Tzotzis Anastasios, Nedelcu Dumitru, Mazurchevici Simona-Nicoleta, Kyratsis Panagiotis
Department of Product and Systems Design Engineering, University of Western Macedonia, 50100 Kila Kozani, Greece.
Department of Manufacturing Engineering, "Gheorghe Asachi" Technical University, 700050 Iasi, Romania.
Polymers (Basel). 2024 Oct 18;16(20):2927. doi: 10.3390/polym16202927.
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.
这项工作展示了与3D打印碳纤维增强聚合物（CFRP）加工相关的实验分析。选用了一种以聚对苯二甲酸乙二醇酯二醇（PETG）为基础、含有20%碳纤维增强的复合材料作为测试材料。该研究的目的是评估轻加工中使用的切削条件对3D打印试件加工表面质量的影响。为此，制作了九个试件，并在包括切削速度、进给量和切削深度等广泛的切削参数下进行加工。使用机械量具测量生成的表面粗糙度，并将获取的数据用于开发一个浅层人工神经网络（ANN）进行预测，结果表明3-6-1结构是最佳解决方案。在此之后，利用遗传算法（GA）使响应最小化，结果显示最佳组合为205米/分钟的速度、0.0578毫米/转的进给量和0.523毫米的切削深度，这有助于制造具有高质量表面的低摩擦零件和轴，同时减少资源浪费。一项验证研究通过展示低于10%的误差，支持了所开发模型的准确性。最后，拍摄了一组增强图像来评估加工表面。结果发现，1.50毫米的切削深度会导致试件圆周上出现缺陷。特别是，与150米/分钟的切削速度和0.11毫米/转的进给量相结合时，会产生更多缺陷。
