Analysis of deep drawing process to predict the forming severity considering inverse finite element and extended strain-based forming limit diagram
Publish place: Journal of Computational and Applied Research in Mechanial Engineering، Vol: 8، Issue: 1
Publish Year: 1397
نوع سند: مقاله ژورنالی
زبان: English
View: 360
This Paper With 10 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JCARME-8-1_004
تاریخ نمایه سازی: 19 خرداد 1398
Abstract:
An enhanced unfolding Inverse Finite Element Method (IFEM) has been used together with an extended strain-based forming limit diagram (EFLD) to develop a fast and reliable approach to predict the feasibility of the deep drawing process of a part and determining where the failure or defects can occur. In the developed unfolding IFEM, the meshed part is properly fold out on the flat sheet and treated as a 2D problem to reduce the computation time. The large deformation relations, nonlinear material behavior and friction conditions in the blank holder zone have also been considered to improve the accuracy and capability of the proposed IFEM. The extended strain-based forming limit diagram based on the Marciniak and Kuczynski (M-K) model has been computed and used to predict the onset of necking during sheet processing. The EFLD is built based on equivalent plastic strains and material flow direction at the end of forming. This new forming limit diagram is much less strain path dependent than the conventional forming limit diagram. Furthermore, the use and interpretation of this new diagram are easier than the stress-based forming limit diagram. Finally, two applied examples have been presented to demonstrate the capability of the proposed approach.
Keywords:
Sheet metal forming , Inverse finite element method , Strain path , Blank shape , Nonlinear deformation , Extended strain-based forming limit diagram
Authors
Mehdi Bostan Shirin
Assistant professor / Amir Kabir University of Technology
Ramin Hashemi
Iran University of Science and Technology
Ahmad Assempour
Sharif University of Technology
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :