基于参考点云配准的涡轮叶片热障涂层机器人磨削在线自动夹紧误差补偿

This paper presents an automated clamping error compensation method for robotic grinding of thermal barrier coatings (TBCs) on turbine blades. In the eye-to-hand robotic grinding cell, a fixed line-laser scanner acquires a point cloud after each blade pickup. The key innovation is a reference-scan strategy: the first blade in a batch is calibrated once with manual assistance, and its scanned point cloud is stored as the reference, against which every subsequent pickup is localized by Trimmed-ICP fine registration. Because the clamping error is mechanically bounded and lies inside the ICP convergence basin, coarse registration is entirely bypassed, enabling fully automated batch production. An error propagation model demonstrates that the proposed compensation decouples the random per-pickup clamping error from the systematic error budget. Experiments over 530 pickup trials across four blade types achieve a mean compensation rate of 91%, post-compensation residuals below 0.2 mm, and a valid-registration rate of 98.5% (530 of 538 records) with no solver failures. Batch validation on 118 T1 blades confirmed uniform coating removal (35.97 ± 4.30 μm) and a reduction in surface roughness from Ra 7.92 μm to 2.57 μm, with the Ra ≤ 3.0 μm limit met at five of six measurement locations.