Meiabadi Mohammad Saleh, Moradi Mahmoud, Karamimoghadam Mojtaba, Ardabili Sina, Bodaghi Mahdi, Shokri Manouchehr, Mosavi Amir H
Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada.
Faculty of Engineering, Environment and Computing, School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry CV1 2JH, UK.
Polymers (Basel). 2021 Sep 23;13(19):3219. doi: 10.3390/polym13193219.
Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.
聚乳酸（PLA）是一种适用性很强的材料，因其具有一些显著特性，如可变形性和成本低廉，而被用于3D打印机。为提高最终使用质量，提升PLA中熔丝制造（FFF）打印物体的质量至关重要。本研究的目的是提高韧性，并降低具有所需部件厚度的FFF打印拉伸试验样品的生产成本。为避免需要大量闲置的打印样品，采用了响应面法（RSM）。通过将挤出机温度（ET）、填充率（IP）和层厚（LT）作为控制因素进行统计分析来处理这一问题。进一步开发了人工神经网络（ANN）和ANN-遗传算法（ANN-GA）这两个人工智能方法来估计韧性、部件厚度和与生产成本相关的变量。通过相关系数和均方根误差（RMSE）值对结果进行评估。根据建模结果，与单一ANN方法相比，作为混合机器学习（ML）技术的ANN-GA在韧性、部件厚度和生产成本方面的建模精度分别可提高约7.5%、11.5%和4.5%。另一方面，优化结果证实，优化后的样品具有成本效益，并且能够相对地承受变形，这使得打印的PLA物体具有可用性。
