Modeling and optimization of high-grade compacted graphite iron milling force and surface roughness via response surface methodology

Abstract

In this study, RuT400(σ_b ≥ 500 MPa) was milled with different cutting speeds, feeding rates and cutting depths to construct cutting force and surface roughness prediction models via response surface method (RSM). The milling parameters were optimised via response surface, contour map and iterative algorithm. The results show that that feeding rate and cutting depth are factors that significantly affect the cutting force. Similarly, cutting speed and feeding rate were found to be significant in regards to surface roughness. The multivariate linear equations yield more accurate surface roughness and cutting force predictions than fitting the linearised multivariate non-linear equations. The cutting force prediction model is F = 60.823 − 0.020V + 149.489f + 41.932a; the surface roughness prediction model is Ra = 1.633 − 0.00161V + 1.803f + 0.0289a. The use of high-strength tools and a high-endurance manufacturing system for cutting force can greatly enhance the machining efficiency in a processing system with favourable rigidity.

Keywords:

• Response surface methodology
• compacted graphite iron
• cutting force
• surface roughness
• parameter optimisation