基于人工神经网络的矩形管拉伸弯曲回弹智能控制

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Journal of Manufacturing Science and Engineering-transactions of The Asme Pub Date : 2023-10-11 DOI:10.1115/1.4063737
Taekwang Ha, Torgeir Welo, Geir Ringen, Jyhwen Wang
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引用次数: 0

摘要

摘要回弹是导致金属成形产品质量下降的因素之一。先进的2D和3D拉伸弯曲工艺可用于制造具有回弹减少的复杂几何形状的轮廓。对于非线性回弹问题,人工神经网络(ANN)是实现回弹预测和控制的一种有吸引力的数据驱动方法。本工作的主要目的是利用人工神经网络控制二维和三维拉伸弯曲的回弹和提高几何质量。一般来说,人工神经网络是用从大量实验中收集的数据集来训练的,这造成了昂贵的成本和耗时的工作。在目前的工作中,所提出的人工神经网络的训练数据集是从实验和分析回弹模型中获得的。由于解析模型可以采用不同的弯曲角度、材料性质和几何形状,因此通过解析模型补充数据大大减少了人工神经网络训练所需的实验次数。与典型的回弹预测相反,所提出的人工神经网络基于所需的尺寸作为输入来综合机器设置。结果表明,通过确定人工神经网络预测提供的弯曲角度,可以控制回弹。该方法在二维和三维拉伸弯曲中进行了验证,与仅使用实验数据集训练的人工神经网络相比,其预测和控制性能优越。
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Smart Control of Springback in Stretch Bending of a Rectangular Tube by an Artificial Neural Network
Abstract Springback is one of the factors that causes decreased product quality in metal forming. Advanced 2D and 3D stretch bending process can be used to manufacture a complex geometry a profile with springback reduction. For a non-linear springback problem, an artificial neural network (ANN) is an attractive data-driven approach to achieving springback prediction and control. The main objective of the present work is to control springback and improve geometrical quality with an ANN in 2D and 3D stretch bending. In general, an ANN is trained with collected data sets from a large number of experiments, causing expensive costs and time-consuming work. In the present work, the training data sets for the proposed ANN are obtained from both experiments and an analytical springback model. As the analytical model can adopt different bending angles, material properties, and geometries, supplementary data by the analytical model significantly reduced the number of experiments needed for ANN training. Contrary to the typical springback predictions, the proposed ANN synthesizes the machine settings based on the desired dimensions as the inputs. It is shown that springback can be controlled by specifying the bend angles provided by the ANN prediction. The proposed ANN method was validated in 2D and 3D stretch bending, and its prediction and control performance is favorably compared to an ANN trained with only experimental data sets.
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
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