StressDiffVis: Visual Analytics for Multi-Model Stress Comparison

Structural analysis is essential in modern industrial design, where engineers iteratively refine geometry models based on stress simulations to achieve optimized designs. However, comparing stress distributions across multiple model variants remains challenging due to the complexity of stress fields, which are high-dimensional, unevenly distributed, and dependent on intricate geometric structures. Existing tools primarily support single-model analysis and lack dedicated functionalities for multi-model comparison. As a result, engineers must rely on manual, cognitively demanding visual inspections, making it difficult to systematically identify and interpret stress variations across design iterations. To address these limitations, we propose StressDiffVis, a visual analytics approach that facilitates stress field comparison across multiple structural models. StressDiffVis employs a volumetric representation to encode stress distributions while minimizing occlusion, enabling voxel-wise difference analysis for model comparison. To support localized analysis, we introduce model segmentation, grouping voxels with similar stress patterns across models. StressDiffVis integrates these techniques into an interactive interface with a tree view, organizing models by the iterative design process, and a comparison view, using a matrix layout for detailed comparisons. We demonstrate the effectiveness of StressDiffVis through two case studies illustrating its utility in comparative stress analysis. In addition, expert interviews confirm its potential to enhance engineering workflows.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Visualization and Computer Graphics
Published
2025-11-27
DOI
https://doi.org/10.1109/tvcg.2025.3634642
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

StressDiffVis: Visual Analytics for Multi-Model Stress Comparison

Hui Song, Shaowu Gao, Zikun Deng, Yi Cai et al.
IEEE Transactions on Visualization and Computer Graphics
Data Visualization and Analytics
article

StressDiffVis: Visual Analytics for Multi-Model Stress Comparison

Hui Song, Shaowu Gao, Zikun Deng, Yi Cai, Daniel Chen, Jiabao Huang
article en

Abstract

Structural analysis is essential in modern industrial design, where engineers iteratively refine geometry models based on stress simulations to achieve optimized designs. However, comparing stress distributions across multiple model variants remains challenging due to the complexity of stress fields, which are high-dimensional, unevenly distributed, and dependent on intricate geometric structures. Existing tools primarily support single-model analysis and lack dedicated functionalities for multi-model comparison. As a result, engineers must rely on manual, cognitively demanding visual inspections, making it difficult to systematically identify and interpret stress variations across design iterations. To address these limitations, we propose StressDiffVis, a visual analytics approach that facilitates stress field comparison across multiple structural models. StressDiffVis employs a volumetric representation to encode stress distributions while minimizing occlusion, enabling voxel-wise difference analysis for model comparison. To support localized analysis, we introduce model segmentation, grouping voxels with similar stress patterns across models. StressDiffVis integrates these techniques into an interactive interface with a tree view, organizing models by the iterative design process, and a comparison view, using a matrix layout for detailed comparisons. We demonstrate the effectiveness of StressDiffVis through two case studies illustrating its utility in comparative stress analysis. In addition, expert interviews confirm its potential to enhance engineering workflows.

IEEE Transactions on Visualization and Computer GraphicsVol. 32(1)
Ministry of Education of the People's Republic of China (CN), Bay Area Medical Center (US), South China University of Technology (CN)
Openalex Percentile: Top 62%
Data Visualization and Analytics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.