壳体厚度和填充密度对ABS打印件力学性能和质量交互影响的预测与研究

A. Bedan, T. Abbas, Emad Hussein
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引用次数: 1

摘要

壳体厚度和填充密度是决定打印件受力时机械稳定性的关键参数。本研究旨在建立预测响应的模型,特别是抗压强度、相对强度和重量,并分析外壳厚度和填充密度对ABS打印件的交互影响,并通过进行压缩测试来评估这些影响。为此,研究了不同壳体厚度(0.4、0.8、1.2、1.6和2.0 mm)和不同填充密度(0%、25%、50%、75%和100%)对ABS打印件抗压强度、相对强度和材料消耗等响应变量的交互影响。实验结果表明,当填充密度为75%、壳厚为2mm时,ABS打印试样的相对压应力最高(1645 N/g)。通过实测数据与预测数据的对比,验证了所提出预测模型的有效性评价,表明本文提出的二次拟合模型适用于所有考虑的响应变量。模型拟合较好,最大误差为6.2%。在制造FDM零件之前,通过估算与工艺参数相关的抗压强度、相对强度和材料消耗,开发的预测模型将帮助从业者减少实验工作的数量,从而节省材料、缩短打印时间和降低能耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction and Investigation of the Interactive Impact of Shell Thickness and Infill Density on the Mechanical Properties, and the Mass of ABS Prints
Shell thickness and infill density are key parameters for determining mechanical stability of a printed part when subjected to stress. This study aimed to establish models for predicting responses, specifically compressive strength, relative strength, and weight, and to analyze the interactive effects of both shell thickness and infill density on ABS prints, which were evaluated by conducting compression tests. For this purpose, the interactive effects of different shell thicknesses (0.4, 0.8, 1.2, 1.6, and 2.0 mm) and different infill densities (0%, 25%, 50%, 75%, and 100%) on the considered response variables, namely, compressive strength, relative strength, and material consumption of ABS prints were investigated. According to the results of the experiments, a specimen printed from ABS with a 75% infill density and a shell thickness of 2 mm has the highest relative compressive stress (1645 N/g). The evaluation of the effectiveness of the proposed prediction models was confirmed by comparing the measured data with the predicted data, which showed that the quadratic fit models presented in this study are suitable for all considered response variables. The model fits the data well, with a maximum error of 6.2%. By estimating the compressive strength, relative strength, and material consumption in relation to process parameters before manufacturing the FDM parts, the developed prediction models will assist practitioners in reducing the number of experimental works, resulting in material savings, reduced printing time, and reduced energy consumption.
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