King Saud University, Industrial Engineering Department, King Saud University, Riyadh, Saudi Arabia.
PLoS One. 2019 Aug 22;14(8):e0221341. doi: 10.1371/journal.pone.0221341. eCollection 2019.
Single-point incremental forming (SPIF) is a technology that allows incremental manufacturing of complex parts from a flat sheet using simple tools; further, this technology is flexible and economical. Measuring the forming force using this technology helps in preventing failures, determining the optimal processes, and implementing on-line control. In this paper, an experimental study using SPIF is described. This study focuses on the influence of four different process parameters, namely, step size, tool diameter, sheet thickness, and feed rate, on the maximum forming force. For an efficient force predictive model based on an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a regressions model were applied. The predicted forces exhibited relatively good agreement with the experimental results. The results indicate that the performance of the ANFIS model realizes the full potential of the ANN model.
单点增量成形（SPIF）是一种使用简单工具从平板上逐步制造复杂零件的技术；此外，这项技术具有灵活性和经济性。使用该技术测量成形力有助于防止故障、确定最佳工艺并实施在线控制。本文描述了使用 SPIF 的实验研究。本研究重点研究了四个不同工艺参数（步长、工具直径、板材厚度和进给速度）对最大成形力的影响。为了建立基于自适应神经模糊推理系统（ANFIS）的高效力预测模型，应用了人工神经网络（ANN）和回归模型。预测力与实验结果具有较好的一致性。结果表明，ANFIS 模型的性能实现了 ANN 模型的全部潜力。
