Experimental analysis of the effect of natural fibers and SiC filler on the thermal behavior of green biocomposites

Brake friction materials (BFMs) have a critical role in ensuring the safety as well as the reliability of automotive braking systems. However, traditional BFMs, typically made from asbestos, are associated with environmental and health concerns. Therefore, this results in a growing interest in developing alternative BFMs that are eco-friendly, sustainable, and cost-effective. This study investigates the effect of varying concentrations of epoxy, rice husk, alumina (Al2O3), and iron oxide (Fe2O3) on the mechanical and thermal properties of BFMs prepared using the hand layup method. In this study, the rice husk, Al2O3, and Fe2O3 were filtered through a 200-mesh sieve. Note that the BFMs were fabricated using different combinations and concentrations of the materials. Their mechanical properties, such as density, hardness, flexural strength, wear resistance, and thermal properties, were investigated. The results suggest that the concentrations of the ingredients significantly influence the mechanical and thermal properties of the BFMs. A specimen made from epoxy, rice husk, Al2O3, and Fe2O3 with concentrations of 50 wt.%, 20 wt.%, 15 wt.%, and 15 wt.%, respectively, produced the best properties for BFMs. On the other hand, the density, hardness, flexural strength, flexural modulus, and wear rate values of this specimen were 1.23 g/cm3, 81.2 Vickers (HV), 57.24 MPa, 4.08 GPa, and 8.665 × 10−7 mm2/kg. In addition, this specimen had better thermal properties than the other specimens. These findings provide valuable insights into developing eco-friendly and sustainable BFMs with suitable performance for automotive applications.

/pdf/influence-of-varying-concentrations-of-epoxy-rice-husk-al2o3-3u8476wc.pdf

Influence of Varying Concentrations of Epoxy, Rice Husk, Al2O3, and Fe2O3 on the Properties of Brake Friction Materials Prepared Using Hand Layup Method

In innovative materials engineering, one of the most important areas of research is the process of incorporating the appropriate combinations of reinforcements into matrix material. These innovative materials have enhanced strength as well as improved wear properties. In light of this, the purpose of the current work was to construct hybrid composites utilizing Al2618 by integrating varied quantities of Boron Carbide (B4C), silicon nitride (Si3N4), and graphite particles by a liquid casting. The manufactured composites were subjected to scanning electron microscopy coupled with XRD analysis, micro hardness test, and wear evaluations. An examination of the microstructure of the material has shown that the reinforcement is evenly dispersed throughout the base alloy. The impact of different reinforcing mixtures on mechanical characteristics and wear behavior was also investigated. 

Effects of tertiary ceramic additives on the micro hardness and wear characteristics of Al2618+Si3N4-B4C-Gr hybrid composites for automotive applications

A novel study on the development of sisal-jute fiber epoxy filler–based composites for brake pad application

Artificial neural networks (ANNs) have recently gained popularity as useful models for grouping, clustering, and analysis in a wide range of fields. An ANN is a kind of machine learning (ML) model that has become competitive with traditional regression and statistical models in terms of useability. Lightweight composite materials have been acknowledged to be the suitable materials, and they have been widely implemented in various industrial settings due to their adaptability. In this research exploration, hybrid composite materials using Al2618 reinforced with B4C and Gr were prepared and then evaluated for hardness and wear behavior. Reinforced alloys have a higher (approximately 36%) amount of ceramic phases than unreinforced metals. With each B4C and Gr increase, the wear resistance continued to improve. It was found that microscopic structures and an appearance of homogenous particle distribution were observed with an electron microscope, and they revealed a B4C and Gr mixed insulation surface formed as a mechanically mixed layer, and this served as an effective insulation surface that protected the test sample surface from the steel disc. The ANN and Taguchi results confirm that load contributed more to the wear rate of the composites.

/pdf/effect-of-b4c-gr-on-hardness-and-wear-behavior-of-al2618-3f95cev9.pdf

Effect of B4C/Gr on Hardness and Wear Behavior of Al2618 Based Hybrid Composites through Taguchi and Artificial Neural Network Analysis

: Artiﬁcial neural networks (ANNs) have recently gained popularity as useful models for grouping, clustering, and analysis in a wide range of ﬁelds. An ANN is a kind of machine learning (ML) model that has become competitive with traditional regression and statistical models in terms of useability. Lightweight composite materials have been acknowledged to be the suitable materials, and they have been widely implemented in various industrial settings due to their adaptability. In this research exploration, hybrid composite materials using Al2618 reinforced with B 4 C and Gr were prepared and then evaluated for hardness and wear behavior. Reinforced alloys have a higher (approximately 36%) amount of ceramic phases than unreinforced metals. With each B 4 C and Gr increase, the wear resistance continued to improve. It was found that microscopic structures and an appearance of homogenous particle distribution were observed with an electron microscope, and they revealed a B 4 C and Gr mixed insulation surface formed as a mechanically mixed layer, and this served as an effective insulation surface that protected the test sample surface from the steel disc. The ANN and Taguchi results conﬁrm that load contributed more to the wear rate of the composites.

Hareesha Manjulaiah

Papers

A novel study on the development of sisal-jute fiber epoxy filler–based composites for brake pad application

Effect of B4C/Gr on Hardness and Wear Behavior of Al2618 Based Hybrid Composites through Taguchi and Artificial Neural Network Analysis

Effect of B 4 C/Gr on Hardness and Wear Behavior of Al2618 Based Hybrid Composites through Taguchi and Artificial Neural Network Analysis