Fatigue Life Prediction of Ductile Iron Based on DE-SVM Algorithm

Yiqun, Ma; Wang, Xiaoping; lun, An

doi:10.1016/j.phpro.2012.05.216

Cited by 7 publications

(3 citation statements)

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“…When searching for data on fatigue strength, in particular on the fatigue strength of ductile iron, a lot of information can be found in the technical literature [3][4][5][6][7][8][9][10]. Various mechanisms related to the fatigue phenomenon, including microstructural conditions, are described [3][4][5], and the onset and propagation of fatigue cracks are related to the microstructure of cast iron metal matrix and morphological features of graphite precipitates [6][7][8].…”

Section: Low Cycle Fatigue Test (Lcf) Vs Modified Low Cycle Fatigue T...mentioning

confidence: 99%

“…Various mechanisms related to the fatigue phenomenon, including microstructural conditions, are described [3][4][5], and the onset and propagation of fatigue cracks are related to the microstructure of cast iron metal matrix and morphological features of graphite precipitates [6][7][8]. Some of the studies also include the developed theoretical models predicting fatigue life at different temperatures [9][10]. Generally speaking, however, fatigue tests performed by the conventional low-cycle fatigue (LCF) method [11]) require at least 10 samples for testing, since the measurement data obtained from a single sample give only one point on the fatigue curve.…”

Section: Low Cycle Fatigue Test (Lcf) Vs Modified Low Cycle Fatigue T...mentioning

confidence: 99%

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Archives of Foundry Engineering

Maj

Pietrzak

Klasik

2019

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Section: Low Cycle Fatigue Test (Lcf) Vs Modified Low Cycle Fatigue T...mentioning

confidence: 99%

Section: Low Cycle Fatigue Test (Lcf) Vs Modified Low Cycle Fatigue T...mentioning

confidence: 99%

Archives of Foundry Engineering

Maj

Pietrzak

Klasik

2019

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“…In manufacturing, we review here two very recent works dealing with hybrid SVM approaches: in Ref a problem of high‐technology equipment fabrication prediction is tackled with an evolutionary SVM and in Ref a problem of gears classification is solved by means of another evolutionary SVM algorithm. In construction, different hybrid approaches have been recently proposed; evolutionary SVM, differential evolution–SVM, particle swarm–SVM, and artificial bee colonies algorithms–SVM have been proposed to different problems of seismic assessment of buildings, fatigue prediction in materials, or leak location and detection in pipelines . Finally, in power and electrical applications, it is possible to find several recent hybrid SVM approaches, such as in Ref , where an evolutionary SVM is applied to the optimization of power quality disturbances in electric networks; in Ref , where an evolutionary SVM is applied to transient stability monitoring in power networks; and finally in Ref , where a problem of power load forecast is tackled with a hybrid PSO–SVM approach.…”

Section: Constructing Novel Algorithms Based On Support Vector Machinesmentioning

confidence: 99%

Support vector machines in engineering: an overview

Salcedo‐Sanz

Rojo-Álvarez

Martínez‐Ramón

et al. 2014

WIREs Data Min & Knowl

169

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This paper provides an overview of the support vector machine (SVM) methodology and its applicability to real‐world engineering problems. Specifically, the aim of this study is to review the current state of the SVM technique, and to show some of its latest successful results in real‐world problems present in different engineering fields. The paper starts by reviewing the main basic concepts of SVMs and kernel methods. Kernel theory, SVMs, support vector regression (SVR), and SVM in signal processing and hybridization of SVMs with meta‐heuristics are fully described in the first part of this paper. The adoption of SVMs in engineering is nowadays a fact. As we illustrate in this paper, SVMs can handle high‐dimensional, heterogeneous and scarcely labeled datasets very efficiently, and it can be also successfully tailored to particular applications. The second part of this review is devoted to different case studies in engineering problems, where the application of the SVM methodology has led to excellent results. First, we discuss the application of SVR algorithms in two renewable energy problems: the wind speed prediction from measurements in neighbor stations and the wind speed reconstruction using synoptic‐pressure data. The application of SVMs in noninvasive cardiac indices estimation is described next, and results obtained there are presented. The application of SVMs in problems of functional magnetic resonance imaging (fMRI) data processing is further discussed in the paper: brain decoding and mental disorder characterization. The following application deals with antenna array processing, namely SVMs for spatial nonlinear beamforming, and the SVM application in a problem of arrival angle detection. Finally, the application of SVMs to remote sensing image classification and target detection problems closes this review. WIREs Data Mining Knowl Discov 2014, 4:234–267. doi: 10.1002/widm.1125 This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning

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Illumination correction model with sine‐cosine algorithm to optimize gray wolf population for least‐squares support vector regression

Yang

Zhao

Chen

2021

Color Research & Application

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The color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA-GWO-LSSVR) based on the sine-cosine algorithm (SCA) and the gray wolf optimization (GWO) algorithm to optimize the regression prediction model of the least-squares support vector regression (LSSVR) algorithm. The performance of the traditional LSSVR is significantly affected by the penalty parameter (gamma) and the sig2 kernel function parameter. The proposed method uses the improved GWO algorithm to search the population to find the best LSSVR parameter solution. The proposed algorithm uses the SCA to create multiple random candidate solutions in population initialization to avoid blind initialization of the GWO algorithm. In the process of iterative optimization, the SCA is infiltrated, and its sine-cosine wave mathematical model is used to quickly identify the best outward or inward position of the gray wolf. Finally, the LSSVR combines the optimal sig2 kernel function parameters and penalty parameters (gamma) to obtain a highly versatile illumination correction model. The experimental results show that the fitting accuracy of the proposed method reaches 86.8%, which is 5% higher than that of the LSSVR algorithm alone.

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Fatigue Life Prediction of Ductile Iron Based on DE-SVM Algorithm

Cited by 7 publications

References 1 publication

Archives of Foundry Engineering

Archives of Foundry Engineering

Support vector machines in engineering: an overview

Illumination correction model with sine‐cosine algorithm to optimize gray wolf population for least‐squares support vector regression

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