Process efficiency measurements in the laser engineered net shaping process

By Raymond R. Unocic, J. N. DuPont. 2004

150 citations recorded by the source corpus.

Subfields: Automotive Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/2e2374b48d29b1c653da471e66eb43e17f534970

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. Huijun Li
2. Zengxi Pan
3. Nima Shamsaei
4. Aref Yadollahi
5. Dominic Cuiuri
6. Donghong Ding
7. D. Jafari
8. T. Vaneker

In Industrial and Manufacturing Engineering

1. Huijun Li
2. Zengxi Pan
3. Dominic Cuiuri
4. Donghong Ding
5. Nima Shamsaei
6. Aref Yadollahi
7. Jun Xiong
8. Guangjun Zhang

In Mechanical Engineering

1. Huijun Li
2. Zengxi Pan
3. Donghong Ding
4. Dominic Cuiuri
5. Nima Shamsaei
6. Aref Yadollahi
7. D. Jafari
8. Ian Gibson

Citations in the corpus

• An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics (2015, background): “50 through the se of a Seebeck envelope calorimeter [106].”
• Additive manufacturing for energy: A review (2020, background): “In laser-based AM processes, only about 30–50% of the laser power is absorbed by a molten pool, due to heat loss caused by reflection on deposited metal, absorption by in-flight powders, absorption by material evaporation, and the dependence of surface absorptivity on materials, temperature, and bea”
• Weld bead modeling and process optimization in Hybrid Layered Manufacturing (2011, background): “However, it has very poor energy efficiency in this application (2%–5%) [7].”
• Wire-feed additive manufacturing of metal components: technologies, developments and future interests (2015, background): “However, it has very poor energy efficiency (2–5%) [40].”
• Wire and arc additive manufacturing: Opportunities and challenges to control the quality and accuracy of manufactured parts (2021, background): “The main specifications of WALM include its high precision and poor energy efficiency [42].”
• A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM) (2015, background): “However, it has very poor energy efficiency (2% - 5%) [15].”
• Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis (2012, background): “But its effective utilization of energy is very poor (Unocic and DuPont 2004).”