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.

Modeling the thermo-mechanical deformations of machine tool structures in CFRP material adopting data-driven prediction schemes

Aggogeri F.
;
Merlo A.;Pellegrini N.
2020-01-01

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.
File in questo prodotto:
File Dimensione Formato  
Full Text_Aggogeri et al.pdf

solo utenti autorizzati

Descrizione: Full Text
Tipologia: Full Text
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 6.43 MB
Formato Adobe PDF
6.43 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/535728
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 8
social impact