Additive manufacturing (AM) technologies such as fused deposition modeling (FDM) rely on the quality of manufactured products and the process capability. Currently, the dimensional accuracy and stability of any AM process is essential for ensuring that customer specifications are satisfied at the highest standard, and variations are controlled without significantly affecting the functioning of processes, machines, and product structures. This study aims to investigate the effects of FDM fabrication conditions on the dimensional accuracy of cylindrical parts. In this study, a new class of experimental design techniques for integrated second-order definitive screening design (DSD) and an artificial neural network (ANN) are proposed for designing experiments to evaluate and predict the effects of six important operating variables. By determining the optimum fabrication conditions to obtain better dimensional accuracies for cylindrical parts, the time consumption and number of complex experiments are reduced considerably in this study. The optimum fabrication conditions generated through a second-order DSD are verified with experimental measurements. The results indicate that the slice thickness, part print direction, and number of perimeters significantly affect the percentage of length difference, whereas the percentage of diameter difference is significantly affected by the raster-to-raster air gap, bead width, number of perimeters, and part print direction. Furthermore, the results demonstrate that a second-order DSD integrated with an ANN is a more attractive and promising methodology for AM applications.

诸如熔融沉积建模（FDM）之类的增材制造（AM）技术依赖于制成品的质量和工艺能力。当前，任何增材制造工艺的尺寸精度和稳定性对于确保以最高标准满足客户规格至关重要，并且在不显着影响工艺，机器和产品结构功能的情况下控制变化。这项研究旨在调查FDM制造条件对圆柱零件尺寸精度的影响。在这项研究中，提出了一种用于集成二阶确定性筛选设计（DSD）和人工神经网络（ANN）的新型实验设计技术，以设计用于评估和预测六个重要操作变量影响的实验。通过确定最佳的制造条件以获得更好的圆柱零件尺寸精度，本研究大大减少了时间消耗和复杂实验的数量。通过二阶DSD生成的最佳制造条件已通过实验测量得到验证。结果表明，切片厚度，零件打印方向和周长数显着影响长度差异的百分比，而栅格间的气隙，珠子宽度，周长，和零件打印方向。此外，结果表明，与ANN集成的二阶DSD对于AM应用来说是一种更具吸引力和前途的方法。