填充、填充样式和周壳数对FDM法铸造模的影响

N. H. Huu, Duc Tran Cong Toan, T. Huu, H. T. T. Thu
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引用次数: 2

摘要

几十年来,快速成型技术一直是取代传统制造工艺的新兴技术。然而,生产一台能够使用金属创建模型的快速原型机的成本太高,不适用于小批量生产。因此,快速成型的焦点之一是为熔模铸造工艺生产模型,称为快速铸造。熔融沉积建模(FDM)是将ABS、PLA等塑料长丝逐层连续熔融形成零件的快速成型工艺。FDM是快速铸造中常用的一种方法。快速铸造存在两个主要问题,一是烧尽过程中模壳开裂,二是模壳烧尽不完全,留下残灰,并释放出影响模壳的腐蚀性副产物。在这项研究中,我们使用三个参数来确定它们对图案留下的残余灰量的影响:周长壳数,填充百分比和填充图案。使用FDM方法制造的ABS模组由于ABS具有相当高的燃烧温度并且在工业过程中使用频率更高,先前的研究也表明,与PLA模组相比,半空心ABS模组具有更好的尺寸精度和表面粗糙度。之后,我们训练了一个人工神经网络(ANN),并让它预测相同实验的结果,并将结果与使用平均绝对百分比误差(MAPE)的田口方法预测的结果进行比较。结果表明,人工神经网络的预测输出与预期输出非常接近,可靠性高(95.218%)。ANN方法的MAPE也低于Taguchi的方法,突出表明ANN方法是一种可靠的替代方法,可以准确预测所选参数与结果之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effects of Infill, Infill Patterns and Number of Perimeter Shells on Casting Patterns Fabricated Using FDM Method
Rapid prototyping has been the up-and-coming technology to replace traditional manufacturing processes for several decades. However, the cost of producing a rapid prototyping machine that’s able to create a model using metal is too high to apply to low volume production. Therefore, one of the focal points of rapid prototyping is producing patterns for investment casting processes, called rapid casting. Fused deposition modelling (FDM) is a rapid prototyping process which forms a part by continuously melts plastic filaments such as ABS and PLA layer by layer. FDM is a method frequently used in rapid casting. There are two main problems with rapid casting, the cracking of mould shells during the burnout process and patterns incompletely burns out, leaving residual ash and releases corrosive by-products which affect the shells. In this study, we use three parameters to determine their effects on the amount of residual ash the patterns leave behind: Number of perimeters shells, infill percentage and infill pattern. ABS patterns fabricated using FDM method are used because ABS has considerably higher burnout temperature amd is used more frequently in industrial processes, previous studies have also shown that semi-hollow ABS plastic patterns have better dimensional accuracy and surface roughness compared to PLA plastic patterns. After that, we trained an Artificial neural network (ANN) and let it predict the outcomes of the same experiments and compared the results to that of Taguchi’s method predictions using the mean absolute percentage error (MAPE). The results indicate that the predicted outputs of the ANN are very close to the expected output with high reliability (95.218%). ANN method also has a lower MAPE than Taguchi’s method, highlighting that ANN method is a reliable alternative method to accurately predict the relationship between the chosen parameters and the results.
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