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

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 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: Lp = 225 W, 250 W, 275 W, and Fl = 4.8 mm, 5.0 mm, 5.2 mm. The factor Pf was used with two levels, 9.40 g/s and 13.35 g/s. Analysis of variance allowed identifying that the Pf affect the Bh, Bh/Bw ratio, d and wa. The increase in Laser power (Lp) resulted in an increase of the geometric characteristics Bw and Dp. The first FIS, for predicting the bead's geometrical characteristics, presented high adequacy (relative error up to 8.43 %) for assessing the experimental conditions. The second FIS indicated the best possible interaction, given the studied operating conditions and the variables evaluated. The maximum Output Defuzzified Index (ODI = 0.845) was obtained with the deposition process parameters Lp = 250 W, Fl = 5 mm, and Pf = 9.40 g/s.