Multi-criteria optimization of the warm hydroforming process of an aluminum component based on the adaptive neuro-fuzzy inference system

IF 6.1 1区 工程技术 Q1 ENGINEERING, MANUFACTURING Journal of Manufacturing Processes Pub Date : 2024-10-31 DOI:10.1016/j.jmapro.2024.10.075
Saeed Yaghoubi , Antonio Piccininni , Masoud Seidi , Pasquale Guglielmi
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Abstract

In sheet metal forming processes, improvement of product quality and its manufacturing conditions have always been considered as key aspects. In the case of processing aluminum alloys, the manufacturing process – especially if carried out in warm conditions – and its main parameters must be properly defined also to overcome the poor formability at room temperature.
In the present work, a multi-criteria optimization approach is applied on the manufacturing of an aluminum-based component via warm sheet hydroforming. Experimental tests were carried out changing the working temperature and oil pressure rate according to a factorial plan; formed blanks were analyzed in terms of final thickness distribution and shape accuracy (expressed in terms of die cavity filling) by means of a Digital Image Correlation (DIC) system. The distribution type of the data obtained from the experiments was determined using the Chi-Square goodness of fit test and, subsequently, the expected value of the distributions for each experimental test was calculated. Data collected from the experimental tests were used to train the adaptive neuro-fuzzy inference system (ANFIS), whose outcome predictions were ranked via simple additive weighting (SAW) and entropy methods. Based on the findings, the weight of main post-forming properties – the die filling percentage, the thinning distribution and the maximum thinning – was calculated as 27.20 %, 26.81 %, and 45.99 %, respectively. The decision-making tool allowed to apply a multi-criteria optimization: by assigning a larger weight to the die cavity filling, it was demonstrated that the process temperature plays a key role and has to be increased. On the other hand, the uniformity in the thickness distribution can be preserved by increasing the applied pressure rate. The approach, therefore, allows to tailor the working conditions (in terms of temperature and oil pressure rate) according to the post-forming property to be privileged.
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基于自适应神经模糊推理系统的铝制部件温压成型工艺的多标准优化
在金属板材成型工艺中,提高产品质量及其制造条件一直被视为关键环节。在加工铝合金时,必须适当确定制造工艺(尤其是在温热条件下进行的制造工艺)及其主要参数,以克服在室温下成形性差的问题。实验测试根据因子计划改变了工作温度和油压率;通过数字图像关联(DIC)系统分析了成型坯料的最终厚度分布和形状精度(以模腔填充度表示)。使用 Chi-Square 拟合度检验确定了实验数据的分布类型,随后计算了每个实验测试的分布预期值。从实验测试中收集的数据用于训练自适应神经模糊推理系统(ANFIS),通过简单加权法(SAW)和熵法对其结果预测进行排序。根据研究结果,计算出主要成型后特性--模具填充率、减薄分布和最大减薄--的权重分别为 27.20 %、26.81 % 和 45.99 %。决策工具允许应用多标准优化:通过赋予模腔填充更大的权重,可以证明工艺温度起着关键作用,必须提高。另一方面,厚度分布的均匀性可以通过增加施加的压力来保持。因此,这种方法可以根据所需的后成型特性来调整工作条件(温度和油压率)。
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来源期刊
Journal of Manufacturing Processes
Journal of Manufacturing Processes ENGINEERING, MANUFACTURING-
CiteScore
10.20
自引率
11.30%
发文量
833
审稿时长
50 days
期刊介绍: The aim of the Journal of Manufacturing Processes (JMP) is to exchange current and future directions of manufacturing processes research, development and implementation, and to publish archival scholarly literature with a view to advancing state-of-the-art manufacturing processes and encouraging innovation for developing new and efficient processes. The journal will also publish from other research communities for rapid communication of innovative new concepts. Special-topic issues on emerging technologies and invited papers will also be published.
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