IJE TRANSACTIONS A: Basics Vol. 31, No. 1 (January 2018) 69-76    Article in Press

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M. Oraon and V. Sharma
( Received: September 14, 2017 – Accepted: November 30, 2017 )

Abstract    In this study, artificial neural network was used to predict the minimum force required to single point incremental forming of thin sheets of Aluminium AA3003-O and calamine brass Cu67Zn33 alloy. Accordingly, the parameters for processing, i.e., step depth, feed rate of tool, spindle speed, wall angle, thickness of metal sheets and type of material were selected as input and the minimum vertical force component was selected as model output. To train the model, a Multi layer perceptron neural network structure and feed-forward back propagation algorithm has been employed. After testing many different ANN architectures, an optimal structure of the model i.e. 6-14-1 was obtained. The results, with a correlation relation between experiments to predicted force,-1.259 mean absolute error, show a very good agreement.


Keywords    SPIF; Input variables; ANOVA; Vertical force component


چکیده    در این مطالعه، شبکه عصبی مصنوعی برای پیش بینی حداقل نیروی مورد نیاز برای تک نقطه تشکیل افزایشی از ورق های نازک از آلومینیوم AA3003-O و کالامین برنج آلیاژ Cu67Zn33 استفاده شد. بر این اساس، پارامترهای برای پردازش، یعنی عمق گام، میزان تغذیه از ابزار، سرعت اسپیندل، زاویه دیوار، ضخامت ورق فلز و نوع مواد به عنوان ورودی انتخاب شدند و حداقل جزء نیروی عمودی به عنوان خروجی مدل انتخاب شد. آموزش مدل، یک لایه پرسپترون چند ساختار شبکه عصبی و الگوریتم انتشار غذا به جلو استفاده شده است. پس از آزمایش بسیاری از معماری های مختلف شبکه عصبی مصنوعی، یک ساختار بهینه مدل یعنی 6-14-1 به دست آمد. نتایج، با یک رابطه همبستگی بین آزمایش به نیروی پیش بینی شده، -1.259 میانگین خطای مطلق، توافق بسیار خوبی را نشان می دهد

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