In the background of Industry 4.0, sustainable advanced manufacturing has become a pivotal challenge, necessitating the development of technologies that minimize resource consumption and environmental impact while maintaining high-quality production. Grinding technology plays a crucial role in this domain, providing solutions for processing hard and brittle materials with microscale accuracy, particularly when the grinding process is classified as precision machining. This study addresses the gaps in sustainable grinding by presenting a comprehensive review of lubrication strategies using an improved latent Dirichlet allocation (LDA) model integrated with Principal component analysis (PCA). The proposed model systematically identifies keyword distributions and topic clusters related to lubricant selection, providing insights into the evolving trends and practices in sustainable grinding. The findings enhance understanding of the main themes and future perspectives of sustainable grinding, bridging the gap between theoretical research and practical implementation in sustainable manufacturing.

Series Snake robots possess strong adaptability for unstructured environments, but their trajectory tracking control is hindered by nonlinear dynamics and model uncertainties. This article proposes an optimal tracking control strategy based on an actor–critic reinforcement learning framework. The method integrates line-of-sight guidance with serpentine gait generation, while a neural network identification system approximates the solution of the Hamilton–Jacobi–Bellman equation for unknown dynamics. Actor and critic networks are employed to update control policies and cost functions online, reducing dependence on precise models. Rigorous theoretical analysis proves that position and velocity errors achieve semi-global uniform ultimate boundedness. Both simulations and prototype experiments were conducted based on a servo-driven yaw-pitch linkage alternating series snake robot. The results verify that the proposed method can achieve accurate trajectory tracking, rapid convergence, and stable joint control, demonstrating its effectiveness and superiority compared to existing methods.

To address the complicated modeling process and inadequate explainability of current methods for improving the contour accuracy of ultraprecision machining (UPM), this study presented an Internet of Things (IoT)–based contour error compensation (CEC) framework. To achieve a convincing and real-time compensation solution, a hybrid mechanism-data-driven CEC model was created that integrated the 1DCNN-BiLSTM-attention model for predicting the axis actual positions, contour error estimation, and bidirectional compensation algorithms. Bayesian hyperparameter optimization and sensitivity analysis were used in the proposed models to improve the prediction accuracy of the actual position of each axis, with high-quality training datasets from well-designed experiments. Finally, validating the system on a three-axis ultraprecision milling machine demonstrated its superior performance. This study first demonstrated the feasibility of a deep learning approach for improving UPM accuracy, which will assist in accelerating digitalization and intellectualization for UPM.