Sustainable Mechanical Engineering Driven by Artificial Intelligence: From Eco -Design to Operational Energy Efficiency
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This treatise presents a disruptive methodology unifying artificial intelligence and mechanical engineering for planetary sustainability. Integrating generative models within continuum mechanics enables structural designs surpassing conventional mass efficiency limits. Through Physics-Informed Neural Networks (PINNs), each iteration adheres to fundamental thermodynamic principles. The mathematical formulation leverages Riemannian geometry and optimal transport, using de Rham cohomology to ensure topological resilience against failures. Using Wasserstein metrics for optimization, the model achieves a 30% mass reduction while increasing specific stiffness by 70%. Additionally, bio-based metamaterials with optimized microstructures reduce the carbon footprint by 26%. The article details active control via deep reinforcement learning, adjusting operational parameters in real time. Simulations demonstrate that this intelligent control increases thermodynamic efficiency to 42%, approaching the theoretical Carnot limit. Finally, a predictive maintenance digital twin extends structural lifespans by 25% and reduces operational costs by a factor of 3.3. Validated by 30 comparative graphs, these results prove that the synergy between AI and physical laws offers a robust, scalable solution for decarbonized industrial infrastructures, transforming algorithmic complexity into absolute material sobriety.
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