Abstract—This paper deals with the quality prediction of the Single Point Incremental forming (SPIF) process. The quality prediction can be evaluated through five parameters: Roughness surface, thickness, springback, circularity and position errors. Despite the contribution of many researchers on the development of sheet metal forming process, the geometric accuracy of the formed part remains less developed and analyzed. Several parameters are relevant to this inaccuracy namely the complexity of the part geometry, the Elasto-Plastic Material Behavior and tool path strategy. The present work proposes an experimental study for a complex geometry part (double truncated cone) obtained by SPIF. To product a truncated cone, two different trajectories were used: single and alternating directions. While in literature three quality parameters are generally used (roughness surface, thickness and springback) we propose in the paper to predict moreover two other quality parameters which are the circularity and the position errors. To deal with the nonlinearity of the problem we proposed to use an ANN and benefit of its generalization capacities to generate new and unpredictable situations through different input parameters: Strategy tool path, incremental step size, spindle speed, feed rate, and the forming angle. To improve the generalization accuracy of the neural network the modified back propagation algorithm was used in the learning phase of one hidden multilayer neural network. Experimental results show that the new proposed prediction model allows to reach an accurate prediction more than 96.74% with respect to all the quality parameters.

中文翻译：

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基于神经网络方法的实验研究的SPIF质量预测

摘要—本文涉及单点增量成形（SPIF）过程的质量预测。可以通过五个参数评估质量预测：粗糙度表面，厚度，回弹，圆度和位置误差。尽管许多研究人员对钣金成形工艺的发展做出了贡献，但成形零件的几何精度仍未得到充分开发和分析。与该误差有关的几个参数是零件几何形状的复杂性，弹塑性材料行为和刀具路径策略。本工作提出了通过SPIF获得的复杂几何形状零件（双截锥）的实验研究。为了产生截头圆锥体，使用了两种不同的轨迹：单方向和交替方向。虽然在文献中通常使用三个质量参数（粗糙度，表面，厚度和回弹），但我们在本文中建议用来预测另外两个质量参数，即圆度和位置误差。为了解决问题的非线性，我们建议使用人工神经网络及其优势，通过不同的输入参数（策略刀具路径，增量步长，主轴速度，进给速度和成形角度）来生成新的和不可预测的情况。为了提高神经网络的泛化精度，在一个隐藏的多层神经网络的学习阶段使用了改进的反向传播算法。实验结果表明，新提出的预测模型可以实现超过96个的准确预测。