Spatial clustering strategies for hierarchical multi-scale modelling of metal plasticity

Abstract

Abstract. In multi-scale simulations of material forming processes, macroscopic zones of nearly homogeneous strain response occur. In such zones the evolution of plastic anisotropy at each finite element integration point can be approximated from the properties at a representative point. We show how these zones can be identified by a clustering algorithm and can be utilized to reduce the computational cost of the simulation.1

STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities

“…Spatiotemporal clustering, one of the most significant branches of spatiotemporal data mining, aims to group a set of spatiotemporal objects into clusters so that objects within a cluster have high similarity with one another but are dissimilar to objects in other clusters (Bai, Cheng, Liang, Shen, & Guo, 2017;Khairullah, Gawad, Roose, & Van Bael, 2016;Shekhar, Zhang, & Huang, 2005). Spatiotemporal clustering analysis is a major tool in many engineering and scientific applications, including pattern recognition, data segmentation, outlier detection, discretization of continuous attributes, data reduction, image processing and noise filtering (Birant & Kut, 2007;Hagenauer & Helbich, 2013;He, Ling, Zhang, & Gong, 2018;Hu, Mao, & McKenzie, 2019;Li, Liu, Tang, & Deng, 2018;Shi et al, 2019;Song, Song, & Kuang, 2019).…”