On the Relationship between Structural Quality Index and Fatigue Life Distributions in Aluminum Aerospace Castings

Özdeş, Hüseyin; Tiryakioğlu, Murat

doi:10.3390/met6040081

Cited by 11 publications

(5 citation statements)

References 28 publications

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“…Especially in transportation, fatigue is one of the main concerns for the utilization of aluminum alloys as aircraft structures. Historical records show that more than 50% of aerospace accidents are caused mainly by material fatigue failure (Findlay and Harrison 2002, Özdes ¸and Tiryakiȏglu 2016, Younis et al 2022. In contrast to steel, the fatigue strength of forged aluminum alloys is disproportionate to their static strengths, such as tensile strength, hardness, etc.…”

Section: Introductionmentioning

confidence: 99%

Composition-based aluminum alloy selection using an artificial neural network

Fatriansyah

Rizqillah

Suhariadi

et al. 2023

Modelling Simul. Mater. Sci. Eng.

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Materials selection for aluminum alloys with desired fatigue and other mechanical properties is very difficult. Usually, when fatigue properties are maximized, other mechanical properties should be compromised. In this paper, an artificial neural network was utilized to build two prediction models that has the purpose of predicting fatigue life from composition and inverse design to predict composition from fatigue properties as a tool for materials selection. A first model was built to predict fatigue life using information on alloy composition, heat treatment, and other mechanical properties. The second model is an inversion of the first model, which predicts the material compositions to get the desired fatigue performance and other mechanical properties. Both models produce good performances based on the R2 scoring metric, where the values were found to be 0.92 and 0.96 for the first and second models, respectively. This study proved that the inversion model for predicting composition based on fatigue properties can reach acceptable accuracy and can be used as a materials selection tool. In addition, to investigate how atomic properties can affect fatigue life, the third model was built. It was found that atomic properties, such as electronegativity and the radius of alloying elements, are closely related to fatigue life and can be used to predict fatigue life as well. The significance of our work is that users can design new alloys for specific applications as well as select available alloys based on fatigue property criteria.

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Section: Introductionmentioning

confidence: 99%

Composition-based aluminum alloy selection using an artificial neural network

Fatriansyah

Rizqillah

Suhariadi

et al. 2023

Modelling Simul. Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…In fact, this is also the ultimate goal in the field of materials science. To date, many approaches are available for theoretical alloy design, such as first-principles (FP) calculations [39] , molecular dynamics simulations [40] , computational thermodynamics (CT) [41][42][43] , computational kinetics [44][45][46][47][48][49][50][51] in the framework of CALculation of PHAse Diagram (CALPHAD) technique, phase-field simulations [52][53][54][55][56] , machine learning (ML) approach [57][58][59][60] , and other empirical/semi-empirical formulas [61][62][63] . Among them, CT in the framework of CALPHAD technique and data-driven ML method is the most advantageous approach for multicomponent alloy design.…”

Section: Introductionmentioning

confidence: 99%

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Wang¹,

Liu²,

Gao³

et al. 2021

JMI

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Casting aluminum alloys are commonly used in industries due to their excellent comprehensive performance. Alloying/microalloying and post-solidification heat treatments are the most common measures to tune the microstructure for enhancing their mechanical properties. However, it is very challenging to achieve accurate and efficient development of novel casting aluminum alloys using the traditional trial-and-error method. With the rapid development of computer technology, the computational thermodynamics (CT) in the framework of the CALculation of PHAse Diagram approach, the data-driven machine learning (ML) technique, and also their combinations have been proved to be effective approaches for the design of casting aluminum alloys. In this review, the state-of-the-art computational alloy design approaches driven by CT and ML techniques, as well as their combinations, were comprehensively summarized. The current status of the thermodynamic database for aluminum alloys, as the core for CT, was also briefly introduced. After that, a variety of successful case studies on the design of different casting aluminum alloys driven by CT, ML, and their combinations were demonstrated, including common applications, CT-driven design of Sc-additional Al-Si-Mg series casting alloys, and design of Srmodified A356 alloys driven by combing CT and ML. Finally, the conclusions of this review were drawn, and perspectives for boosting the computational design approach driven by combining CT and ML techniques were pointed out.

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“…Development of the aerospace industry and automobile industry, requiring light weight, high reliability, and good dimensional stability, promotes the application of carbon fiber-reinforced plastics (CFRPs) and light metal materials, such as Al alloy [1][2][3][4]. Joining of CFRP and Al alloy, encountered commonly, has become a critical issue.…”

Section: Introductionmentioning

confidence: 99%

Effect of Ultrasonic Vibration on Adhesive Bonding of CFRP/Al Alloy Joints Grafted with Silane Coupling Agent

Tong

Wang

et al. 2020

Polymers

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Adhesive bonding is widely used in the joining of metals and carbon fiber-reinforced plastics (CFRPs). Ultrasonic vibration was used to improve adhesive bonding of CFRP/Al alloy joints grafted with silane coupling agent, and the effect of the ultrasound on the bonding was studied. The surface of Al alloy was treated with a silane coupling agent, and then the ultrasonic vibration was applied on the adherend during the adhesive bonding process. The shear strength was tested, and the mechanism was analyzed by scanning electron microscope (SEM) and Fourier transform infrared spectrometer (FTIR). It is found that the ultrasonic assisting can further promote the bonding of the Al alloy and the adhesive. For the test joins, the shear strength was increased by 267.50% using the silanization treatment plus the ultrasonic assisting. The ultrasonic assisting promoted the grafted epoxy group to react with the adhesive more sufficiently at the Al/adhesive interface by causing micro-mixing and intensified molecule collision, and thus more chemical bond was formed. Under the ultrasonic action, the interface and the adhesive layer became tighter owing to the impact contact at the interface and the oscillating flow in the adhesive layer. The ultrasonic vibration assisting increased the bonding strength by promoting the chemical bond and improving physical morphology.

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On the Relationship between Structural Quality Index and Fatigue Life Distributions in Aluminum Aerospace Castings

Cited by 11 publications

References 28 publications

Composition-based aluminum alloy selection using an artificial neural network

Composition-based aluminum alloy selection using an artificial neural network

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Effect of Ultrasonic Vibration on Adhesive Bonding of CFRP/Al Alloy Joints Grafted with Silane Coupling Agent

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