Abstract The thermo-mechanical effects in machine tools (MTs) are represented by complex models since they may produce non-linear distortions overtime, impacting significantly on the machining accuracy. This paper aims to model the deformation of CFRP (Carbon-Fiber-Reinforced-Polymers) structures using data-driven schemes to predict and compensate the structural thermo-mechanical behavior. A novel study is presented to investigate the thermally-induced distortions of CFPR structural materials, selecting and positioning sensors, simulating and validating models to compensate the error in real-time. Anisotropic materials are becoming an effective solution to reduce structure mass and increase damping of a MT, nevertheless their physical complexity and the different thermal-coefficients at the interface with conventional materials may generate undesired effects, limiting the obtained advantages. The proposed strategy is based on the evaluation of a set of data-driven models simultaneously, identifying the most suitable solution and comparing finite element simulations with machine learning approach. The study is developed on a vertical axis frame made of CFRP material. The experimental validation is executed on a commercial 5-axis machine tool by varying the temperature conditions and evaluating the structural thermo-mechanical deformation effect on the Tool-Tip-Point (TTP) displacement. The thermo-mechanical behavior is measured by fiber Bragg grating (FBG) sensing technology embedded in the CFRP structure. Data-driven lab tests are evaluated in operational conditions during 36 h, considering: i) training-deployment periods (875 min interval), ii) typical machining stresses and iii) environmental perturbations. The final selected data-driven model is able to reduce the detected error lower than 10 μm range. In particular, the achieved results indicate a congruence between the TTP displacement measured and predicted with a residual error lower than 7.0 μm (Y-direction) using the ANN- multilayer perceptron algorithm.

中文翻译：

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采用数据驱动的预测方案对 CFRP 材料中机床结构的热机械变形进行建模

摘要 机床（MTs）中的热机械效应由复杂的模型表示，因为它们可能会随着时间的推移产生非线性变形，对加工精度产生显着影响。本文旨在使用数据驱动方案对 CFRP（碳纤维增强聚合物）结构的变形进行建模，以预测和补偿结构热机械行为。提出了一项新颖的研究，以研究 CFPR 结构材料的热致畸变、选择和定位传感器、模拟和验证模型以实时补偿误差。各向异性材料正在成为减少结构质量和增加 MT 阻尼的有效解决方案，然而，它们的物理复杂性和与传统材料界面处的不同热系数可能会产生不良影响，从而限制了所获得的优势。所提出的策略基于同时评估一组数据驱动模型，确定最合适的解决方案并将有限元模拟与机器学习方法进行比较。该研究是在由 CFRP 材料制成的垂直轴框架上开发的。通过改变温度条件并评估结构热机械变形对刀尖点 (TTP) 位移的影响，在商用 5 轴机床上执行实验验证。热机械行为通过嵌入 CFRP 结构的光纤布拉格光栅 (FBG) 传感技术进行测量。数据驱动的实验室测试在 36 小时的操作条件下进行评估，考虑：i) 培训部署期（875 分钟间隔），ii) 典型的加工应力和 iii) 环境扰动。最终选择的数据驱动模型能够将检测到的误差降低到 10 微米以下。特别是，所获得的结果表明，使用 ANN 多层感知器算法测量的 TTP 位移和预测的 TTP 位移之间存在一致性，残差小于 7.0 μm（Y 方向）。