1. Proceedings
  2. I3M
  3. 2020
  4. EMSS
  5. 10.46354/i3m.2020.emss.058

Fuzzy Logic-Based Inference System for Prediction of Energy Input in Laser Metal Deposited Aisi316 Single-Beads

  • a Daniel R. Tasé Velázquez ,
  • b André Luís Helleno,
  • c Maria Célia de Oliveira,
  • d Hipólito Carvajal Fals,
  • e Emilio Jimenez Macias 
  • a,c Post-Graduation Programme in Production Engineering, Methodist University of Piracicaba, São Paulo, Brazil
  • b,c Engineering School, Mackenzie Presbyterian University, São Paulo, Brazil
  • d College of Mechanical and Industrial Engineering, Oriente University, Santiago de Cuba, Cuba
  • d Visiting Professor at Federal Technological University of Paraná, Paraná, Brazil
  • e Department of Electrical Engineering (DIE), University of La Rioja, Logroño, Spain
Cite as
Tasé Velázquez D.R., Luís Helleno A., de Oliveira M.C., Carvajal Fals H., Jimenez Macias E. (2020). Fuzzy Logic-Based Inference System for Prediction of Energy Input in Laser Metal Deposited Aisi316 Single-Beads. Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), pp. 400-409. DOI: https://doi.org/10.46354/i3m.2020.emss.058

Abstract

Laser metal deposition (LMD) process has the capability to produce functional and complex 3D parts. The deposits characteristics are strongly influenced by the deposition parameters and volume energy input. The aims of this paper is to predict using a fuzzy logic-based inference system (FIS), the volume energy generated after depositing AISI 316 SS single-beads by LMD. Previously to FIS modeling, the influence of laser power (Lp), laser scan speed (Lss), powder flow (Pf) and focal length (Fl) on deposited beads were studied by analyzing the response-variables bead height (Bh) and bead width (Bw). ANOVA allowed identifying that Pf mostly affect the Bh, and Lp has greater significance on Bw. Predictive FIS modeled presented high adequacy assessing the experimental conditions, showing an average relative error of 4.76 %. Thus, the proposed FIS can be can be effectively utilized to predict the volume energy input and be integrated within an automated LMD environment to reduce complexities in process planning activities and increase process stability.

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