Advances in Manufacturing

Papers
(The H4-Index of Advances in Manufacturing is 17. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-04-01 to 2024-04-01.)
ArticleCitations
Digital twin-based sustainable intelligent manufacturing: a review297
A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm83
A review on conventional and nonconventional machining of SiC particle-reinforced aluminium matrix composites81
On the application of additive manufacturing methods for auxetic structures: a review64
Precision micro-milling process: state of the art53
Thermal error modeling based on BiLSTM deep learning for CNC machine tool47
An investigation on machined surface quality and tool wear during creep feed grinding of powder metallurgy nickel-based superalloy FGH96 with alumina abrasive wheels42
Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using definitive screening design and deep learning feedforward artificial neural network41
State-of-the-art survey on digital twin implementations40
Prediction and analysis of process failures by ANN classification during wire-EDM of Inconel 71838
Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks26
A comprehensive review on residual stresses in turning26
Research on ultrasonic-assisted drilling in micro-hole machining of the DD6 superalloy23
Porosity, cracks, and mechanical properties of additively manufactured tooling alloys: a review23
Prediction of product roughness, profile, and roundness using machine learning techniques for a hard turning process21
Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing20
A novel paradigm for feedback control in LPBF: layer-wise correction for overhang structures18
A cortical bone milling force model based on orthogonal cutting distribution method17
Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process17
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