Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems

This study investigated the effect of stirring speed, stirring time, and particle weight fraction on the mechanical properties of magnesium matrix composites (Mg-MMCs) synthesized by the stir casting process. In addition, response surface methodology (RSM) and artificial neural network (ANN) models were used to optimize process parameters and create predictive models for evaluating the mechanical properties of Mg-MMCs. According to the results, the optimal parameter conditions for maximum mechanical properties based on the desirability function methodology were achieved at a stirring speed of 312.8 rpm, a stirring time of 11.9 min, and a weight fraction of particles of 9.9 wt%. In conjunction with the ANN and RSM models, the expected findings in this study will present beneficial recommendations in selecting main process parameters, contributing to the development of a good database for magnesium composites in manufacturing and mechanical performance evaluation. 

Optimized mechanical properties of magnesium matrix composites using RSM and ANN

Abstract Mg–Al–Zn alloys are widely preferred in many applications by considering their excellent properties of high stiffness-to-weight ratio, lightweight, high strength-to-weight ratio, low density, castability, high-temperature mechanical properties, machinability, high corrosion resistance, and great damping. Improving the properties of such alloys is challenging due to their hexagonal crystal structure and other alloying limitations. This study aims to synthesize Mg–Al–Zn alloy by incorporating the alloying elements 8.3 wt% Al, 0.35 wt% Zn on pure magnesium (Control specimen). Then synthesize Mg–Al–Zn/BN/B 4 C hybrid composite by reinforcing B 4 C at three weight proportions (3 wt%, 6 wt%, 9 wt%) along with constant solid lubricant BN (3 wt%) through a stir casting process. The hybrid composite samples were characterized and compared with the performances of the control specimen. The results reveal that 9 wt% B 4 C reinforced samples outperformed through recording the improvement of tensile strength by 28.94%, compressive strength by 37.89%, yield strength by 74.63%, and hardness by 14.91% than the control specimen. Apart from this, it has reduced the corrosion area (37.81%) and noticed negligible changes in density (increased by 0.03%) and porosity (decreased by 0.01%) than the control specimen. The samples were characterized using SEM, XRD, and EDAX apparatus. 

/pdf/a-synergistic-effect-on-enriching-the-mg-al-zn-alloy-based-1alabo3t.pdf

A synergistic effect on enriching the Mg–Al–Zn alloy-based hybrid composite properties

Prediction of wear performance of ZK60 / CeO2 composites using machine learning models

Friction and wear optimization of SiC/graphite reinforced AZ31 hybrid composite using Taguchi method

The machine learning methodology is gaining immense exposure as a potential methodology for solving and modelling the machining behaviour of advanced materials. The present paper deals with the application of machining learning approach in analyzing and predicting the effect of reinforced silicon carbide (SiC) particle size on the erosion behaviour of silicon carbide reinforced polymer composites. L27 orthogonal array was designed based on Taguchi’s methodology to execute the experiments. Support vector machine (SVM) and multi-linear regression (MLR) approach were coupled with Taguchi’s methodology to validate obtained optimized response characteristics. These machine learning-based SVM and MLR models are adopted to analyze the absurdity among obtained experimental results and predicted response. Out of 27 experimental runs based on experimental design, 19 experimental runs were selected for training models whereas 08 models were selected for the testing phase. Impingement angle, workpiece reinforcement, standoff distance and slurry pressure were used as input process parameters, whereas material loss was observed as response characteristics. The kernel functions, i.e. Pearson VII based universal kernel (PUK) and radial based function (RBF) kernel were used with machine learning models to obtain the best performing machine learning approach in predicting erosion behaviour of polymer composites.

Machine Learning-Based Erosion Behavior of Silicon Carbide Reinforced Polymer Composites

Manual farming tools must match the body characteristics of the workers for their safety and performance. With this objective to serve in the present study, 18 body dimensions were measured including age and mass for 200 male farmers from the five districts of Haryana. Handle design of spade and sickle was suggested following the measured body characteristics. Biomechanical study was conducted to evaluate the compression loading at L4–L5 of five subjects using newly designed spade and pre-existing spade alternately. A similar virtual environment was developed in the CATIA-V5 to examine the compression loading at L4–L5. The results obtained were compared using t test, which show a significant reduction in compression forces at L4–L5 for p < 0.05.

Biomechanical Evaluation of Spinal Loading Using Farm Hand Tools and Handle Design Modifications for Haryana Male Farmers

This article statistically investigates the effect of various parameters such as material factors: silicon carbide (SiC) fraction, graphite (Gr) fraction and mechanical factors: normal load, sliding distance and speed on the sliding wear rate of self-lubricating AZ91D-SiC-Gr hybrid magnesium composites. The self-lubricating hybrid composites were fabricated using advanced vacuum assisted stir casting process. The sliding wear tests have been performed under dry conditions on pin-on-disc tribometer at 10-50 N loads, 1-3 m/s sliding speed and 1000-2000 m sliding distance. It has been examined that hybrid composites yielded improved wear resistance with reinforcement of SiC and solid lubricant graphite. ANOVA and signal-to-noise ratio investigation indicated that applied load was the most critical factor influencing the wear rate of fabricated hybrid composites followed by sliding distance. Further, the AZ91D/5SiC/5Gr hybrid composite has exhibited the best wear properties. From the SEM and EDS analysis of worn surfaces, delamination was confirmed as the dominant wear mechanism for AZ91D-SiC-Gr hybrid composites.

Optimization and Effect of Reinforcements on the Sliding Wear Behavior of Self-Lubricating AZ91D-SiC-Gr Hybrid Composites

In present research AZ91D/SiC composites were developed using advanced vacuum assisted stir casting process. The tribological properties of AZ91D/SiC composites under dry, oil and nanofluids lubricating conditions were investigated. The nanofluids were prepared by adding the silicon carbide (SiC) nanoparticles to the soluble oil with different weight percentages (1 wt.%, 1.5 wt.%, 2.0 wt.%) for experimentation. The mixing of nanoparticles with base fluid improves the tribological and thermal properties of fluids as the nanoparticles possess higher thermal conductivity than base fluids. The thermal conductivity of SiC nanofluids was evaluated. The wear surfaces under different lubricating conditions (dry, oil and nanofluids) were also analyzed with FESEM. The findings of the study reveal that there was a remarkable enhancement in tribological properties (wear and coefficient of friction) when SiC nanoparticles were added to soluble oil. Furthermore, the selected samples were analyzed using energy-dispersive X-ray spectroscopy (EDS), scanning electron microscope (SEM).

Tribological Investigation of AZ91/SiC Magnesium Hybrid Composite under Dry, Oil and Nanofluids Lubricating Conditions

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Papers

Machine Learning-Based Erosion Behavior of Silicon Carbide Reinforced Polymer Composites

Biomechanical Evaluation of Spinal Loading Using Farm Hand Tools and Handle Design Modifications for Haryana Male Farmers

Optimization and Effect of Reinforcements on the Sliding Wear Behavior of Self-Lubricating AZ91D-SiC-Gr Hybrid Composites

Tribological Investigation of AZ91/SiC Magnesium Hybrid Composite under Dry, Oil and Nanofluids Lubricating Conditions