Green manufacturing via machine learning enabled approaches

Raj, Aditya; Gyaneshwar, Amogh; Chadha, Utkarsh; Chadha, A.; Asija, Adi; Abrol, Aarye; Priya, Sharad; Alam, Arbab; Joshi, Ayushma; Bastola, Kashyap; Selvaraj, Senthil Kumaran; Karthikeyan, B.; Nagalakshmi, R.; Chandramohan, Vishjit; Hadidi, Haitham

doi:10.1007/s12008-022-01136-0

Cited by 7 publications

(1 citation statement)

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“…It can also enhance build-time estimations, speed up design iterations, optimize cost and weight performance, and enhance our capability to infuence part performance by examining the powder and its characteristics. With the introduction of machine learning (ML) such as supervised learning, unsupervised learning, and reinforcement learning algorithms, methods, and techniques depicted in Figure 1 were used to implement tasks such as defect detection, in situ monitoring, real-time process monitoring, quality monitoring, porosity analysis, optimizing process and parameters, parameter selection, prediction of inherent strain, fatigue life, part distortions, and anomalies and parameters like laser power [12][13][14].…”

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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Section: Introductionmentioning

confidence: 99%