基于随机网格的变形加工界面附近塑性流动DIC分析

Deepika Gupta, K. Viswanathan
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引用次数: 0

摘要

数字图像相关(DIC)是一种原位分析技术,在过去的二十年里在力学界得到了广泛的应用。尽管如此,准确计算应变和位移场,特别是在界面和自由表面,仍然是一个核心挑战。这个问题尤其严重,因为材料在自由表面和界面附近流动,对于理解几种变形加工配置(如加工和成形)的力学至关重要。存在两种常见的DIC实现,它们利用有关变形的局部或全局信息。局部技术在子集之间缺乏连续性,而全局方法尽管确保了连续性,但无法准确地估计接口上的字段。此外,全局DIC需要网格细化来捕获异质变形,并且通常在计算上很昂贵。局部方法和全局方法最终都使用插值格式获得连续位移场,并使用有限差分格式计算应变。然而,这些也带来了额外的限制,如界面上的伪应变和实验数据的丢失。在这项工作中,我们提出了一种基于随机网格的方案,该方案使用局部相关搜索,同时利用全局信息。我们的算法基于前向6参数(位移及其一阶导数)牛顿-拉夫森(N-R)搜索。首先生成一个底层随机网格,用于定位相关方案的子集中心。然后用三角法计算二阶导数。多个随机网格实现使平均与最小的数据丢失,从而消除了后处理的需要。二阶导数的使用确保了连续的应变场,否则将需要基于十二个参数(位移,其一阶和二阶导数)的相关搜索。通过合成非均匀位移场的标准测试案例,验证了该方法的有效性,并在实际加工和变形加工中得到了应用。
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Random Grid-Based DIC Analysis of Plastic Flow Near Interfaces in Deformation Processing
Digital Image Correlation (DIC), an in situ analysis technique, has gained widespread popularity within the mechanics community over the past two decades. Despite this, accurate computation of strain and displacement fields, especially at interfaces and free surfaces, remains a central challenge. This problem is particularly acute since material flow near free surfaces and interfaces is paramount for understanding the mechanics of several deformation processing configurations, such as machining and forming. Two common DIC implementations exist, and they exploit either local or global information about the deformation. Local techniques suffer from a lack of continuity across subsets, while global methods, despite ensuring continuity, fail to estimate fields at interfaces accurately. Furthermore, global DIC necessitates grid refinement to capture heterogeneous deformation and can often be computationally expensive. Both local and global methods finally use interpolation schemes to obtain continuous displacement fields, along with a finite difference scheme to compute strains. However, these present additional limitations, such as spurious strains at interfaces and loss of experimental data. In this work, we present a random grid-based scheme that uses local correlation search, while simultaneously exploiting global information. Our algorithm is based on a forward 6-parameter (displacement and its first order derivatives) Newton-Raphson (N-R) search. An underlying random grid is first generated and serves to locate subset centers for the correlation scheme. Second derivatives are then computed using a triangulation method. Multiple random grid realizations enable averaging with minimal data loss, thereby eliminating the need for post-processing. The use of second-order derivatives ensures continuous strain fields, which will otherwise need a twelve-parameter (displacement, its first and second derivatives) based correlation search. We establish the validity of our scheme using standard test cases derived from synthetic non-homogeneous displacement fields and demonstrate its utility in practical machining and deformation processing applications.
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