Experimental and Modeling Study of the Turning Process of PA 6/Nano Calcium Carbonate Composite

Nowadays, polymeric nanocomposites have emerged as a new material class with rapidly growing use in industrial products because of good mechanical, thermal, and physical properties. Recently, the requirement of the direct machining of these materials has increased due to the production of the most of them by extrusion method in simple cross section and the increased demand for personalized products. In this work, the effect of turning parameters (cutting speed and feed) and nano calcium carbonate content on the machinability properties of polyamide 6/nano calcium carbonate composites was investigated by analysis of variance. A novel modeling approach of modified harmony search-based neural network was also utilized to create predictive models of surface roughness and total cutting force from the experimental data. The results revealed that the nano calcium carbonate content on polyamide 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 modified harmony search-based neural network is effective, reliable, and authoritative in modeling the turning process of polyamide 6/nano calcium carbonate composite