An investigation of the machinability of PA 6/nano-CaCO3 composite

Abstract

Nowadays, polymer nanocomposites have attracted manufacturers’ attention because of their good mechanical, thermal, and physical properties. Over the past decade, the requirement of the direct machining of polymer nanocomposites has increased due to the production of most polymer nanocomposites using the extrusion method in simple cross-section and the increased demand for personalized products. In this paper, the effect of milling parameters (spindle speed and feed per tooth) and nano-CaCO3 content on the machinability properties of PA 6/nano-CaCO3 composites was studied by analyzing variance. Harmony search-based neural network (HSNN) was then utilized to create predictive models of surface roughness and total cutting forces from the experimental data. The results revealed that the nano-CaCO3 content on PA 6 decreased the cutting forces significantly, but did not have a significant effect on surface roughness. Moreover, the results for modeling total cutting forces and surface roughness showed that HSNN is effective, reliable, and authoritative in modeling the surface roughness formation and total cutting force mechanism for end-milling of PA 6/nano-CaCO3 composites.