Statistical Modeling of Surface Roughness in Hard Milling H13 Steel

Song Zhang; Jiang Feng Li; Tong Chao Ding

doi:10.4028/www.scientific.net/MSF.697-698.61

Paper Titles

A Study on Planarization Grinding Process Based on the Cluster Magnetorheological Effect
p.44

Effect of Various Cooling Strategies on Surface Roughness of High Speed Milling Ti-6Al-4V
p.49

A New Vari-Speed Cutting Scheme for Chatter Suppression
p.53

Surface Damages Generated in Hole Making of a Cast Nickel-Based Alloy
p.57

Statistical Modeling of Surface Roughness in Hard Milling H13 Steel
p.61

Surface Burn and Suppression in Grinding TC4 with Segmented CBN Cup Wheel
p.67

A New Method for Identifying the Cutter Runout Parameters in Flat End Milling Process
p.71

Chip Thickness Analysis in Peripheral Milling of Curved Surfaces with Variable Curvature Considering Cutter Runout
p.75

Grinding Capability of the Indigenous Polymer-Based Solid Lubricant Coating Wheels Used in Dry Grinding
p.80

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Statistical Modeling of Surface Roughness in Hard Milling H13 Steel

Abstract:

Surface roughness is one of the most important requirements in the cutting process. In this paper, advantages of Taguchi’s experimental design technique, experimental measurements, and analysis of variance (ANOVA) are integrated to statistically investigate surface roughness in hard milling H13 steel (HRc50±1) with coated round inserts under dry cutting conditions. A 12-parameter cubic regression model for surface roughness is developed, which simultaneously takes into consideration the main effects and the interactions of four cutting parameters. A good correlation is observed between the predictive values and the experimental data.