Reference health
Selection of effective manufacturing conditions for directed energy deposition process using machine learning methods
22/22 checkable references clean · checked 2026-07-23
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 22 checked references that resolve
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In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy
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Control of Directed Energy Deposition Process to Obtain Equal-Height Rectangular Corner
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A Study on Activation Algorithm of Finite Elements for Three-Dimensional Transient Heat Transfer Analysis of Directed Energy Deposition Process
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Effects of Laser-Beam Defocus on Microstructural Features of Compositionally Graded WC/Co-Alloy Composites Additively Manufactured by Multi-Beam Laser Directed Energy Deposition
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Understanding the microstructural evolution of cold sprayed Ti-6Al-4V coatings on Ti-6Al-4V substrates
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Modeling of the laser powder–based directed energy deposition process for additive manufacturing: a review
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A review of laser engineered net shaping (LENS) build and process parameters of metallic parts
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Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry
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Porosity prediction: Supervised-learning of thermal history for direct laser deposition
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Quality analysis in metal additive manufacturing with deep learning
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Evolution of interfacial contact during low pressure rotary friction welding: A finite element analysis
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Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications
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Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L
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Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring
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no DOI — not checkedSreeraj, P. & Kannan, T. Modelling and prediction of stainless steel clad bead geometry deposited by GMAW using regression and artificial neural network models. Adv. Mech. Eng. 2012, 12 (2012).
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