Institute of Mechatronics, Kaunas University of Technology, 51424 Kaunas, Lithuania.
Department of Applied Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania.
Sensors (Basel). 2021 Dec 21;22(1):18. doi: 10.3390/s22010018.
This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces. This study focuses on the influence of mechanical properties, process parameters and sheet thickness on the maximum forming force. Artificial Neural Network (ANN) and different machine learning (ML) algorithms have been applied to develop an efficient force prediction model. The predicted forces agreed reasonably well with the experimental results. Assuming that the variability of each input function is characterized by a normal distribution, sampling data were generated. The applicability of the models in an industrial environment is due to their relatively high performance and the ability to balance model bias and variance. The results indicate that ANN and Gaussian process regression (GPR) have been identified as the most efficient methods for developing forming force prediction models.
本文提出了一种从单点增量成形（SPIF）工艺参数中提取信息的方法。使用这项技术测量成形力有助于避免故障、识别最佳工艺，并实现常规控制。由于成形力还取决于工具和金属板之间的摩擦，因此提出了一种创新的解决方案，通过调节振动来主动控制摩擦力，从而取代对环境不友好的接触表面润滑。本研究重点研究了机械性能、工艺参数和板厚对最大成形力的影响。人工神经网络（ANN）和不同的机器学习（ML）算法已被应用于开发有效的力预测模型。预测力与实验结果相当吻合。假设每个输入函数的可变性都由正态分布来描述，生成了采样数据。由于其较高的性能和平衡模型偏差和方差的能力，这些模型在工业环境中的适用性得以保证。结果表明，ANN 和高斯过程回归（GPR）已被确定为开发成形力预测模型的最有效方法。
