Directed Energy Deposition via Artificial Intelligence‐Enabled Approaches

Chadha, Utkarsh; Selvaraj, Senthil Kumaran; Lamsal, Aakrit Sharma; Maddini, Yashwanth; Ravinuthala, Abhishek Krishna; Choudhary, Bhawana; Mishra, Anirudh; Padala, Deepesh; Shashank, M.; Lahoti, Vedang; Adefris, Addisalem; Dhanalakshmi, S.

doi:10.1155/2022/2767371

Cited by 15 publications

(4 citation statements)

References 92 publications

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“…The melted material is deposited layer by layer in a controlled chamber at reduced oxygen levels [70]. This is important to prevent the oxidation of the melted material [71]. In certain cases, a shielding gas creates an inert environment during the printing of a part.…”

Section: Direct Energy Depositionmentioning

confidence: 99%

Sustainable considerations in additive manufacturing processes: A review

Ononiwu,

Ako,

Anyaoha

et al. 2024

J. Mech. Eng. Sci.

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Efficient waste management practices are becoming increasingly necessary due to the negative environmental and health impacts of waste generation and disposal. One type of waste that has received particular attention is electronic waste (e-waste). This category of waste has the potential to cause significant environmental harm if not disposed of properly. The management of e-waste is crucial in the electrical/electronic industry which has led to the creation of models and institutional legislature to promote sustainable production processes. Among these processes, Additive manufacturing otherwise referred to as 3D printing is particularly effective in reducing waste generation and energy requirements by reusing spent parts and products as feedstock. Sustainability in the manufacturing and production sectors can be promoted through the inculcation of certain practices. Of these practices, reusing e-waste that would otherwise be disposed of in landfills has the potential to promote environmental cost savings. This article introduces the potential of e-waste being integrated into the manufacturing sector to promote sustainable production. The article also addresses the problem of geometric e-waste generation and suggests an efficient way of reusing waste from the electrical and electronic industries.

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Section: Direct Energy Depositionmentioning

confidence: 99%

Sustainable considerations in additive manufacturing processes: A review

Ononiwu,

Ako,

Anyaoha

et al. 2024

J. Mech. Eng. Sci.

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“…So, if the FE model simulates the total laser path, the computation time can reach days, and even months, and becomes unaffordable. In order to reduce computation time, many numerical techniques exist, such as using an artificially elongated heat source (part of a track or even a few layers) to increase the zone that is printed at once, with the major drawback being that temperature peaks are lost [27], or data driven models (mostly deep neural network enriched with physical laws) which need a large amount of data for their training [28,29], or an equivalent mathematical data driven technique such as the Proper Orthogonal Decomposition (POD). This latter technique can be applied on parts of the studied volume with low thermal gradients in order to reduce the total number of degrees of freedom (replacing FE computed thermal field by thermal modes).…”

Section: Introductionmentioning

confidence: 99%

Impact of Boundary Parameters Accuracy on Modeling of Directed Energy Deposition Thermal Field

Gallo,

Duchêne,

Quy Duc Pham

et al. 2024

Metals

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Within the large Additive Manufacturing (AM) process family, Directed Energy Deposition (DED) can be used to create low-cost prototypes and coatings, or to repair cracks. In the case of M4 HSS (High Speed Steel), a reliable computed temperature field during DED process allows the optimization of the substrate preheating temperature value and other process parameters. Such optimization is required to avoid failure during the process, as well as high residual stresses. If 3D DED simulations provide accurate thermal fields, they also induce huge computation time, which motivates simplifications. This article uses a 2D Finite Element (FE) model that decreases the computation cost through dividing the CPU time by around 100 in our studied case, but it needs some calibrations. As described, the identification of a correct data set solely based on local temperature measurements can lead to various sets of parameters with variations of up to 100%. In this study, the melt pool depth was used as an additional experimental measurement to identify the input data set, and a sensitivity analysis was conducted to estimate the impact of each identified parameter on the cooling rate and the melt pool dimension.

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“…Today, the use of ML can be seen in many diferent areas of the AM process [11]. It can help optimize input parameters and output characteristics to get optimal results much faster.…”

Section: Introductionmentioning

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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Directed Energy Deposition via Artificial Intelligence‐Enabled Approaches

Cited by 15 publications

References 92 publications

Sustainable considerations in additive manufacturing processes: A review

Sustainable considerations in additive manufacturing processes: A review

Impact of Boundary Parameters Accuracy on Modeling of Directed Energy Deposition Thermal Field

Powder Bed Fusion via Machine Learning-Enabled Approaches

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