Impedance-Based PZT Transducer and Fuzzy Logic to Detect Damage in Multi-point Dressers
Alternative techniques such as impedance-based lead zirconate titanate (PZT) transducers has emerged as an innovative approach for manufacturing monitoring process, because its flexibility of using low-cost piezoelectric diaphragms and its simple methodology in terms of apparatus by using the electromechanical impedance (EMI). In addition, this technique has been under several improvements due to the advance of artificial intelligent systems. On this point, the use of fuzzy logic systems has been reported in literature as an attractive combination to improve the process performance. Therefore, this study proposes an approach to detect damage in multi-point dresser based on EMI technique incorporating a fuzzy logic system. To this end, a fuzzy model is built considering the information obtained from representative damage indices corresponding to the different damage cases that are generated at the dresser. At the end, authors expected that the dressing operation can be optimized, preventing the operation from being performed with worn or damaged dressers and ensuring quality standards and precision to the grinding process, which have a high benefit to the manufacturing chain.
KeywordsElectromechanical impedance PZT transducers Fuzzy logic Tool condition Grinding process
The authors would like to thank the Sao Paulo Research Foundation (FAPESP), under grant #2016/02831-5 and grant #2017/16921-9 for supporting this research work.
- 3.Junior, P.O.C., Marchi, M., Henrique, C., Martins, R., Aguiar, P.R., Bianchi, E.C.: Spectral estimation of vibration signal for monitoring the wear of single-point dresser. Mater. J. Scielo. 21(04), 827–840 (2016)Google Scholar
- 4.Junior, P.O.C., Ruzzi, R.S., Lopes, W.N., et al.: A new approach for dressing operation monitoring using voltage signals via impedance-based structural health monitoring. KnE Eng. 3, 942–952 (2018)Google Scholar
- 5.Aguiar, P.R., Souza, A.G.O., Bianchi, E.C., Leite, R.R., Dotto, F.R.L.: Monitoring the dressing operation in the grinding process. Int. J. Mach. Mach. Mater. 5(1), 3–22 (2009)Google Scholar
- 9.Junior, P.O.C., Souza, R.V., Ferreira, F.I., Martins, C.H., Aguiar, P.R., Bianchi, E.C.: Wear monitoring of single-point dresser in dry dressing operation based on neural models. In: Proceedings of the IASTED International Conference on Modelling, Identification and Control (2017)Google Scholar
- 12.Adnan, M.R.H., Sarkheyli, A., Mohd Zain, A., Haron, H.: Fuzzy logic for modeling machining process: a review. Artif. Intell. Rev. 43(3), 345–379 (2015)Google Scholar
- 13.Miranda, H.I.C., Aguiar, P.R., Euzebio, C.D.G., Bianchi, E.C.: Fuzzy logic to predict thermal damages of ground parts. In: Scopus, pp. 434–441 (2010)Google Scholar
- 18.Ribeiro, D.M.S., Junior, P.O.C., Sodário, R.D., Marchi, M., Aguiar, P.R., Bianchi, E.C.: Low-Cost Piezoelectric transducer applied to workpiece surface monitoring in grinding process. In: ABCM International Congress Mechanical Engineering - COBEM, vol. 23, pp. 1–7 (2015)Google Scholar
- 22.Vieira, R., Park, S., Steffen, V., Inman, D.J.: Fuzzy logic applied to damage characterization through SHM techniques. Allen Inst. Artif. Intell. Semant. Sch. 1–8 (2007)Google Scholar
- 27.Murata, M.C.: Piezoelectric Sound Components. http://www.murata.com/
- 29.de Almeida, V.A.D., Baptista, F.G., de Aguiar, P.R.: Piezoelectric Transducers assessed by the pencil lead break for impedance-based structural health monitoring. IEEE Sens. J. 15(2), 693–702 (2015)Google Scholar