This paper presents the first comprehensive literature review of deep learning (DL)
applications in additive manufacturing (AM). It addresses the need for a thorough analysis in …

Selecting optimal input variables for machine learning (ML) algorithms is essential for any
model outputs. This study presented a feature selection-based approach for determining the …

Detecting and classifying the melt pool states in laser-based direct energy deposition (L-
DED) is crucial for reducing defects and enhancing the mechanical properties of L-DED …

Fused deposition modeling comes with many conveniences for the manufacturing industry,
but many defects tend to appear in actual production due to the problems of the FDM …

In this study, machine learning (ML), a subdivision of artificial intelligence (AI), is
implemented to study the mechanical behavior of composite adhesive single-lap joints …

This article provides an insightful review of the recent applications of machine learning (ML)
techniques in additive manufacturing (AM) for the prediction and amelioration of mechanical …

Additive manufacturing offers a solution for producing advanced ceramics with complex
geometries by enabling precise control over geometry, microstructure, and composition. By …

This study presents a comprehensive experimental dataset and a novel classification model
based on Deep Neural Networks to estimate surface roughness for additive manufacturing …

The inherent brittle behavior of cementitious composite is considered one of its weaknesses
in structural applications. This study evaluated the impact strength and failure modes of …

In this work we employ an encoder–decoder convolutional neural network to predict the
failure locations of porous metal tension specimens based only on their initial porosities. The …