Application of multiple regression and neural networks to synthesize a model for peen forming process planning

• Autorzy: Delijaicov S. , Fleury A.T. , Martins F. P. R.
• Języki publikacji: EN

Abstrakty

EN

Purpose: this paper aims to present a simple method to synthesize an empirically-based model that permit to estimate the maximum displacement of a plate when a shotpeening process values are known. Design/methodology/approach: This approach regards the difficulty to develop a mathematical model to describe the relationship between the shot peening process variables (shot diameter, impact velocity, static preload and coverage) and the curvature of the piece. Such a model was generated through the application of statistical inference methods - multivariable regression and neural networks – to a set of experimental data concerning the application of peen forming processes to a group of 215 aluminium 7050 alloy rectangular plates. Findings: Although the estimated displacements from both models comply reasonably well with the experimental data, the obtained results exposed the superiority of the regressive model concerning accuracy. Research limitations/implications: Shot peen forming, a die less forming process, is one of the most successful methods to produce slight and smooth curvatures on large panels and plates. Through the application of a regulated blast of small round steel shot on the piece surface, a thin internal layer of residual compressive stress causes the elastic stretching of the shotted surface, giving rise to a permanent non-plastic deformation of the whole piece. Although this forming process has been used since the fifties, especially by the aerospatial industry, a scientific method for peen forming process planning has not been developed yet. Originality/value: The referred model can be used as an engineering tool to aid setting up a peen forming process in order to produce a desired curvature on a given plate.

Słowa kluczowe

EN

peen forming; artificial neural network; aluminium alloy 7050

PL

formowanie; kulkowanie; sztuczna sieć neuronowa; stop aluminium

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2010
• Tom: Vol. 43, nr 2
• Strony: 651--656
• Opis fizyczny: Bibliogr. 14 poz., rys., tab., wykr.

Twórcy

autor

Delijaicov S.

• Centro Universitário da Fundacao de Ensino Inaciano, Av. Humberto de Alencar Castelo Branco, 3972, S. Bernardo do Campo, São Paulo, Brazil

autor

Fleury A.T.

• Centro Universitário da Fundacao de Ensino Inaciano, Av. Humberto de Alencar Castelo Branco, 3972, S. Bernardo do Campo, São Paulo, Brazil

autor

Martins F. P. R.

• Escola Politecnica da Universidade de Sao Paulo, Av. Mello Moraes, 2231, São Paulo, São Paulo, Brazil

Bibliografia

• [1] D. Clarke, S.S. Birley, The control of manual shot peening, Proceedings of the 1st International Conference on Shot Peening ICSP’1, Paris, 1981, 161-167.
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• [3] T. Wang, M.J. Platts, A. Levers, A process model for shot peen forming. Journal of Materials, Processing and Technology 172/2 (2006) 159-162.
• [4] M.N. Neelakantan, R.G. Green, S.H. Foo, R. John, Measurement and control of impact finishing processes, Proceedings of the 1st International Conference on Shot Peening ICSP’1, Paris, 1981, 147-158.
• [5] B. Barker, K. Young, L. Pouliot, Particle velocity sensor for improving shot peening process control, Proceedings of the 9th International Conference on Shot Peening ICSP’9, Paris, 2005, 385-391.
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• [7] S.A. Meguid, G. Shagal, J.C. Stranart, J. Daly, Three-dimensional dynamic finite element analysis of shot-peening induced residual stresses, Finite Element in Analysis and Design 31 (1999) 179-191.
• [8] K. Schiffner, C.D. Helling, Simulation of residual stress by shot peening, Computers and Structures 72 (1999) 329-340.
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• [13] J.M. Champaigne, Almen gage calibration, Proceedings of the 8th International Conference on Shot Peening ICSP’8, Munich, 2002, 108-113.
• [14] Z.L. Kovács, Redes Neurais Artificiais: Fundamentos e Aplicações, Livraria Física Editora, São Paulo 1996.