A survey of electromagnetic metal casting computation designs, present approaches, future possibilities, and practical issues

Raj, Aditya; Kishore, S. Ram; Jose, Lanz; Karn, Atul Kumar; Chadha, Utkarsh; Selvaraj, Senthil Kumaran

doi:10.1140/epjp/s13360-021-01689-1

Cited by 21 publications

(4 citation statements)

References 63 publications

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“…ANN model is the best model, but combining any other model of artificial intelligence technology will make it much better, making it easy to get accurate results. Finally, we can say that the ANN model with optimization algorithms is a powerful tool in metal melting processes [90][91][92][93].…”

Section: Discussionmentioning

confidence: 99%

RETRACTED: AI-driven techniques for controlling the metal melting production: a review, processes, enabling technologies, solutions, and research challenges

Chadha

Selvaraj

Raj

et al. 2022

Mater. Res. Express

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Artificial Intelligence has left no stone unturned, and mechanical engineering is one of its biggest consumers. Such technological advancements in metal melting can help in process simplification, hazard reduction, human involvement reduction & lesser process time. Implementing the AI models in the melting technology will ultimately help various industries, i.e., Foundry, Architecture, Jewelry Industry, etc. This review extensively sheds light on Artificial Intelligence models implemented in metal melting processes or the metal melting aspect, alongside explaining additive manufacturing as a competitor to the current melting processes and its advances in metal melting and AI implementations.

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Section: Discussionmentioning

confidence: 99%

RETRACTED: AI-driven techniques for controlling the metal melting production: a review, processes, enabling technologies, solutions, and research challenges

Chadha

Selvaraj

Raj

et al. 2022

Mater. Res. Express

Self Cite

View full text Add to dashboard Cite

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“…Tus, ANNs are seen to be versatile, thus being helpful in this procedure [112][113][114][115][116]. Most of the results obtained using this mechanism are also promising, supported by various papers [64,65] showing the model's accuracy varying in the range of 90's in percentages [109,117,118].…”

Section: Discussionmentioning

confidence: 85%

“…Out of these, the most signifcant algorithm that stands out comparatively regarding metal melting procedures is the ANN. Since it is an artifcial mimic of the biological brain, it can capture and process complex relations between parameters efortlessly due to the numerous interconnections between nodes [78,[109][110][111]. ANNs see applications in several areas in this procedure as described in [54], including process parameter optimization, in situ observation, control of part geometry, tailoring microstructure and properties, and reduced defects.…”

Section: Discussionmentioning

confidence: 99%

Powder Bed Fusion via Machine Learning-Enabled Approaches

et al. 2023

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Powder bed fusion (PBF) applies to various metallic materials used in the metal printing process of building a wide range of complex parts compared to other AM technologies. PBF process has several variants such as DMLS (direct metal laser sintering), EBM (electron beam melting), SHS (selective heat sintering), SLM (selective laser melting), and SLS (selective laser sintering). For PBF to reach its maximum potential, machine learning (ML) algorithms are used with suitable materials to achieve goals cost-effectively. Various applications of neural networks, including ANNs, CNNs, RNNs, and other popular techniques such as KNN, SVM, and GP were reviewed, and future challenges were discussed. Some special-purpose algorithms were listed as follows: GAN, SeDANN, SCNN, K-means, PCA, etc. This review presents the evolution, current status, challenges, and prospects of these technologies in terms of material, features, process parameters, applications, advantages, disadvantages, etc., to explain their significance and provide an in-depth understanding of the same.

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“…ese, too, are depicted as a flowchart in the part of the discussion. Finally, to improve the quality of the components, the building parameters such as feeding rate, scan feed, and laser power are vital factors [41,[95][96][97][98].…”

Section: Discussionmentioning

confidence: 99%

Directed Energy Deposition via Artificial Intelligence‐Enabled Approaches

et al. 2022

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Additive manufacturing (AM) has been gaining pace, replacing traditional manufacturing methods. Moreover, artificial intelligence and machine learning implementation has increased for further applications and advancements. This review extensively follows all the research work and the contemporary signs of progress in the directed energy deposition (DED) process. All types of DED systems, feed materials, energy sources, and shielding gases used in this process are also analyzed in detail. Implementing artificial intelligence (AI) in the DED process to make the process less human-dependent and control the complicated aspects has been rigorously reviewed. Various AI techniques like neural networks, gradient boosted decision trees, support vector machines, and Gaussian process techniques can achieve the desired aim. These models implemented in the DED process have been trained for high-precision products and superior quality monitoring.

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A survey of electromagnetic metal casting computation designs, present approaches, future possibilities, and practical issues

Cited by 21 publications

References 63 publications

RETRACTED: AI-driven techniques for controlling the metal melting production: a review, processes, enabling technologies, solutions, and research challenges

RETRACTED: AI-driven techniques for controlling the metal melting production: a review, processes, enabling technologies, solutions, and research challenges

Powder Bed Fusion via Machine Learning-Enabled Approaches

Directed Energy Deposition via Artificial Intelligence‐Enabled Approaches

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