Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 31, No. 1 (January 2018) 88-95   

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  PREDICTING FORCE IN SINGLE POINT INCREMENTAL FORMING BY USING ARTIFICIAL NEURAL NETWORK
 
M. Oraon and V. Sharma
 
( Received: September 14, 2017 – Accepted in Revised Form: November 30, 2017 )
 
 

Abstract    In this study, an artificial neural network was used to predict the minimum force required to single point incremental forming (SPIF) of thin sheets of Aluminium AA3003-O and calamine brass Cu67Zn33 alloy. Accordingly, the parameters for processing, i.e., step depth, the feed rate of the 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 the model output. To train the model, a Multilayer perceptron neural network structure and feed-forward backpropagation algorithm have been employed. After testing many different artificial neural network (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,-0.215 mean absolute error, show a very good agreement.

 

Keywords    SPIF; Input variables; ANOVA; Vertical force component

 

چکیده    در این مطالعه، یک شبکه عصبی مصنوعی برای پیش بینی حداقل نیروی مورد نیاز برای تشکیل تک مرحله ای (SPIF) ورق های نازک آلومینیوم AA3003-O و آلیاژ Cu67Zn33 برنج Calamine مورد استفاده قرار گرفت. بر این اساس، پارامترهای پردازش، به عنوان مثال، عمق قدم، سرعت تغذیه ابزار، سرعت واشر، زاویه دیوار، ضخامت ورق های فلزی و نوع مواد انتخاب شده به عنوان ورودی انتخاب شد و حداقل اجزای نیروی عمودی به عنوان خروجی مدل انتخاب شد. برای آموزش مدل، ساختار شبکه عصبی پروپرتن Multilayer و الگوریتم بازگشت عقب به جلو استفاده شده است. پس از آزمایش بسیاری از معماری های شبکه های عصبی مصنوعی (ANN)، ساختار بهینه ای از مدل 6-14-1 بدست آمد. نتایج، با یک رابطه همبستگی بین آزمایش ها به نیروی پیش بینی، -0.215 میانگین خطای مطلق، نشان می دهد که توافق بسیار خوبی است.

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