Abstract




 
   

IJE TRANSACTIONS B: Applications Vol. 31, No. 2 (February 2018) 382-393   

PDF URL: http://www.ije.ir/Vol31/No2/B/24-2700.pdf  
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  MODELLING OF CONVENTIONAL AND SEVERE SHOT PEENING INFLUENCE ON PROPERTIES OF HIGH CARBON STEEL VIA ARTIFICIAL NEURAL NETWORK
 
E. Maleki and G. H. Farrahi
 
( Received: June 10, 2017 – Accepted in Revised Form: September 08, 2017 )
 
 

Abstract    Shot peening (SP), as one of the severe plastic deformation (SPD) methods is employed for surface modification of the engineering components by improving the metallurgical and mechanical properties. Furthermore artificial neural network (ANN) has been widely used in different science and engineering problems for predicting and optimizing in the last decade. In the present study, effects of conventional shot peening (CSP) and severe shot peening (SSP) on properties of AISI 1060 high carbon steel were modelled and compared via ANN. In order to networks training, the back propagation (BP) error algorithm is developed and data of experimental test results are employed. Experimental data illustrates that SSP has superior influence than CSP to improve the properties. Different networks with different structures are trained with try and error process and the one which had the best performance is selected for modeling. Testing of the ANN is carried out using experimental data which they were not used during networks training. Distance from the surface (depth), SP intensity and coverage are regarded as inputs and microhardness, residual stress and grain size are gathered as outputs of the networks. Comparison of predicted and experimental values indicates that the networks are tuned finely and adjusted carefully; therefore, they have good agreement.

 

Keywords    artificial neural network, severe shot peening, residual stress, coverage, Almen intensity

 

چکیده    فرایند ساچمه زنی به عنوان یکی از روشهای تغییر شکل دائمی شدید، در بسیاری از قطعات مهندسی به منظور ارتقا خواص مکانیکی و متالورژیکی سطوح استفاده می شود. از طرفی دیگر، در دهه گذشته شبکه های عصبی مصنوعی به میزان قابل توجهی برای پیش بینی و بهینه سازی در مسائل مختلف علوم و مهندسی مورد استفاده قرار گرفته اند. در این مقاله اثرات ساچمه زنی های معمولی و شدید بر روی خواص فولاد پرکربن AISI 1060 با استفاده از شبکه عصبی مصنوعی مدلسازی شده و مورد مقایسه قرار گرفته. برای آموزش شبکه الگوریتم خطای انتشار بازگشتی به همراه داده های تجربی-آزمایشگاهی به دست امده استفاده شده اند. نتایج آزمایشگاهی نشان می دهند که ساچمه زنی شدید اثراتی به مراتب بیشتر و بهتر نسبت به ساچمه زنی معمولی داشته. شبکه های گوناگونی با استفاده از آزمون و خطا برای یافتن ساختار بهینه ی شبکه آموزش داده شدند و ارزیابی شبکه توسط داده هایی که در فرآیند اموزش مورد استفاده قرار نگرفته بودند صورت گرفت. عمق، میزان شدت و میزان پوشش دهی به عنوان ورودی و سختی، تنش پسماند و اندازه دانه ها به عنوان خروجی شبکه در نظر گرفته شدند. مقایسه نتایج پیش بینی شده و نتیایج ازمایشگاهی تطابق قابل ملاحظه و قابل قبولی را در فرایند مدلسازی نشان می دهند.

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