Surface Quality Evaluation of 3D-Printed Carbon-Fiber-Reinforced PETG Polymer During Turning: Experimental Analysis, ANN Modeling and Optimization.

This work presents an experimental analysis related to 3D-printed carbon-fiber-reinforced-polymer (CFRP) machining. A polyethylene-terephthalate-glycol (PETG)-based composite, reinforced with 20% carbon fibers, was selected as the test material. The aim of the study was to evaluate the influence of cutting conditions used in light operations on the generated surface quality of the 3D-printed specimens. For this purpose, nine specimens were fabricated and machined under a wide range of cutting parameters, including cutting speed, feed, and depth of cut. The generated surface roughness was measured with a mechanical gauge and the acquired data were used to develop a shallow artificial neural network (ANN) for prediction purposes, showing that a 3-6-1 structure is the best solution. Following this, a genetic algorithm (GA) was utilized to minimize the response, revealing that the optimal combination is 205 m/min speed, 0.0578 mm/rev feed, and 0.523 mm depth of cut, contributing to the fabrication of low friction parts and shafts with a high quality surface, as well as to the reduction of resource waste. A validation study supported the accuracy of the developed model, by exhibiting errors below 10%. Finally, a set of enhanced images were taken to assess the machined surfaces. It was found that 1.50 mm depth of cut is responsible for the generation of defects across the circumference of the specimens. Especially, combined with 150 m/min cutting speed and 0.11 mm/rev feed, more flaws are produced.