Optical tomography in additive manufacturing: a review, processes, open problems, and new opportunities

Dharnidharka, Mohit; Chadha, Utkarsh; Dasari, Lohitha Manya; Paliwal, Aarunya; Surya, Yash; Selvaraj, Senthil Kumaran

doi:10.1140/epjp/s13360-021-02108-1

Cited by 24 publications

(2 citation statements)

References 62 publications

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“…is is proven for two types of tool steels -H13 and D2 in the recent research works conducted, which is also mentioned in the process parameters section [99][100][101][102][103][104].…”

Section: Discussionmentioning

confidence: 83%

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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“…is is proven for two types of tool steels -H13 and D2 in the recent research works conducted, which is also mentioned in the process parameters section [99][100][101][102][103][104].…”

Section: Discussionmentioning

confidence: 83%

Directed Energy Deposition via Artificial Intelligence‐Enabled Approaches

et al. 2022

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“…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. 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: 84%

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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