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Autonomous AI Agent for End-to-End Component Data Extraction
1. Objective Streamline: complex, error-prone manual data entry Reallocate: engineering talent to high-value innovation Automation: Achieve...
Press
July 19, 2024
Traditional approaches, such as Trace Mapping FEA, often encounter significant challenges due to uncertainties in material properties and high computational costs. This whitepaper introduces Wiwynn's innovative Hybrid FEA method, which integrates experimental data from three-point bending tests with numerical simulations to more accurately and efficiently determine material properties.
The Hybrid FEA method has proven effective in evaluating risks such as DIMM insertion stress, solder ball cracking, and power pin mounting deformation. Case studies demonstrate that the Hybrid FEA method delivers results comparable to traditional methods while significantly reducing computational demands.
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2 min read
1. Objective Streamline: complex, error-prone manual data entry Reallocate: engineering talent to high-value innovation Automation: Achieve...
1 min read
Deploying large-scale AI clusters introduces engineering challenges that extend well beyond the individual server rack. From liquid cooling...
1 min read
This paper discusses the rapid expansion of AI workloads and the resulting transformation in data center infrastructure requirements. Traditional...