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All Journal PROCEEDINGS CONFERENCE ON DESIGN AND MANUFACTURE AND ITS APPLICATION
Putri Amalia Maviroh
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Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Optimasi Kekasaran Permukaan dan Laju Pemotongan pada Mesin Laser cutting Menggunakan Material SUS 316L dengan Metode Taguchi dan Neural Network Putri Amalia Maviroh; Bayu Wiro Karuniawan; Farizi Rachman
Proceedings Conference On Design Manufacture Engineering And Its Application Vol 4 No 1 (2020): Conference on Design and Manufacture and Its Aplication
Publisher : Proceedings Conference On Design Manufacture Engineering And Its Application

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Abstract

Laser cutting machine is able to cutting hard material and having complicated patterns. In the cutting process in the laser cutting machine has several parameters, they are the laser beam focus point, cutting gas pressure and cutting speed. These parameters can cause cutting defects. Starting from the roughness surface until the product is not cut. So we need an optimization process through research methods. The optimization method in this research use the L9(34) orthogonal matrix with 3 levels on each parameter of taguchi and neural network prediction. From the taguchi optimization process, it produces an optimal parameter combination for the surface roughness response at the laser beam focus point -14 mm, gas cutting pressure 17 bar and cutting speed 0.4 m / min. And for the response of the cutting rate at the laser beam focus point -17 mm, gas pressure cutting 20 bar and cutting speed 0.6 m / min. While the optimal parameter settings using a neural network, for the response of surface roughness at the laser beam focus point -14 mm, gas pressure cutting 20 bar and cutting speed 0.2 m / minute. And for the response of the cutting rate at the laser beam focus point -20 mm, gas pressure cutting 17 bar and cutting speed 0.6 m / minute. In optimal parameter settings using the taguchi method, the laser beam focus point parameters have the greatest contribution, with a percentage of 46.5061% for the response of surface roughness. While cutting speed has a significant contribution in reducing the variation of the cutting rate response, with a contribution percentage of 66.0430%.
Co-Authors Bayu Wiro Karuniawan Farizi Rachman