Experimental Study on the Manufacturing of Steel Inclined Walls by Directed Energy Deposition Based on Dimensional and 3D Surface Roughness Measurements

Abstract

Robotic-directed energy deposition has attracted the attention of the research community and industry as a process capable of producing large metallic parts. The selection of the manufacturing conditions is a critical step in improving the process efficiency and quality of the produced parts. The present work aims at analyzing the geometry and surface topography of walls built using several conditions and inclination angles, without additional supports except for the substrate. The walls were made of AWS A5.18. ER70S-6 steel using the Wire Arc Additive Manufacturing process. The study used both dimensional and 3D topography measurements to analyze the results. As findings, the travel speed played an important role in the size of the cross-section due to the heat input to the welding zone. Heat accumulation was a critical factor in the size and accuracy of the beads. Moreover, intermediate cooling provided structures with more uniform dimensions, smaller width, and higher layer growth. The inclination of the pieces influenced the width and uniformity of the beads, generating minor imperfections on the downside of the pieces because of gravity.

Characterization of 5356 Aluminum Walls Produced by Wire Arc Additive Manufacturing (WAAM)

“…The design of experiments was created by fixing one parameter and varying the two others using two levels. In this sense, A, E, and V tests were performed at a fixed travel speed of 925 mm/min, a cooling time of 60 s and a ‘Back and forth’ path strategy, respectively [ 13 ]. These testing conditions are displayed in Table 4 .…”

“…Yang et al [ 12 ] proposed using the double electrode–gas metal arc welding (DE-GMAW) process to minimize energy transfer to the base metal. Pereira et al [ 13 ] studied the robotic WAAM process to analyze the geometry and surface topography of inclined walls made of AWS A5.18. ER70S-6 steel.…”

Surface Properties and Tribological Behavior of Additively Manufactured Components: A Systematic Review

“…Therefore, 10 articles dealing with metal additive manufacturing were identified. Interestingly, an equal number of articles dealing with the relatively new wire-arc additive manufacturing (WAAM) [10][11][12][13][14] and more conventional laser powder bed fusion processes for metal (LPBF-M) [15][16][17][18][19] could be identified. From the analyzed research, the use of machine learning in roughness prediction should be highlighted.…”

“…Predicting or measuring the roughness Metal LPBF, WAAM [10][11][12][13][14][15][16][17][18][19] Polymer SLS, FFF [20][21][22][23][24][25][26][27] Methods for reducing roughness Metal LBF, DMLS [29,50,[56][57][58][59][60][61][62][63][64][65][66] Polymer [28] Effects of printing and process parameters on roughness Metal LBF, WAAM, GMAW [30][31][32][33][34][35][36][37][38][39][40] Polymers FFF [41][42][43] Effects of roughness on part properties Corrosion LPBF-M [49,67] Mechanical LPBF-M, Arbitrary [44]…”

Modeling and optimization of height-related geometrical parameters for thin wall structures manufactured by metal additive manufacturing

“…Kumar et al [22] also developed second order response surface models to predict the bead geometries and used the model to produce near net shape thin wall structures. Although there have been a lot of efforts to model and minimize the microscale surface roughness of metallic parts produced by DED processes [23][24][25], there is a scarcity of studies that model and minimize the macroscale geometrical deviations of the parts. In a notable contribution by Lehmann et al [26], a comprehensive model was developed to investigate wall width deviations, with consideration given to process parameters such as WFS and TS.…”