Recently, robotic simulation environments have improved visual fidelity and have mitigated the limitations caused by the scarcity of real-world robotic data, thereby facilitating sim2real transfer. Modern embodied simulator heavily depends on standardized description formats such as URDF. However, current implementations often struggle with accurately modeling dynamic interactions within articulated systems, primarily due to imprecise collision meshes and suboptimal joint parameterization. To enable high-quality physical simulation of articulated objects, we introduce URDF-X, a plug-and-play enhancement module that overcomes these limitations through two key breakthroughs. For URDF-based articulation, we propose two key techniques: (1) an EdgeCNN architecture for collision mesh optimization that achieves order-invariant geometric feature learning via symmetric edge convolution operators, and (2) a simulation-guided joint parameter correction with spatial sampling that reduces positional errors by 61.9% compared to baseline URDF specifications. Evaluations on Objaverse-XL and PartNet datasets demonstrate that URDF-X improves articulation accuracy by 28.9% and achieves a 3.8 times faster collision detection in simulations. By incorporating URDF-X optimized 3D assets, the accuracy of simulated physical processes improves by 18.5%. Our method establishes a novel paradigm for physics-aware robotic simulation by effectively bridging the gap between dynamic simulation requirements and real-world physical processes.