Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty
Yining Wang, Erva Ulu, Aarti Singh, Levent Burak Kara

TL;DR
This paper introduces an efficient method for identifying the worst-case external force contact location on a 3D object, using geometric sampling and regression to reduce computational costs in structural stress analysis.
Contribution
The authors propose a novel approach combining experimental design and regression modeling to efficiently predict worst-case forces without extensive finite-element analysis.
Findings
Significant speed-up over brute-force methods.
Accurate worst-case force prediction with minimal samples.
Enhanced efficiency when allowing small stress error tolerance.
Abstract
An important task in structural design is to quantify the structural performance of an object under the external forces it may experience during its use. The problem proves to be computationally very challenging as the external forces' contact locations and magnitudes may exhibit significant variations. We present an efficient analysis approach to determine the most critical force contact location in such problems with force location uncertainty. Given an input 3D model and regions on its boundary where arbitrary normal forces may make contact, our algorithm predicts the worst-case force configuration responsible for creating the highest stress within the object. Our approach uses a computationally tractable experimental design method to select number of sample force locations based on geometry only, without inspecting the stress response that requires computationally expensive…
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Taxonomy
TopicsProbabilistic and Robust Engineering Design · Manufacturing Process and Optimization · Advanced Multi-Objective Optimization Algorithms
