AI-driven predictive modelling of residual stress of HVOF thermal sprayed carbon-based composite coatings using physics-informed neural networks.

Ankit Tyagi, Abhishek Dadhich, Sachin Sirohi, Amit Kumar Gupta

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Ankit TyagiDepartment of Mechanical Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India.

Abhishek DadhichDepartment of Computer Science Engineering, Poornima Institute of Engineering and Technology, Jaipur, Rajasthan, 302022, India.

Sachin SirohiDepartment of Mechanical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology Delhi NCR Campus, Modinagar, India. sachinsirohi2008@gmail.com.

Amit Kumar GuptaDepartment of Computer Science and Engineering, Faculty of Science, Technology and Architecture, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India. amit.gupta@jaipur.manipal.edu.

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6 · The paper itself

Abstract

The residual stress is an important factor affecting the performance, durability and failure behaviour of High Velocity Oxy-Fuel (HVOF) sprayed based composite coating of carbon. Finite element analysis (FEA) and other conventional numerical methods are computationally demanding and constrained by assumptions, whereas machine learning (ML) models without physical interpretation can be trained entirely on less assumptions and purely using only data. This paper recommends a combined Physics-Informed Neural Network (PINN) model to predict HVOF thermal sprayed carbon-based composite coatings residual stress distributions. The model incorporates herein governing thermo-mechanical equations, such as heat transfer and elastic plastic deformation as a direct part of the neural network loss. Training and validation are done using experimental and simulated datasets. The suggested PINN model achieves a better accuracy (R

Indexed as

AI modellingCarbon-based compositesPhysics-informed neural networksResidual stress

Identifiers

PMID42629348

DOI10.1038/s41598-026-61240-z

PMCPMC13498599

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