Application of &lt;i&gt;Niyama&lt;/i&gt; criterion to predict shrinkage porosity in vertical centrifugal casting of ASTM A356 alloy

Bhoraniya, Divya; Dharaiya, Vishesh; Sata, Amit

doi:10.1504/ijpmb.2022.122231

Cited by 3 publications

(1 citation statement)

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“…Vertical centrifugal casting utilizes continuous rotation of a mould to pour molten metal, allowing it to spread along the inner wall and solidify upon cooling. Quality and uniformity of the final products depend on crucial process parameters [1], analysed and optimized by manufacturers through numerical simulation, particularly the finite element method. This simulation accurately predicts deformations and residual stresses.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the process parameters for vertical centrifugal casting of A356 by numerical simulation

Kumar Azad,

Mazloum,

Khandelwal

et al. 2024

Eng. Res. Express

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This paper investigates the influence of critical process parameters on the centrifugal casting of A356 (Al-Si) alloys using a casting simulation tool. Effects of mould rotation, pouring temperature, mould aspect ratio, and heat transfer coefficient on filling and solidification behaviours have been studied. Furthermore, the study employs Grey Relation Analysis, Taguchi, and ANOVA to systematically optimize the casting process parameters, addressing uncertainties and identifying statistically significant factors. Process optimization, supported by numerical simulations, has revealed that the optimal configuration (rotation of 150 rpm, aspect ratio as 2, pouring temperature as 775°C) minimizes stress and strain (maximum shear stress, average normal stress, principal stress, principal strain) that can be considered to be responsible for the occurrence of defects such as cracks in final castings. Controlling these process parameters will help in reducing overall stresses and in turn reducing defects as well as improvement of overall productivity.

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Section: Introductionmentioning

confidence: 99%

Optimization of the process parameters for vertical centrifugal casting of A356 by numerical simulation

Kumar Azad,

Mazloum,

Khandelwal

et al. 2024

Eng. Res. Express

Self Cite

View full text Add to dashboard Cite

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Digital twinning of vertical centrifugal casting

Anadkat,

Dave,

Sata

et al. 2024

Concurrent Engineering

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This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation.

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Machine learning-based study of dynamic shrinkage behavior during solidification of castings

Zhang Tong,

田帅,

孙旭冉

et al. 2025

Acta Phys. Sin.

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Shrinkage cavities and porosity are the main defects generated during the solidification process of castings. The cause of these defects is the contraction of the alloy during solidification, with the last regions to solidify not receiving effective compensation from liquid metal, resulting in cavitation defects. Shrinkage cavities and porosity significantly reduce the mechanical properties of castings and shorten their service life, thus necessitating appropriate process elimination measures. Utilizing numerical simulation technology can effectively predict the shrinkage of castings during solidification and optimize the process based on simulation results, thereby reducing the occurrence of shrinkage defects, which is a low-cost and high-efficiency method. This paper presents a machine learning-driven dynamic mesh model to simulate the dynamic shrinkage behavior of castings during solidification. Cellular automata are used to simulate the solidification process of castings, dynamically marking the displacement of boundary points and calculating the displacement of other grids using RBF neural network algorithms and support vector machine algorithms, achieving dynamic simulation of the solidification process. The model was used to simulate the shrinkage cavity morphology of the Al-4.7%Cu alloy solidification process and corresponding casting experiments were designed for verification. Comparisons between simulation and experimental results indicate that this coupled method can effectively capture the deformation of castings caused by solidification shrinkage, the evolution of complex solid-liquid interface morphologies, and the deformation of internal grids within the castings. The simulation results have an error of no more than 2% compared to experimental results, providing a new approach for numerical simulation of the solidification process.

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Application of <i>Niyama</i> criterion to predict shrinkage porosity in vertical centrifugal casting of ASTM A356 alloy

Cited by 3 publications

References 0 publications

Optimization of the process parameters for vertical centrifugal casting of A356 by numerical simulation

Optimization of the process parameters for vertical centrifugal casting of A356 by numerical simulation

Digital twinning of vertical centrifugal casting

Machine learning-based study of dynamic shrinkage behavior during solidification of castings

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