Optimization of drilling characteristics of Al2O3 and boiled eggshell filler-added hybrid bio composite from agriculture residue

Saravanakumar, S.; Sathiyamurthy, S.; Ananthi, N.; Devi, Pooja

doi:10.1007/s13399-023-04572-4

Cited by 11 publications

(2 citation statements)

References 29 publications

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“…“Residual” denotes unexplained variability, while “Cor Total” represents the total corrected for. The F ‐value stands at 93.3, and the p ‐value is less than 0.0001, signifying the model's significance 45 . This implies that at least one factor or interaction significantly affects the SWR.…”

Section: Resultsmentioning

confidence: 94%

Optimized machine learning with hyperparameter tuning and response surface methodology for predicting tribological performance in bio‐composite materials

Sengottaiyan,

Subbarayan,

Viswanathan

et al. 2024

Polymer Composites

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This study investigates the influence of NaOH treatment on tribological behavior in hybrid fiber‐reinforced composites, specifically employing Banana fiber with Al2O3 filler in an epoxy matrix. Through design of experiments (DOE), disc speed, wear duration, and NaOH treatment are analyzed for specific wear rate (SWR) and coefficient of friction (COF). To advance the understanding of wear characteristics, the study leverages advanced machine learning, using Python‐powered artificial neural networks (ANN), is integrated with innovative ANN hyperparameter optimization. Optimized parameters (1050 rpm, 60 s, 5% treatment) significantly minimize SWR (12.38 × 10−5 mm3/Nm) and COF (0.2). Scanning electron microscopy (SEM) analysis reveals improved interfacial adhesion and identifies micro‐cracks as the primary wear mechanism. This work contributes to a profound understanding of wear in hybrid composites, offering a fine‐tuned predictive model for optimizing tribological behavior and advancing material science and engineering.Highlights Reduced SWR, COF in composites via NaOH optimization. DOE: Explored disc speed, duration, NaOH impact. Advanced machine learning techniques enhanced wear prediction. Innovative: ANN hyperparameter optimization. Optimal Parameters: 1050 rpm, 60 s, 5% treatment.

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Section: Resultsmentioning

confidence: 94%

Optimized machine learning with hyperparameter tuning and response surface methodology for predicting tribological performance in bio‐composite materials

Sengottaiyan,

Subbarayan,

Viswanathan

et al. 2024

Polymer Composites

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“…Important information concerning the impact of one or more variables on a response variable can be obtained through a well-designed experiment. [41][42][43] In order to reduce surface roughness (R a ) and kerf taper angle (K a ), this study used standoff distance (SD), transverse speed (TS), and abrasive flow rate (AF) as input parameters. A complete factorial design was used to test each parameter at three different levels, and the interactions in hybrid fiber-reinforced composites were investigated in detail.…”

Section: Design Of Experimentsmentioning

confidence: 99%

Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber‐reinforced composites

Vinoth,

Sathiyamurthy,

Saravanakumar

et al. 2024

Polymer Composites

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The aim of this investigation was to delve into the impact of abrasive water jet machining (AWJM) process variables on the surface roughness (Ra) and kerf angle (Ka) of hybrid fiber‐reinforced polyester composites. Utilizing both response surface methodology (RSM) and artificial neural network (ANN) prediction models, the study sought to optimize the input parameters for abrasive water jet machining, specifically in the context of paddy straw and PALF‐reinforced polyester hybrid composites. The process parameters targeted for optimization included the abrasive flow rate, traverse rate, and standoff distance during AWJM. The investigation identified an optimal combination of AWJM parameters that effectively meets the practical requirements for machining hybrid fiber‐reinforced polyester composites. According to the RSM, the suggested optimal values for the process parameters are an abrasive flow rate set at 300 g/min, traverse speed at 110 mm/min, and standoff distance at 1 mm. The ANN exhibited robust predictive capabilities, achieving high R2 scores of 0.932 and 0.962 for surface roughness and kerf angle, respectively. To enhance the performance of abrasive water jet machining and minimize surface roughness and kerf angle, the researchers conducted an optimization of the process parameters. Subsequently, confirmation experiments were executed to validate the predictive model and fine‐tune the set of process parameters for practical application.Highlights Investigated AWJM impact on Ra value and kerf angle of hybrid composites. Used RSM and ANN models for parameter optimization in biocomposite. Optimal AWJM parameters: AFR (300 g/min), TS (110 mm/min), and SOD (1 mm). ANN showed strong predictions: R2 scores 0.932 (Ra) and 0.962 (Ka). Confirmation experiments validated the predictive model for applications.

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Optimizing Cold Metal Transfer-Wire Arc Additive Manufacturing Parameters for Enhanced Mechanical Properties and Microstructure of ER5356 Aluminum Alloy Using Artificial Neural Network and Response Surface Methodology

Manikandan,

Arumugam

2024

J. of Materi Eng and Perform

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Optimization of drilling characteristics of Al2O3 and boiled eggshell filler-added hybrid bio composite from agriculture residue

Cited by 11 publications

References 29 publications

Optimized machine learning with hyperparameter tuning and response surface methodology for predicting tribological performance in bio‐composite materials

Optimized machine learning with hyperparameter tuning and response surface methodology for predicting tribological performance in bio‐composite materials

Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber‐reinforced composites

Optimizing Cold Metal Transfer-Wire Arc Additive Manufacturing Parameters for Enhanced Mechanical Properties and Microstructure of ER5356 Aluminum Alloy Using Artificial Neural Network and Response Surface Methodology

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