利用人工神经网络和遗传算法提高熔融沉积模型(FDM)零件的抗拉强度

V. Chowdary Boppana, Fahraz Ali
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引用次数: 0

摘要

目的采用i -最优设计方法,对FDM工艺参数与聚碳酸酯(PC)试样抗拉强度之间的关系进行实验研究。设计/方法/方法i -采用最优设计方法,通过Minitab-17.1软件规划实验。样品使用Stratsys FDM 400mc制造,并按照ISO标准进行测试。此外,建立了人工神经网络模型,并与回归模型进行了比较,以便选择合适的模型进行优化。最后,运用遗传算法求解提高FDM制造PC构件的抗拉强度。研究表明,工艺参数的选择(栅格角度、栅格间气隙、沿Y轴的构建方向和等高线数)对拉伸强度有显著影响,其中栅格角度是影响最大的因素。增加沿Y轴的构建方向可以使试样结构紧凑,从而提高抗断裂能力。拟合的回归模型的p值小于0.05,这表明模型项显著地代表了PC样品的抗拉强度。此外,从正态概率图中发现残差遵循一条直线,因此所开发的模型提供了足够的预测。此外,从验证运行中,沿着参考线可以看到预测值和实际值之间的密切一致,这进一步支持了令人满意的模型预测。本研究利用i -优化设计和方差分析成功地研究了所选工艺参数——栅格角度、栅格间气间隙、围绕Y轴的构建方向和等高线数量——对PC样品抗拉强度的影响。此外,针对零件强度的预测,建立了回归模型和人工神经网络模型。使用ga求解器对选定的ANN模型进行优化,以确定最优参数设置。提出的ANN-GA方法更适合于建立所选工艺参数与抗拉强度之间的非线性关系。此外,所提出的ANN-GA方法可以帮助以尼龙,聚对苯二甲酸乙二醇酯(PETG)和PET作为新的3d打印材料制造各种工业产品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improvement of tensile strength of fused deposition modelling (FDM) part using artificial neural network and genetic algorithm techniques
Purpose This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design. Design/methodology/approach I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components. Findings This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance. Research limitations/implications The fitted regression model has a p -value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions. Practical implications This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings. Originality/value The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.
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