An Approach of Data Science for the Prediction of Wear Behaviour of Hypereutectoid Steel

Hulipalled, Poornima; Algur, Veerabhadrappa; Lokesha, V.

doi:10.1007/s40735-022-00668-y

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…In our earlier work [21], we trained three ML models, namely SVM, GPR, and RF, to predict the wear behavior of hypereutectoid steel using the default parameter values or without passing any explicit values to the arguments. From Table 2, it is seen that random forest showed the best results in training and test datasets with 95.4% and 94% accuracy respectively.…”

Section: Resultsmentioning

confidence: 99%

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Optimized Machine Learning Algorithms to predict wear behavior of Tribo- Informatics

Hulipalled

Lokesha

2022

Preprint

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Wear rate prediction is most important in industrial applications. Machine learning (ML) has made an admirable contribution to the field of tribology. Standard ML models are extremely dependent on the parameter values; hence, tuning plays a crucial role in enhancing predictive performance. ML models largely work empirically, based on the data availability and application domain, the parameter tuning process effectively attains the desired accuracy of the models. The main aim of this study is to develop optimized ML models which render better accuracy than the previous study by using a grid search hyperparameter optimization technique. Five ML models namely Random Forest (RF), Support Vector Machine (SVM), K- Nearest Neighbor (KNN), Gaussian Process Regression (GPR), and Linear Regression (LR) are designed by tuning the parameters which lead to the optimization of models concerning the prediction accuracy.

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Section: Resultsmentioning

confidence: 99%

“…In our previous work [21], we used various ML algorithms with default argument values to predict the wear behavior of hypereutectoid steel.…”

Section: Introductionmentioning

confidence: 99%

Optimized Machine Learning Algorithms to predict wear behavior of Tribo- Informatics

Hulipalled

Lokesha

2022

Preprint

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“…There are three sorts of S/N ratios used to gauge quality: smaller is better, greater is better, and nominally better. Here the study involving, the ultimate tensile strength and total elongation are estimated using the S/N ratio characteristic "Larger is better," which may be derived using the equation: [22][23].…”

Section: Taguchi Methodsmentioning

confidence: 99%

Effect of Process Parameters on Tensile Behaviour of FSW AA 6061-AA 7075 Reinforced with TiO<sub>2</sub> Particles Using Statistical Approach

Mahendra

Sreenivasa

Kumar³

et al. 2022

jmmf

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Joining of dissimilar materials especially aluminium alloys through friction stir welding approach has been effectively carried out in different experimentations resulting in enhanced mechanical properties and meeting various application sectors. Process parameter plays an exceptional influence on the joint properties and determines the mechanical characterization. Here the objective is to investigate the AA6061 and AA 7075 reinforced with titanium dioxide (TiO2) nanoparticles using Taguchi and ANOVA methods considering pin profile, welding speed, tool rotational speed, and tilt angle utilizing this process. TiO2 powder was added at the weld juncture. All the specimens were welded subjected to various levels of operating conditions of the process parameters and further processed for tensile evaluation. Result shows that the most significant parameters are the tilt angle and pin profile for ultimate tensile strength.

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Intelligent retrieval of wear rate prediction for hypereutectoid steel

Hulipalled

Algur²,

Lokesha

2023

Multiscale and Multidiscip. Model. Exp. and Des.

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An Approach of Data Science for the Prediction of Wear Behaviour of Hypereutectoid Steel

Cited by 5 publications

References 20 publications

Optimized Machine Learning Algorithms to predict wear behavior of Tribo- Informatics

Optimized Machine Learning Algorithms to predict wear behavior of Tribo- Informatics

Effect of Process Parameters on Tensile Behaviour of FSW AA 6061-AA 7075 Reinforced with TiO<sub>2</sub> Particles Using Statistical Approach

Intelligent retrieval of wear rate prediction for hypereutectoid steel

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