Modeling the thermo-mechanical deformations of machine tool structures in CFRP material adopting data-driven prediction schemes. (November 2020)

• Record Type: Journal Article
• Authors: Aggogeri, Francesco; Merlo, Angelo; Pellegrini, Nicola
• Is Part Of: Mechatronics. Volume 71(2020)
• Publication Date: 2020-11
• Subjects: Thermo-mechanical effect compensation -- Data-driven models -- Fiber bragg grating sensors -- Machine learning; Computer integrated manufacturing systems -- Periodicals; Flexible manufacturing systems -- Periodicals; Mechatronics -- Periodicals; Productique -- Périodiques; Fabrication, Systèmes flexibles de -- Périodiques; Mécatronique -- Périodiques; Computer integrated manufacturing systems; Flexible manufacturing systems; Mechatronics; Periodicals; 629.89
• DOI: 10.1016/j.mechatronics.2020.102436
• Languages: English
• ISSNs: 0957-4158

Abstract

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.