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Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference

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Abstract

Laser metal deposition (LMD) process is an additive manufacturing technique that has attracted the interest of the automotive and aerospace industries due to its ability to manufacture parts with complex geometries and different types of metallic materials. However, the structure of the deposited layers and the geometrical characteristics of the manufactured parts are influenced by the interaction among the deposition process parameters. In this paper, fuzzy inference (FIS) technique was used to develop two models for predicting the geometrical characteristics and for optimizing the LMD process parameters. LMD was performed using AISI 316 stainless steel powder and substrate. An experimental design, based on factorial analysis, was used to correlate the influence of selected deposition process parameters, laser power (Lp), powder flow (Pf) and focal length (Fl) with the process geometrical characteristics bead height (Bh), bead width (Bw), depth of penetration (Dp), dilution (d) and wetting angle (wa). The factors Lp and Fl were used with three operating levels each, and the factor Pf was used with two operating levels. An analysis of variance allowed identifying that the Pf affects the Bh, Bh/Bw ratio, d and wa, as well as the increase in Lp showed an increasing of the geometric characteristics Bw and Dp. The first FIS, for predicting the bead’s geometrical characteristics, presented high adequacy (error up to 8.43%) for assessing the experimental conditions. The second FIS showed through the output defuzzified index (ODI) measured the best possible process parameters interaction, given the studied operating conditions and the output variables assessed.

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The data that support the findings of this work are available on request from the corresponding author.

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Acknowledgements

The authors would like to thank the Higher Education Personnel Improvement Coordination – Brazil (CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) which financed this study in part - Finance Code 001. In addition, the authors would like to thank the ROMI Industry for allowing the open and wide access to their facilities for studying the hybrid system ROMI DCM 620-5X HYBRID.

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Authors and Affiliations

  1. Post-Graduate Programme in Production Engineering, Methodist University of Piracicaba, São Paulo, Brazil

    Daniel René Tasé Velázquez

  2. Engineering School, Mackenzie Presbyterian University, São Paulo, Brazil

    André Luís Helleno

  3. College of Mechanical and Industrial Engineering, Oriente University, Santiago de Cuba, Cuba

    Hipólito Carvajal Fals

  4. Federal Technological University of Paraná, Curitiba, Paraná, Brazil

    Hipólito Carvajal Fals

  5. Insper - Institute for Education and Research, São Paulo, Brazil

    Raphael Galdino dos Santos

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  1. Daniel René Tasé Velázquez
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  2. André Luís Helleno
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  3. Hipólito Carvajal Fals
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  4. Raphael Galdino dos Santos
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Contributions

Daniel René Tasé Velázquez designed and implemented the fuzzy models and analysed the results. André Luís Helleno designed the experiments, helped verify the results and supervised the project. Hipólito Carvajal Fals helped to collect the data and measurements. Raphael Galdino dos Santos helped to perform the tests, collect data and verify the results. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Daniel René Tasé Velázquez.

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Velázquez, D.R.T., Helleno, A.L., Fals, H.C. et al. Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference. Int J Adv Manuf Technol 115, 1547–1564 (2021). https://doi.org/10.1007/s00170-021-07269-y

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