Application of Support Vector Regression on Mechanical Properties of Austenitic Stainless Steel 304 at Elevated Temperatures

Kanumuri, Lakshmi; Srishuka, M.; Gupta, Amit Kumar; Singh, Swadesh Kumar

doi:10.1016/j.matpr.2015.07.073

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Research has been conducted demonstrating the benefits of artificial intelligence in the field of materials to develop expert systems focused on data analysis and failure diagnosis [7][8][9]. Artificial neural networks have been present in stainless steel to solve classification, recognition, and estimation tasks.…”

Section: Bibliographic Reviewmentioning

confidence: 99%

Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing

Ruelas-Santoyo,

Ríos-Lira,

Pantoja-Pacheco

et al. 2024

Applied Sciences

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The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed.

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Section: Bibliographic Reviewmentioning

confidence: 99%

Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing

Ruelas-Santoyo,

Ríos-Lira,

Pantoja-Pacheco

et al. 2024

Applied Sciences

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“…Nonlinear mapping is used to map the sample set from low-dimensional space to high-dimensional space. This kind of nonlinear mapping can be defined as shown below [39]:…”

Section: Support Vector Regression Optimized By Slime Mould Algorithmmentioning

confidence: 99%

Improving Support Vector Regression for Predicting Mechanical Properties in Low-Alloy Steel and Comparative Analysis

Che,

Peng

2024

Mathematics

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Low-alloy steel is widely employed in the aviation industry for its exceptional mechanical properties. These materials are frequently used in critical structural components such as aircraft landing gear and engine mounts, where a high strength-to-weight ratio is crucial for optimal performance. However, the mechanical properties of low-alloy steel are influenced by various components and their compositions, making identification and prediction challenging. Accurately predicting these mechanical properties can significantly reduce the development time of new alloy steel, lower production costs, and offer valuable insights for design analysis. support vector regression (SVR) is known for its superior learning and generalization capabilities. However, optimizing SVR performance can be challenging due to the significant impact of the penalty factor and kernel parameters. To address this issue, a hybrid method called SMA-SVR is proposed, which combines the Slime Mould Algorithm (SMA) with SVR. This hybrid approach aims to efficiently and accurately predict two crucial mechanical parameters of low-alloy steel: tensile strength and 0.2% proof stress. Detailed descriptions of the modeling processes and principles that are involved in the hybrid method are provided. Furthermore, three other popular hybrid models for comparison are introduced. To evaluate the performance of these models, four statistical measures are utilized: Mean Absolute Error, Root Mean Square Error, R-Squared, and computational time. Using data from the NIMS database and from material tests conducted on a universal testing machine, experiments were carried out to compare the performance of these models. The results indicate that SMA-SVR outperforms the other methods in terms of accuracy and computational efficiency.

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“…The material and fatigue properties of ASTM 304 stainless steel were obtained from [14] and validated by [15]. The properties are shown in Table II.…”

Section: B Materials Selectionmentioning

confidence: 99%

Prediction of fatigue life of mistuned steam turbine blades subjected to variations in blade geometry

Mashiachidi,

Desai

2023

icat

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There is a large number of power stations suffering from fatigue failures of the steam turbine blades. The steam turbine blades are also subjected to steam flow bending, centrifugal loading, vibration response, and structural mistuning. These mentioned factors significantly contribute to the fatigue failure of the steam turbine blades. Low-Pressure (LP) steam turbines experience premature blade and disk failures due to the stress concentrations at the blade root area of its bladed disk. Driven by the problems encountered by the steam power plant electricity generating utilities with regards to steam turbine blades fatigue failure, this study of the mistuned steam turbine blades subjected to variation in blade geometry will be of great significance to the electricity generation industry. A simplified, scaled-down mistuned steam turbine bladed disk model was developed using ABAQUS finite element analysis (FEA) software. Acquisition of the vibration characteristics and steady-state stress response of the disk models was performed through FEA. Thereafter, numerical stress distributions were acquired, and the model was subsequently exported to Fe-Safe software for fatigue life calculations based on centrifugal and harmonic sinusoidal pressure loading. The vibration characteristics and the response of the variation steam turbine geometric blade was conducted. The FEA natural frequencies compared well with published literature of the real steam turbines indicating reliability of the developed FEA model. The study found that the fatigue life is most sensitive to changes in blade length, followed by the width, and then the thickness, in this order. The analytical life cycles and Fe-Safe software shows the percentage difference of less than 4.86%. This concludes that the developed numerical methodology can be used for real-life mistuned steam turbine blades subjected to variations in blade geometry.

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Application of Support Vector Regression on Mechanical Properties of Austenitic Stainless Steel 304 at Elevated Temperatures

Cited by 5 publications

References 7 publications

Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing

Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing

Improving Support Vector Regression for Predicting Mechanical Properties in Low-Alloy Steel and Comparative Analysis

Prediction of fatigue life of mistuned steam turbine blades subjected to variations in blade geometry

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