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چکیده
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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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