Modelling of the hole quality characteristics by Extreme Learning Machine in fiber laser drilling of Ti-6Al-4V

2018 ◽  
Vol 36 ◽  
pp. 138-148 ◽  
Author(s):  
Mustafa AY
Keyword(s):  
Fiber Laser ◽  
Extreme Learning Machine ◽  
Quality Characteristics ◽  
Laser Drilling ◽  
Hole Quality ◽  
Learning Machine

scholarly journals Particle Swarm Optimisation of Hole Quality Characteristics in Laser Trepan Drilling of Inconel 718

2019 ◽  
Vol 69 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Kedari Lal Dhaker ◽  
Arun Kumar Pandey
Keyword(s):  
Inconel 718 ◽  
Input Parameter ◽  
Particle Swarm ◽  
Quality Characteristics ◽  
Laser Drilling ◽  
Particle Swarm Optimisation ◽  
Drilling Process ◽  
Hole Quality ◽  
Geometrical Features ◽  
Laser Trepan Drilling

Inconel-718 is a nickel based super alloy and is extensively in use for working at very high temperature (upto 2000 °C) such as aero engine gas path equipment, nuclear equipment etc. Drilling micro size hole in such material with laser beam has been a proven choice and laser drilling process produces geometrically and dimensionally improved hole. Hole geometrical features can be improved further if laser drilling system operated at optimal input parameter setting. This paper experimentally investigates the behavior of hole geometrical features hole circularity and hole taper in laser trepan drilling of Inconel -718 sheet. Optimal value of laser input parameters for improved hole circularity and reduced hole taper have been suggested with the help of computational intelligence technique particle swarm optimisation. The effect of each laser input parameter on hole quality characteristics are also discussed and demonstrated graphically. Finally the experimental validation of the predicted results has been carried out.

Sistema Híbrido Baseado Em Extreme Learning Machine E Harmony Search

2016 ◽  
Author(s):  
Edgar Wellington Marques de Almeida ◽  
Mêuser Jorge da Silva Valença
Keyword(s):  
Extreme Learning Machine ◽  
Harmony Search ◽  
Learning Machine
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