Neural Network Calculator of Rubber Characteristics with Improved Properties

Abstract

A new technique for the use of Artificial Neural Networks (ANN) for the generalization and visual presentation of the results of experimental studies is proposed. The possibility of using ANN for cases for which their use was previously considered impossible is shown. ANN calculators have been created that summarize the results of experimental studies on the effect of trans-polynorbornene and basalt fiber on the characteristics of a rubber compound based on general-purpose rubbers (isoprene SKI-3, butadiene-methylstyrene SKMS-30ARK and butadiene SKD), which also contained vulcanizing agents (N, N′-dithiodimorpholine, thiuram D), vulcanization accelerators (sulfenamide C, 2-mercapto-benzothiazole), vulcanization activators (zinc white, stearic acid), emollients (industrial oil I-12A, rosin) and antioxidants (acetonanil H, diaphene FP). The rubber mixture was prepared on laboratory rollers LB 320 160/160. Subsequently, the rubber mixture was vulcanized in a P-V-100-3RT-2-PCD press. For the resulting vulcanizates, the physical and mechanical properties and their changes were determined after daily exposure to air and in a standard SZhR-1 hydrocarbon liquid at a temperature of 100 °C. We also studied the change in the mass of vulcanizates after exposure to industrial oil I-20A and water. The dynamic parameters (modulus of elasticity and mechanical loss tangent) of vulcanizates, which characterize their noise and vibration-absorbing properties, were studied on a Metravib VHF 104 dynamic mechanical analyzer. The created ANN calculators allow solving a direct problem - interpolating the dependences of all rubber characteristics on the content of basalt fiber, as well as solving inverse problems - to determine the required content of basalt fiber to create rubber with the required performance properties. The autonomous executable modules of the calculators developed by ANN were made and can be passed to everyone.