Current Status and Progress of Robotic Cutting Systems

With the increase in labor costs, industrial robots have found widespread application across various industries. In particular, robotic cutting enables efficient and precise shaping of complex surface workpieces. However, due to differences in the structure, control strategies, and motion modes of robotic cutting systems, their relatively low stiffness makes them susceptible to vibrations, which negatively impact the accuracy and stability of the machining process. As a result, enhancing machining efficiency and quality has become a focal point of research. This paper provides a comprehensive review of various optimization techniques currently applied in robotic cutting, with a particular focus on preprocessing optimization, cutting process control, post-processing and data analysis, as well as dynamic balancing and adaptive control. The results indicate that different process parameters play a crucial role in the surface quality of workpieces. Surface roughness can be reduced by up to 30% in robotic cutting applications by adjusting cutting speed and feed rate, adaptive control systems can reduce vibration amplitude by up to 50%, and dynamic balancing reduces fluctuations in cutting forces by more than 40%, resulting in improved surface quality and tool life. In addition, the use of neural networks for process predictive analysis can reduce tool wear and further improve machining quality and efficiency. In addition, the combination of deep reinforcement learning and real-time feedback from the force sensor optimizes the cutting path and dynamically adjusts the machining parameters, resulting in a 15% increase in machining efficiency and a 20% reduction in energy consumption. Moreover, promising applications can also be found in areas such as intelligent material design, multimodal visual recognition, tool wear monitoring, and surface integrity inspection. Finally, this study presents a forward-looking analysis of future trends in robotic cutting and proposes an optimization scheme that integrates artificial intelligence and deep learning technologies, aiming to develop a more precise, efficient, and adaptive robotic cutting system.