Machine learning-based model of surface tension of liquid metals: a step in designing multicomponent alloys for additive manufacturing

Assi, Mariam; Favre, Jean-Michel; Frączkiewicz, A.; Tancret, Franck

doi:10.1007/s10853-022-07441-z

Cited by 5 publications

(3 citation statements)

References 104 publications

(4 reference statements)

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“…Given the complexity, few studies have developed process-structure-property relations through physicbased [115][116][117] or data-driven models. [118][119][120][121] AM has a greater capacity to make components with complicated geometries, more operational flexibility, and shorter production times than traditional processes. However, severe problems related to the mechanical properties and surface quality of AM processes also exist.…”

Section: Future Research Pathsmentioning

confidence: 99%

A Review on Vibration Characteristics of Additively Manufactured Metal Alloys

Azher,

Shah,

Bakhtari

et al. 2024

Adv Eng Mater

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The advent of additive manufacturing (AM) has dramatically shifted the manufacturing sector conceptualization, design, and creation of products. AM is poised to revolutionize goods production by establishing a new manufacturing paradigm, from reducing lead times to enabling rapid prototyping. AM can facilitate the production of complicated geometries and create functioning components with distinctive features for aerospace and automotive applications. However, defects such as pores, voids, interfaces, and inclusions can impair the quality and functionality of AM components. Vibration Analysis (VA) has become a popular tool for the dynamic qualification and testing of products and non‐destructive testing, but the literature lacks a comprehensive review of VA applied to AM. Hence, this paper summarizes recent advances in the application of VA for identifying and characterizing flaws in metal alloys, including titanium, aluminum, and nickel‐based alloys produced by AM. The review also includes studies on defects, such as porosity, cracks, and inclusions, and their effect on VA. The article concludes with a discussion of the limitations of VA for defect characterization and future research directions. Overall, VA is a promising non‐destructive testing method for quality assurance in AM and offers insights on overcoming the difficulties for further development and application of this technology.This article is protected by copyright. All rights reserved.

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Section: Future Research Pathsmentioning

confidence: 99%

A Review on Vibration Characteristics of Additively Manufactured Metal Alloys

Azher,

Shah,

Bakhtari

et al. 2024

Adv Eng Mater

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“…Surface tension of liquid metal alloys consisting of one or more elements from a pool of 22 metals was successfully modelled using Gaussian process regression Figure 7 (a-b). [104] High accuracy was achieved for binary and ternary systems and trends in the surface tension values as a function of alloy composition were identified. However, the use of the obtained model to predict the surface tension of new alloys remains a challenging task.…”

Section: Artificial Intelligence In Liquid Alloy Catalysismentioning

confidence: 99%

Liquid Metal Alloy Catalysis – Challenges and Prospects

Ameen,

Jabbar,

Yang

et al. 2023

ChemCatChem

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Significant progress has been made in recent years in the development of liquid metal alloy catalysts. This article provides an overview of the state‐of‐the‐art research pertaining to liquid metal alloy catalysis, including alloy synthesis, reactor design, and theoretical calculations. Different alloy synthesis methods are discussed with a focus on strategies that can achieve colloidal intermetallic structures in liquid metal alloys. Current reactors for liquid metal‐based electrocatalytic and thermochemical processes are summarized. The application of theoretical tools, such as machine learning and computational chemistry to further liquid metal alloy design, is discussed. Finally, an outlook on the technological challenges and our perspective on future research opportunities for liquid metal alloy catalysis is presented.

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“…In another work, Gaussian process regression was done to predict the surface tension of multicomponent metallic systems. Still, it did not incorporate the gallium-based system we have targeted here [ 53 ]. Least-squares support vector machine was found to perform better among three different optimization methods that were used to predict the surface tension of pure alcohol [ 54 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys

Hossain,

Kamran,

Tavakkoli

et al. 2023

J. Phys. Mater.

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Pendant drops of oxide-coated high-surface tension fluids frequently produce perturbed shapes that impede interfacial studies. Eutectic gallium indium (eGaIn) or gallium indium tin (Galinstan) are high-surface tension fluids coated with a ~5nm gallium oxide (Ga2O3) film and falls under this fluid classification, also known as liquid metals (LM). The recent emergence of LM-based applications often cannot proceed without analyzing interfacial energetics in different environments. While numerous techniques are available in the literature for interfacial studies- pendant droplet-based analyses are the simplest. However, the perturbed shape of the pendant drops due to the presence of surface oxide has been ignored frequently as a source of error. Also, exploratory investigations of surface oxide leveraging oscillatory pendant droplets have remained untapped. We address both challenges and present two contributing novelties- (a) by utilizing the machine learning technique, we predict the approximate surface tension value of perturbed pendant droplets, (ii) by leveraging the oscillation-induced bubble tensiometry method, we study the dynamic elastic modulus of the oxide-coated LM droplets. We have created our dataset from LM's pendant drop shape parameters and trained different models for comparison. We have achieved >99% accuracy with all models and added versatility to work with other fluids. The best-performing model was leveraged further to predict the approximate values of the nonaxisymmetric LM droplets. Then, we analyzed LM's elastic and viscous moduli in air, harnessing oscillation-induced pendant droplets, which provides complementary opportunities for interfacial studies alternative to expensive rheometers. We believe it will enable more fundamental studies of the oxide layer on LM, leveraging both symmetric and perturbed droplets. Our study broadens the materials science horizon, where researchers from machine learning and artificial intelligence domains can work synergistically to solve more complex problems related to surface science, interfacial studies, and other studies relevant to LM-based systems.

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Machine learning-based model of surface tension of liquid metals: a step in designing multicomponent alloys for additive manufacturing

Cited by 5 publications

References 104 publications

A Review on Vibration Characteristics of Additively Manufactured Metal Alloys

A Review on Vibration Characteristics of Additively Manufactured Metal Alloys

Liquid Metal Alloy Catalysis – Challenges and Prospects

Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys

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