IJE TRANSACTIONS C: Aspects Vol. 30, No. 12 (December 2017) 1885-1893    Article in Press

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M. Mahdavi Jafari, G. R. Khayati, M. Hosseini and H. Danesh-Manesh
( Received: April 02, 2017 – Accepted in Revised Form: September 08, 2017 )

Abstract    This paper deals with modeling and optimization of the roll-bonding process of Ti/Cu/Ti composite for determination of the best roll-bonding parameters leading to the maximum Ti/Cu bond strength by combination of neural network and genetic algorithm. An artificial neural network (ANN) program has been proposed to determine the effect of practical parameters, i.e., rolling temperature, reduction in thickness, post-annealing time, post-annealing temperature and rolling speed on the bond strength of Ti/Cu composite. The most suitable model with correlation coefficient (R2) of 0.98 and mean absolute error (MAPE) 3.5 was determined using genetic algorithm (GA) and the optimum practice condition are proposed. Moreover, the sensitivity analysis results showed the post-annealing temperature with the negative effects is the most influential parameter on the strength of bonding.


Keywords    Ti/Cu/Ti clad composite; Roll-bonding; Bond strength; Genetic algorithm; Artificial neural network


چکیده    این مقاله به مدلسازی و بهنیه سازی فرایند اتصال نوردی کامپوزیت Ti/Cu/Ti با هدف تعیین پارامترهای بهینه جهت افزایش استحکام پیوند با استفاده از ترکیب شبکه عصبی و الگوریتم ژنتیک می پردازد. برنامه ای از شبکه ی عصبی مصنوعی (ANNs) برای تعیین اثر پارامترهایی نظیر دمای نورد، کاهش در ضخامت، زمان تابکاری اولیه، دمای تابکاری اولیه و سرعت نورد بر روی استحکام پیوند کامپوزیت Ti/Cu/Ti پیشنهاد گردید. بهترین مدل با ضریب تصحیح (R2) معادل 0.98 و میانگین مطلق خطا (MAPE) 3.5 تعیین و با استفاده از الگوریتم ژنتیک (GA) شرایط بهینه پیشنهاد شد. علاوه بر این، نتایج آنالیز حساسیت نشان داد که دمای تابکاری اولیه با اثر منفی اثرگذارترین عامل بر استحکام پیوند است.


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