مشخصات پژوهش

عنوان	A Neural Networks Approach to Measure Residual Stresses using Spherical Indentation
نوع پژوهش	مقاله ارائه شده کنفرانسی
کلیدواژه‌ها	Residual stresses, spherical indentation, Neural Networks
چکیده	Abstract. In the present study an Artificial Neural Network (ANN) approach is proposed for residual stresses estimation in engineering components using indentation technique. First of all, load-penetration curves of indentation tests for tensile and compressive residual stresses are studied using Finite Element Method (FEM) for materials with different yield stresses and work-hardening exponents. Then, experimental tests are carried out on samples made of 316L steel without residual stresses. In the next step, multi-layer feed forward ANNs are created and trained based on 80% of obtained numerical data using Back-Error Propagation (BEP) algorithm. Then the trained ANNs are tested against the remaining data. The obtained results show that the predicted residual stresses are in good agreement with the actual data. Materials Science Forum توضیحات شورا: International Conference on Residual Stresses 9 (ICRS 9) این مقاله کنفرانسی است بر اساس کنفرانسی امتیاز داده می شود
پژوهشگران	امیرحسین محمودی (نفر اول)، میترا قنبری مطلوب (نفر دوم)، سروش حیدریان (نفر سوم)