Parametric analysis of erosion wear of sponge iron slag-filled ramie–epoxy composites using Taguchi and preference selection index methods

Self Cite

This article reports on development of an adaptive framework for predicting the erosion performance of polymer composites using certain statistical and machine learning (ML) models. For this, ramie‐epoxy composites reinforced with variations (0–30 wt%) of sponge iron slag (an iron industry waste) are considered. The composites are fabricated and then subjected to high temperature solid particle erosion wear trials following Taguchi's L27 orthogonal array. The effects of different control factors on the erosion rate in an interactive environment are appraised by analysis of variance (ANOVA) which reveals the filler content as the most significant factor contributing 66.21%, followed by impact velocity (22.86%) and impingement angle (2.28%). A regression model based on the input–output parameters obtained from experimentation is constructed for prediction of erosion rate. Further, four predictive models using different machine learning algorithms are also proposed to predict the erosion rate of the composites. The feasibility and performance of each ML model is assessed using appropriate performance metrics. Among all the models, the gradient boosting machine model is found to be the most reliable model exhibiting the highest prediction accuracy and least errors.Highlights Development of novel class of composites reinforced with sponge iron slag. Database creation based on erosion wear experimentation on the composites. Data‐driven modeling for prediction of erosion rates using machine learning. Comparison of performance of different models and identifying the best one.

Section: Materials Methods and Experimentsmentioning

confidence: 99%

Section: Input and Output Parametersmentioning

confidence: 99%

Development of machine learning models for the prediction of erosion wear of hybrid composites

Mahapatra,

Satapathy

2024

Self Cite

Parametric appraisal and wear prediction of hybrid composites using an integrated soft computing approach

Mahapatra,

Satapathy

2024

Self Cite

This article reports on the implementation of artificial neural network (ANN) and statistical methods to analyze and predict the erosion performance of titania (TiO2) filled ramie‐epoxy based composites. These hybrid composites are prepared by conventional hand lay‐up route and subjected to solid particle erosion tests as per Taguchi's L27 orthogonal array. The effects of different control factors on erosion rate of these composites are studied using Taguchi method. It reveals that impact velocity and filler content have significant effect on the erosion wear rate followed by other least significant factors. The individual effect of each control factor while keeping other factors constant is ascertained by performing steady state erosion experiments. Further, a computational model based on ANN is used as a tool to effectively predict the erosion rates of the composites. The results show that the predicted values are in reasonably good agreement with the experimental ones with an accuracy of 90% and relative error lying within a range of 1%–10%. Further, the trained ANN model is validated by considering the erosion rates obtained during steady state erosion process as the input parameter. The possible mechanisms causing the wear loss are identified using electron microscopy.Highlights Successful fabrication of titanium oxide reinforced hybrid composites. Steady state erosion experiments are performed. Erosion rates are predicted using ANN and compared with the experimental data. Wear loss mechanisms are identified using electron microscopy.

A comparison of fabrication routes and property evaluation methods for bio‐particulate filled glass‐epoxy composites

Batha,

Mahapatra,

Satapathy

2024

This research work aims to investigate the effect of fabrication techniques on the physico‐mechanical properties of pistachio shell (PS) filled glass‐epoxy composites and to compare the experimental findings with numerical simulation results using finite element analysis (FEM). These hybrid composites are fabricated by vacuum‐assisted resin transfer molding (VARTM) and hand layup (HL) techniques with fixed glass fiber fraction (20 wt. %) but different PS powder loadings of 0–30 wt. %. The composites are then characterized for physical, compositional, micro‐structural and mechanical properties. The micro‐structural analysis of the filler and composites is performed using electron microscopy and stereo‐microscopy. To identify the phases present in both the raw filler and the composite, an x‐ray diffraction test is performed. The presence of functional groups is identified with the help of Fourier transform infrared spectroscopy (FTIR). It is observed that there is a reasonable improvement in the mechanical properties of the composites with increase in PS powder content, irrespective of the fabrication process used. The density and void fraction are also greatly affected by the amount of filler particles present in the composites and also on the composite fabrication technique. The FEM analysis is also carried out using ANSYS 22.0 workbench to determine the characteristic properties numerically. The comparison of experimental and numerical results yields that the properties obtained by using VARTM results are close to experimental ones with errors lying in the range of 1%–4%. These composites can possibly find potential applications in light duty structures and wear resistant applications.Highlights Development of hybrid composites using HL and VARTM techniques. Physical and mechanical properties alter with filler loading. Evaluation of mechanical properties using finite element analysis. Validating the model by comparing experimental and numerical results.