视觉辅助机器人低温去毛刺系统设计与实验研究

Design and Experimental Study of a Robotic Deburring System with Machine Vision and Low-Temperature Assistance

摘要: 铝合金构件在精密制造中面临一个关键挑战：其优异延展性在加工边缘易产生难以彻底去除的微细毛刺，严重制约工件最终质量。传统机器人去毛刺依赖于机械力，未能改变材料的塑性变形本质，导致高延展性材料加工效果不佳。为解决这一瓶颈，本文提出了一种融合机器视觉与低温辅助加工的机器人去毛刺新方法。本研究首先构建了一套高精度2D视觉系统，通过改进的边缘检测与轮廓追踪算法，实现了对毛刺特征的精准识别，并创新性地提出基于特征分类的路径点简化策略，将机器人加工路径数据量压缩约80%，显著提升了路径规划效率。本研究的核心创新在于引入液氮低温场，通过系统的力学性能测试与断口分析，证实低温(−196℃)使铝合金6061的硬度提升18%，延伸率降低7%，材料断裂机制由塑性主导转变为脆性主导。这一脆化效应从根本上将毛刺去除机制从“塑性撕裂”改变为“脆性断裂”。实验结果表明，在最优参数(主轴转速14,000 rpm，进给速度15 mm/s)下，该系统能完全清除边缘毛刺，工件表面无损伤，且表面粗糙度优化至Ra 0.388 μm。本研究不仅提供了一种高效的自动化去毛刺工艺方案，更深化了对低温辅助精密加工机理的认识。

Abstract: Aluminum alloy components face a critical challenge in precision manufacturing: their excellent ductility leads to the formation of fine burrs at machined edges that are difficult to remove thoroughly, severely compromising the final part quality. Traditional robotic deburring relies on mechanical force without altering the material’s plastic deformation nature, resulting in suboptimal outcomes for high-ductility materials. To address this bottleneck, this paper proposes a novel robotic deburring method that integrates machine vision with low-temperature assisted machining. This study first constructs a high-precision 2D vision system. Utilizing improved edge detection and contour tracking algorithms, it achieves accurate identification of burr features. Innovatively, a feature classification-based path point simplification strategy is proposed, compressing the robotic machining path data volume by approximately 80% and significantly enhancing path planning efficiency. The core innovation lies in introducing a liquid nitrogen cryogenic field. Systematic mechanical property tests and fracture analysis confirm that at low temperature (−196˚C), the hardness of aluminum alloy 6061 increases by 18% while its elongation decreases by 7%, and the material’s fracture mechanism shifts from being plasticity-dominated to brittleness-dominated. This embrittlement effect fundamentally changes the burr removal mechanism from “plastic tearing” to “brittle fracture.” Experimental results demonstrate that under optimal parameters (spindle speed 14,000 rpm, feed rate 15 mm/s), the system completely eliminates edge burrs without damaging the workpiece substrate, achieving a surface roughness of Ra 0.388 μm. This research not only provides an efficient automated deburring process solution but also deepens the understanding of the mechanisms involved in low-temperature assisted precision machining.