Impact of parameters on the process response: A Taguchi orthogonal analysis for laser engraving


KASMAN Ş.

MEASUREMENT, vol.46, no.8, pp.2577-2584, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 8
  • Publication Date: 2013
  • Doi Number: 10.1016/j.measurement.2013.04.022
  • Journal Name: MEASUREMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2577-2584
  • Keywords: Laser engraving, Surface roughness, Milling depth, Taguchi method, MILLING PROCESS PARAMETERS, WORK TOOL STEEL, SURFACE-ROUGHNESS, OPTIMIZATION, MACHINABILITY, IMPROVEMENT, WEAR
  • Dokuz Eylül University Affiliated: Yes

Abstract

Compared to conventional methods, laser engraving is the most effective technique in the machining of hard materials that have a complex geometry. Therefore, laser based machining is widely used in many industries like mold making, and the manufacture of automotive, electronics and biomedical parts. The present study investigates the machinability of hard metal produced with powder metallurgy and puts forward a new approach relating to the laser engraving of P/M metals. The main objective of this study is to determine the impact of laser engraving process on Vanadis 10. For this purpose, three process parameters - namely effective scan speed, frequency, and laser effective power - were correlated with the surface roughness (R-a) and engraving depth (D). The Taguchi and linear regression were used in the analysis. The experiments were performed in accordance with an L-9 orthogonal array. Based on the S/N ratio for R-a and D, the optimal condition was found as SS3F2P1 for R-a and SS1F2P3 for D. It was found that scan speed has a statistically significant effect on both R-a and D. Furthermore, a mathematical model for both R-a and D was established and estimated using linear regression. The model was also tested using different experimental conditions than existing ones. The results obtained from the new experimental conditions show that the predicted models could explain the process. (C) 2013 Elsevier Ltd. All rights reserved.