Advancing Additive Manufacturing through Deep Learning: A Comprehensive
  Review of Current Progress and Future Challenges
Amirul Islam Saimon Emmanuel Yangue Xiaowei Yue Zhenyu James Kong Chenang Liu
Additive manufacturing (AM) has already proved itself to be the potential
alternative to widely-used subtractive manufacturing due to its extraordinary
capacity of manufacturing highly customized products with minimum material
wastage. Nevertheless, it is still not being considered as the primary choice
for the industry due to some of its major inherent challenges, including
complex and dynamic process interactions, which are sometimes difficult to
fully understand even with traditional machine learning because of the
involvement of high-dimensional data such as images, point clouds, and voxels.
However, the recent emergence of deep learning (DL) is showing great promise in
overcoming many of these challenges as DL can automatically capture complex
relationships from high-dimensional data without hand-crafted feature
extraction. Therefore, the volume of research in the intersection of AM and DL
is exponentially growing each year which makes it difficult for the researchers
to keep track of the trend and future potential directions. Furthermore, to the
best of our knowledge, there is no comprehensive review paper in this research
track summarizing the recent studies. Therefore, this paper reviews the recent
studies that apply DL for making the AM process better with a high-level
summary of their contributions and limitations. Finally, it summarizes the
current challenges and recommends some of the promising opportunities in this
domain for further investigation with a special focus on generalizing DL models
for wide-range of geometry types, managing uncertainties both in AM data and DL
models, overcoming limited and noisy AM data issues by incorporating generative
models, and unveiling the potential of interpretable DL for AM.