The properties of functional parts printed by additive manufacturing are highly dependent on various process parameters of the machine. The process parameters can be optimized by hybrid statistical tools to enhance the objective function. The present study investigates the tensile strength of Dogbone American Society for Testing and Materials (ASTM)-D638-V standardized parts fabricated by FDM 3D printer, using poly lactic acid (PLA) plus material. The test specimens were fabricated by varying three parameters: infill density (20–100%), speed (50–150 mm/s) and temperature (190–210 °C). For the parametric combination, response surface methodology (RSM) based central composite design (CCD) matrix was developed using second order polynomial fitting model. The maximum tensile strength of testing specimens on UNITEK-94100 universal testing machine (UTM) was recorded as 45.27 MPa. Further, hybrid optimization techniques like genetic algorithm-artificial neural network (GA-ANN), genetic algorithm-response surface methodology (GA-RSM) and genetic algorithm-adaptive neuro fuzzy interface system (GA-ANFIS) are deployed to optimize these process parameters. Among these tools, the maximum accuracy of 99.89% obtained with GA-ANN which results in optimized parameters as infill density 100%, temperature 210 °C, and speed 124.778 mm/s to achieve the maximum tensile strength of 47.0212 MPa. The results of this examination will facilitate the added substance producing units to choose the optimize factor value of input factors for FDM parts fabrication with improved mechanical properties. The hybrid developed models could be proposed for precise prediction and optimization of other process parameters and results for any industrial application problems.

通过增材制造印刷的功能部件的特性高度依赖于机器的各种工艺参数。可以通过混合统计工具优化过程参数以增强目标功能。本研究调查了由FDM 3D打印机使用聚乳酸（PLA）和材料制成的Dogbone美国测试和材料学会（ASTM）-D638-V标准化零件的拉伸强度。通过改变三个参数来制造试样：填充密度（20–100％），速度（50–150 mm / s）和温度（190–210°C）。对于参数组合，使用二阶多项式拟合模型开发了基于响应面方法（RSM）的中央复合设计（CCD）矩阵。在UNITEK-94100通用测试机（UTM）上测试样品的最大拉伸强度记录为45.27 MPa。此外，还采用了遗传算法-人工神经网络（GA-ANN），遗传算法-响应面方法（GA-RSM）和遗传算法自适应神经模糊接口系统（GA-ANFIS）等混合优化技术来优化这些工艺参数。在这些工具中，使用GA-ANN获得的最大精度为99.89％，这将优化参数，例如填充密度100％，温度210°C和速度124.778 mm / s，以实现最大拉伸强度47.0212 MPa。该检查的结果将有助于添加的物质生产单元选择具有改善的机械性能的FDM零件制造的输入因子的最佳因子值。