A Novel Fluid–Structure Interaction (FSI) Modeling Approach to Predict the Temperature Distribution in Single-Point Cutting Tool for Condition Monitoring During Turning Process

Mohanta, Neha; Singh, Rabesh Kumar; Katiyar, Jitendra Kumar; Sharma, Anuj Kumar

doi:10.1007/s13369-021-05861-8

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Pre-trained models are architectures previously trained on an extensive data set, and then the weights of these models are transferred and trained on a downstream task. For example, a popular deep convolutional neural network called ResNet [ 75 , 76 ] has been trained on 3.2 million ImageNet [ 52 ] images and have been used for many downstream tasks such as, predicting fluid flow [ 77 ], hydraulic fracture [ 78 ], and fluid-structure interaction [ 79 ]. Similar architectures with residual blocks have seen many adoptions in challenging downstream tasks such as image-to-image translation, image inpainting, and semantic segmentation [ 80 , 81 ].…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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AI for tribology: Present and future

Yin,

Yang,

Liu

et al. 2024

Friction

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With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of “tribo-informatics” as a novel interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of “AI for tribology”. In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (Ts), and derived state information (Ds). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology.

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Precise measurement of geometric and physical quantities in cutting tools inspection and condition monitoring: A review

WANG,

LIU,

ZHANG

et al. 2024

Chinese Journal of Aeronautics

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A Novel Fluid–Structure Interaction (FSI) Modeling Approach to Predict the Temperature Distribution in Single-Point Cutting Tool for Condition Monitoring During Turning Process

Cited by 4 publications

References 19 publications

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

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

AI for tribology: Present and future

Precise measurement of geometric and physical quantities in cutting tools inspection and condition monitoring: A review

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