Abstract An online monitoring approach for the uncertain loading-induced deformation of heavy-duty gantry machine tool base has been proposed by the combined use of Fiber Bragg Grating (FBG) sensors and a curvature-deformation algorithm. A placement strategy has been proposed to the deployment of these FBG sensors, ensuring to effectively sense the curvature. A cubic spline interpolation method has been applied to fully construct several continuous curvature functions. Deformation reconstruction principle based on these piecewise curvature functions has been elaborated and derived. FEM-based simulations for a prototyped base have been conducted to preliminary validate the effectiveness of the proposed approach. Experimental results further indicate that the maximum relative error of the reconstructed deformation values is decreased from 69.53% to 11% with an in-site calibration. The average of relative error with a value of less than 5.6% under different uncertain loadings, together with good consistency between the calculated deformation and detected values from the commercial sensors, have demonstrated the effectiveness and feasibility of the proposed approach. Several application experiments in a real gantry boring and milling machine tool base have been performed to further validate the feasibility of the proposed approach.

中文翻译：

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基于FBG的重型龙门机床底座不确定加载变形在线监测

摘要 结合光纤布拉格光栅（FBG）传感器和曲率变形算法，提出了一种重型龙门机床底座不确定加载变形在线监测方法。已经提出了一种放置策略来部署这些 FBG 传感器，以确保有效地感知曲率。已应用三次样条插值方法来完全构造多个连续曲率函数。阐述并导出了基于这些分段曲率函数的变形重建原理。已经对原型基地进行了基于 FEM 的模拟，以初步验证所提出方法的有效性。实验结果进一步表明，重构变形值的最大相对误差从69降低。53% 到 11% 使用现场校准。不同不确定载荷下平均相对误差小于5.6%，计算变形量与商用传感器检测值之间具有良好的一致性，证明了该方法的有效性和可行性。已经在真实的龙门镗铣机床基础上进行了几次应用实验，以进一步验证所提出方法的可行性。