Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions

Karolczuk, Aleksander; Skibicki, Dariusz; Pejkowski, Łukasz

doi:10.3390/ma15217797

Cited by 9 publications

(2 citation statements)

References 75 publications

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“…However, it's noteworthy that the authors believe the linear model adequately describes the experimental results (see Figure 12). This is supported by the fact that a scatter factor of less than three is commonly accepted, 66–68 and a value less than or equal to two indicates a good estimation 69,70 . Apart from the prediction‐experiment diagram (Figure 12), other diagrams concerning fatigue performance might be utilized, taking into account the specific characteristics of crack sources (see, e.g., previous papers 20,41 ).…”

Section: Resultsmentioning

confidence: 91%

Mechanical properties of additively manufactured AlSi10Mg under quasi‐static and cyclic loading

Yankin,

Seisekulova,

Perveen

et al. 2024

Fatigue Fract Eng Mat Struct

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This study examines the effects of minor variations in the building angle of 3D‐printed AlSi10Mg components on mechanical properties under different processing and postprocessing conditions (as‐built [AB], stress relief [SR], and platform heating [HP]). Beginning with tensile tests to determine stress ranges for subsequent fatigue experiments, findings indicate that the building angle significantly impacts tensile strength, yield strength, and ultimate strain, rising with inclining angles. However, Young's modulus remains unaffected. High‐cycle fatigue testing at stress amplitudes below yield strength values reveals differences in S‐N fatigue curves among AB, SR, and HP conditions. While the building angle's influence is not statistically significant, AB samples exhibit lower fatigue resistance than SR and HP counterparts. HP samples perform similarly to SR at high stress levels but notably worse at low stress values. The developed multiple linear regression model shows reasonable accuracy, with 84% of predictions within ±2‐factor and 97.2% within ±3‐factor.

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Section: Resultsmentioning

confidence: 91%

Mechanical properties of additively manufactured AlSi10Mg under quasi‐static and cyclic loading

Yankin,

Seisekulova,

Perveen

et al. 2024

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) methods have advanced quickly in recent years, and their theories and techniques have been widely utilized to tackle challenging issues in a variety of engineering and scientific domains [2][3][4][5][6][7][8]. Researchers have been driven to apply ANN models and optimization methods to address a variety of civil engineering issues due to the growth of ML techniques [9].…”

Section: Introductionmentioning

confidence: 99%

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Wang,

Du,

Jia

et al. 2024

Materials

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Due to its exceptional qualities, ultra-high-performance concrete (UHPC) has recently become one of the hottest research areas, although the material’s significant carbon emissions go against the current development trend. In order to lower the carbon emissions of UHPC, this study suggests a machine learning-based strategy for optimizing the mix proportion of UHPC. To accomplish this, an artificial neural network (ANN) is initially applied to develop a prediction model for the compressive strength and slump flow of UHPC. Then, a genetic algorithm (GA) is employed to reduce the carbon emissions of UHPC while taking into account the strength, slump flow, component content, component proportion, and absolute volume of UHPC as constraint conditions. The outcome is then supported by the results of the experiments. In comparison to the experimental results, the research findings show that the ANN model has excellent prediction accuracy with an error of less than 10%. The carbon emissions of UHPC are decreased to 688 kg/m3 after GA optimization, and the effect of optimization is substantial. The machine learning (ML) model can provide theoretical support for the optimization of various aspects of UHPC.

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Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool

Adizue,

Tura,

Isaya

et al. 2023

Int J Adv Manuf Technol

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High-quality machining is a crucial aspect of contemporary manufacturing technology due to the vast demand for precision machining for parts made from hardened tool steels and super alloys globally in the aerospace, automobile, and medical sectors. The necessity to upheave production efficiency and quality enhancement at minimum cost requires deep knowledge of this cutting process and development of machine learning-based modeling technique, adept in providing essential tools for design, planning, and incorporation in the machining processes. This research aims to develop a predictive surface roughness model and optimize its process parameters for ultra-precision hard-turning finishing operation. Ultra-precision hard-turning experiments were carried out on AISI D2 of HRC 62. The response surface method (RSM) was applied to understand the effect of process parameters on surface roughness and carry out optimization. Based on the data gained from experiments, machine learning models and algorithms were developed with support vector machine (SVM), Gaussian process relation (GPR), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) for the prediction of surface roughness. The results show that all machine learning models gave excellent predictive accuracy with an average MAPE value of 7.38%. The validation tests were also statistically significant, with ANFIS and ANN having MAPE values of 9.98% and 3.43%, respectively. Additional validation tests for the models with new experimental data indicate average R, RMSE, and MAPE values of 0.78, 0.19, and 36.17%, respectively, which are satisfactory. The RSM analysis shows that the feed is the most significant factor for minimizing surface roughness Rɑ, among the process parameters, with 92% influence, and optimal cutting conditions were found to be cutting speed = 100 m/min, feed = 0.025 mm/rev, and depth of cut = 0.09 mm, respectively. This finding can be helpful in the decision-making on process parameters in the precision machining industry.

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Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions

Cited by 9 publications

References 75 publications

Mechanical properties of additively manufactured AlSi10Mg under quasi‐static and cyclic loading

Mechanical properties of additively manufactured AlSi10Mg under quasi‐static and cyclic loading

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool

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