Surface treatment of tool steels against galling failure

Psyllaki, P.; Σταματίου, Κωνσταντίνος; Iliadis, Iason; Mourlas, Athanasios; Vaxevanidis, Nikolaos M.

doi:10.1051/matecconf/201818804024

Cited by 44 publications

(21 citation statements)

References 11 publications

(17 reference statements)

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“…These models are mainly based on improved regression analysis [9]. However, the regression method is having a limitation as the structure of the model is based on only a limited number of linear or nonlinear equations [10,11].…”

Section: Introductionmentioning

confidence: 99%

Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis

Nguyen¹,

Pham²,

Tuyen³

et al. 2019

TOBCTJ

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Background: Consolidation coefficient (Cv) is a key parameter to forecast consolidation settlement of soft soil foundation as well as in treatment design of soft soil foundation, especially when drainage consolidation is used in foundation treatment of soft soil. Objective: In this study, the main objective is to predict accurately the consolidation coefficient (Cv) of soft soil using an artificial intelligence approach named Random Forest (RF) method. In addition, we have analyzed the sensitivity of different combinations of factors for prediction of the Cv. Method: A total of 163 soil samples were collected from the construction site in Vietnam. These samples at various depth (m) were analyzed in the laboratory for the determination of clay content (%), moisture content (%), liquid limit (%), plastic limit (%), plasticity index (%), liquidity index (%), and the Cv for generating datasets for modeling. Performance of the models was validated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Correlation Coefficient (R) methods. In the present study, various combinations of soil parameters were applied and eight models were developed using RF algorithm for predicting the Cv of soft soil. Results: Results of model’s study show that performance of the models using different combinations of input factors is much different where R value varies from 0.715 to 0.822. Conclusion: Present study suggested that RF model with appropriate combination of soil properties input factors can help in better and accurate prediction of the Cv of soft soil.

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Section: Introductionmentioning

confidence: 99%

Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis

Nguyen¹,

Pham²,

Tuyen³

et al. 2019

TOBCTJ

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“…In a previous work [19] concerning the comparative study of the same steel grades with and without surface nitrocarburizing surface treatment, it was demonstrated that Tufftriding prevents galling failure (Figure 8), i.e., motion suspension of a metal-metal tribopair due to strong adhesion phenomena having occurred at the contact surface during dry sliding, typically observed in the case of sliding bearings. Moreover, nitrocarburizing protects the metallic surface from extensive oxidation (Figure 9a) that can lead to secondary tribo-corrosion mechanisms that would increase the wear rate of the metallic component.…”

Section: Degradation Of Nitriding Layers and Acceleration Of Base Metmentioning

confidence: 96%

An Introduction to Wear Degradation Mechanisms of Surface-Protected Metallic Components

Psyllaki

2019

Metals

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Despite the fact that ceramics and polymers have found numerous applications in several mechanical systems, metals and metallic alloys still remain the main materials family for manufacturing the bulk of parts and components of engineering assemblies. However, in cases of components that are serving as parts of a tribosystem, the application of surface modification techniques is required to ensure their unhampered function during operation. After a short introduction on fundamental aspects of tribology, this review article delves further into four representative case studies, where the inappropriate application of wear protection techniques has led to acceleration of the degradation of the quasi-protected metallic material. The first deals with the effects of the deficient lubrication of rolling bearings designed to function under oil lubrication conditions; the second is focused on the effects of overloading on sliding bearing surfaces, wear-protected via nitrocarburizing; the third concerns the application of welding techniques for producing hardfacing overlayers intended for the wear protection of heavily loaded, non-lubricated surfaces; the fourth deals with the degradation of thermal-sprayed ceramic coatings, commonly used as wear-resistant layers.

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“…In most cases, the hidden layer performs a nonlinear transformation in order to capture the nonlinear behavior between the input and output variables of the considered problem [72][73][74][75]. In this study, the well-known sigmoid function was employed as the nonlinear transformation of the signal for a given neuron in the hidden layer as follows [76][77][78][79][80]:…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…given neuron in the hidden layer as follows [76][77][78][79][80] are the received signals coming from the previous neurons and y is the output signal of the considered neuron. In the output layer, a linear transformation function is applied to calculate the response of the prediction problem, which is the compressive strength of HPC in this study.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

et al. 2020

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This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadratic) and an Artificial Neural Network (ANN) using a Monte Carlo simulation for prediction of High-Performance Concrete (HPC) compressive strength. To this purpose, 1030 samples were collected, including eight input parameters (contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregates, fine aggregates, and concrete age) and an output parameter (the compressive strength) to generate the training and testing datasets. The proposed AI models were validated using several standard criteria, namely coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To analyze the sensitivity and robustness of the models, Monte Carlo simulations were performed with 500 runs. The results showed that the GPR using the Matern32 kernel function outperforms others. In addition, the sensitivity analysis showed that the content of cement and the testing age of the HPC were the most sensitive and important factors for the prediction of HPC compressive strength. In short, this study might help in selecting suitable AI models and appropriate input parameters for accurate and quick estimation of the HPC compressive strength.

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Surface treatment of tool steels against galling failure

Cited by 44 publications

References 11 publications

Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis

Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis

An Introduction to Wear Degradation Mechanisms of Surface-Protected Metallic Components

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

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