only the part’s state of cure but also its mechanical characteristics. As a first approach, second-order polynomials were considered suitable for describing the properties of compression-molded parts. However, more precision is required for injection molding due to the narrower distribution of mechanical characteristics of parts produced at different vulcanization temperatures. This became evident when the approximation of mechanical data with second order models partly revealed significant failures of part behavior prediction. To tackle this issue, a combined approach for approximation is proposed in this contribution by means of logistic growth function in addition to second order polynomials. To feed the model, an experimental plan was designed for producing injection-molded parts from an SBR compound at various temperatures and to different degrees of cure. The parts obtained were then characterized mechanically, and the results were opposed to varying degrees of cure and extents of reaction to calculate the model coefficients. Once available, a simulation-based calculation of the mechanical part quality is possible. The comparison of test results from the simulation and the real process has shown a reliable prediction, as simulation results were found within the natural deviation of the real measurements.

AB - Selecting the optimal settings for the production of rubber goods can be a very time-consuming and resource-intensive process. A promising method for optimizing rubber processing in a short period of time is the use of simulation routines. However, process simulations have only recently enabled meaningful predictions of not only the part’s state of cure but also its mechanical characteristics. As a first approach, second-order polynomials were considered suitable for describing the properties of compression-molded parts. However, more precision is required for injection molding due to the narrower distribution of mechanical characteristics of parts produced at different vulcanization temperatures. This became evident when the approximation of mechanical data with second order models partly revealed significant failures of part behavior prediction. To tackle this issue, a combined approach for approximation is proposed in this contribution by means of logistic growth function in addition to second order polynomials. To feed the model, an experimental plan was designed for producing injection-molded parts from an SBR compound at various temperatures and to different degrees of cure. The parts obtained were then characterized mechanically, and the results were opposed to varying degrees of cure and extents of reaction to calculate the model coefficients. Once available, a simulation-based calculation of the mechanical part quality is possible. The comparison of test results from the simulation and the real process has shown a reliable prediction, as simulation results were found within the natural deviation of the real measurements.

KW - injection molding

KW - logistic growth

KW - mechanical characterization

KW - optimization

KW - rubber part quality

KW - simulation

KW - sustainability

UR - http://www.scopus.com/inward/record.url?scp=85199662992&partnerID=8YFLogxK

U2 - 10.3390/polym16142033

DO - 10.3390/polym16142033

M3 - Article

AN - SCOPUS:85199662992

VL - 16.2024

JO - Polymers

JF - Polymers

SN - 2073-4360

IS - 14

M1 - 2033

ER -