Zhejiang Ocean University

Unlocking the high-performance potential of Hydraulically-Driven Soft Robotic Arms (HDSRAs) requires computationally tractable dynamic models that are both physically faithful and rigorously validated, a combination that remains a critical challenge. This paper addresses this gap by presenting a systematic framework for the modeling, identification, and multi-faceted validation of such systems. Central to the framework is an enhanced coupled dynamic model incorporating often-neglected physical phenomena, including stiffness coupling, Rayleigh damping, and pressure-dependent hydraulics. The framework’s value is then established through a cohesive suite of four targeted experimental studies. An ablation study first quantitatively confirms the necessity of each model enhancement. A comparative analysis subsequently demonstrates the model’s superior accuracy against representative existing methods. A model-based feedforward control experiment then proves the model’s practical utility by significantly improving trajectory tracking performance. Finally, a generalization study on a more complex tri-chamber arm confirms the framework’s scalability. This work delivers not just a model, but a fully validated, high-fidelity “digital twin” that provides a solid foundation for designing high-performance controllers for a broad class of HDSRAs.

In-situ monitoring methods and deep learning models are increasingly being used for the quality assessment of parts fabricated using laser powder bed fusion to overcome the limitations of poor process repeatability. However, the massive data collection required for part-quality monitoring results in high transmission loads and storage costs. To address this problem, this study utilized the compressed sensing theory to acquire compressed photodiode signals. These signals were then used to train and test convolutional neural networks (CNN) to identify the lack-of-fusion, normal, and keyhole modes. At a compressive-sampling rate of 25%, the classification accuracy decreased from 93.1% (raw signals) to 79.3%. However, increasing the compression rate from 25% to 90% did not significantly decrease the classification accuracy. The linear mapping of the raw signal via a Gaussian measurement matrix causes coordinate information folding, thereby impairing the representation of latent features. Therefore, Gaussian process modeling was adopted for the features extracted using a pretrained CNN to mitigate the temporal information collapse and allow the compressed signals to achieve an accuracy comparable to that of the raw data. Furthermore, the sparsity and rank complexity of the melt-pool radiation signals were evaluated using sparse representation and principal component analysis.