Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts
By T. Kıvak. 2014
411 citations recorded by the source corpus.
Abstract In this paper, the Taguchi method and regression analysis have been applied to evaluate the machinability of Hadfield steel with PVD TiAlN- and CVD TiCN/Al 2 O 3 -coated carbide inserts under dry milling conditions. Several experiments were conducted using the L 18 (2 × 3 × 3) full-factorial design with a mixed orthogonal array on a CNC vertical machining center. Analysis of variance (ANOVA) was used to determine the effects of the machining parameters on surface roughness and flank wear. The cutting tool, cutting speed and feed rate were selected as machining parameters. The analysis results revealed that the feed rate was the dominant factor affecting surface roughness and cutting speed was the dominant factor affecting flank wear. Linear and quadratic regression analyses were applied to predict the outcomes of the experiment. The predicted values and measured values were very close to each other. Confirmation test results showed that the Taguchi method was very successful in the optimization of machining parameters for minimum surface roughness and flank wear in the milling the Hadfield steel.
Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/f065954bc628d7d6e511705fd7e5f0b834eb2f51
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Citations in the corpus
- Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method (2015, methodology): “Furthermore, it provides a simple, efficient and systematic approach to specifying the optimum cutting parameters in the manufacturing process [24,25].”