Erosion and wear analysis of fly ash filled GFRP composite

Ekka, Anisha; Panda, Amruta; Mahapatra, Trupti Ranjan; Mishra, Debadutta

doi:10.1016/j.matpr.2023.01.140

Cited by 3 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The composite samples after erosion test are displayed in Figure 2B. The erosion wear rate after each test is calculated using Equation () 43 . Tests are replicated on three identical samples of similar composition to validate the erosion loss and the mean value of the erosion rates is taken as the data point.

italicEWR goodbreak= \frac{m_{italiclc}}{m_{e}} goodbreak= \frac{m_{ci} - m_{cf}}{e_{d} \times t},

where EWR stands for erosion wear rate and m lc represents the mass lost by the sample and m e denotes the mass of the silica particles impacted on the specimen.…”

Section: Materials Methods and Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

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

Mahapatra,

Satapathy

2024

Polymer Composites

View full text Add to dashboard 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.

show abstract

italicEWR goodbreak= \frac{m_{italiclc}}{m_{e}} goodbreak= \frac{m_{ci} - m_{cf}}{e_{d} \times t},

where EWR stands for erosion wear rate and m lc represents the mass lost by the sample and m e denotes the mass of the silica particles impacted on the specimen.…”

Section: Materials Methods and Experimentsmentioning

confidence: 99%

“…The erosion wear rate after each test is calculated using Equation (1). 43 Tests are replicated on three identical samples of similar composition to validate the erosion loss and the mean value of the erosion rates is taken as the data point.…”

Section: Test Apparatusmentioning

confidence: 99%

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

Mahapatra,

Satapathy

2024

Polymer Composites

View full text Add to dashboard Cite

show abstract

“…Figure 1B shows the composite samples after erosion trials. Equation () 43 is used to determine the non‐dimensional erosion wear rate of each test. Tests are replicated on three identical samples of similar composition to validate the erosion loss and the mean value of the erosion rates is taken as the data point.

Erosion rate goodbreak= \frac{mass lost by the composite}{mass of the erodent particles} goodbreak= \frac{m_{i} - m_{f}}{e \times t}

where m i and m f are the initial and final masses of composite sample.…”

Section: Methodsmentioning

confidence: 99%

“…Figure 1B shows the composite samples after erosion trials. Equation (1) 43 is used to determine the non-dimensional erosion wear rate of each test. Tests are replicated on three identical samples of similar composition to validate the erosion loss and the mean value of the erosion rates is taken as the data point.…”

Section: Test Apparatusmentioning

confidence: 99%

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

Mahapatra,

Satapathy

2024

Polymer Composites

View full text Add to dashboard 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.

show abstract

Mechanothermal assessment of outdoors aged hybrid glass fibre reinforced polymer composite filled with fly ash as industrial waste

Pradhan,

Chakraverty,

Behera

et al. 2024

Journal of Reinforced Plastics and Composites

View full text Add to dashboard Cite

Industrial fly ash impregnated glass fibre reinforced polymer (GFRP) composites were examined to assess their overall characteristics in adverse ambient conditions, keeping in mind that use of filler materials in FRP composites was expected to enhance the strength properties of the material. GFRP composite specimens with 2–10 wt % industrial fly ash were fabricated in the laboratory. These were exposed to open ambient ageing for 120 days. The samples impregnated with 8 wt % fly ash and aged for 120 days were seen to absorb the minimum moisture and exhibited the maximum ILSS of 35.19 MPa and flexural strength of 690 MPa, respectively. These samples also exhibited the highest Tg of 101.53°C as revealed through low temperature DSC. It was also observed that the 8 wt% fly ash containing samples aged in the open for 120 days showed the ILSS to be 5.83% higher and Tg to be 21.95% higher as compared to the unaged GFRP samples without fly ash. FTIR spectra confirm the trend of thermo-mechanical properties. Both optical and scanning electron microscopy of the fractured surfaces of the test samples revealed the modes of mechanical failure of the hybrid GFRP composite with their indicative properties at optimized extent of fly ash dispersion.

show abstract

Erosion and wear analysis of fly ash filled GFRP composite

Cited by 3 publications

References 14 publications

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

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

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

Mechanothermal assessment of outdoors aged hybrid glass fibre reinforced polymer composite filled with fly ash as industrial waste

Contact Info

Product

Resources

About