Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Machine Learning Optimizers
This work addresses energy efficiency in cam profile design for mechanical systems, but it is incremental as it extends existing machine learning-optimized methods with an energy objective.
The paper tackled the problem of piecewise polynomial approximation by incorporating energy optimization as an additional objective, using modern machine learning optimizers to minimize elastic strain energy in cam profiles, resulting in smoother motion and Pareto-efficient trade-offs between approximation quality and energy consumption.
This work explores an extension of machine learning-optimized piecewise polynomial approximation by incorporating energy optimization as an additional objective. Traditional closed-form solutions enable continuity and approximation targets but lack flexibility in accommodating complex optimization goals. By leveraging modern gradient descent optimizers within TensorFlow, we introduce a framework that minimizes elastic strain energy in cam profiles, leading to smoother motion. Experimental results confirm the effectiveness of this approach, demonstrating its potential to Pareto-efficiently trade approximation quality against energy consumption.