“Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing”, 2nd Edition

By J. Edgar, Saxon Tint. 2015

377 citations recorded by the source corpus.

Subfields: Automotive Engineering

View Paper: https://www.semanticscholar.org/paper/81e5769eb9090913f0c183d1c737b55bef03350b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Jing Zhang
2. Je-hyun Lee
3. Yeon‐Gil Jung

Citations in the corpus

• Machine Learning in Additive Manufacturing: A Review (2020, methodology): “This is important for selecting ML models for some reasons: (1) even with the same applications, the ML models applied may be different with different data types, such as defect detection, which can be achieved by both supervised and unsupervised learning models; (2) ML models tend to have similar p”
• Unlocking value for a circular economy through 3D printing: A research agenda (2017, background): “, 2003), the technology has shown significant potential in a range of high value manufacturing applications, such as medical products, automotive and aerospace components, industrial machinery and high-end consumer products (Gibson et al., 2014).”