Abstract
Wire and arc additive manufacturing (WAAM) is among the most promising additive manufacturing techniques for metals because it yields high productivity at low raw material costs. However, additional post-processing is required to remove redundant surface material from components manufactured by the WAAM process, and thus the productivity decreases. To increase productivity, multi-variable process parameters need to be optimized, including thermo-mechanical effects caused by high deposition rates. When the process is modeled, deposit shape and productivity are challenging to quantify due to uncertainty in multiple variables of the complicated WAAM process. Therefore, we modeled the WAAM process parameters, including uncertainties, using a Gaussian process regression (GPR) method, thus allowing us to develop a WAAM optimization model to improve both productivity and the quality of the deposit shape. The accuracy of the optimized output was verified through a close agreement with experimental values. The optimized deposited material had a wide effective area ratio, small height differences, and near 90° deposition angle, highlighting the usefulness of the GPR model approach to deposit nearly ideal material shapes.
Highlights
Additive manufacturing (AM) for metals has been a topic of interest in recent years as a means to improve the productivity of manufacturing processes in various industries, including in the aerospace, shipbuilding, and automotive industries
The experimental validation was performed using the optimization parameters obtained from the represent the wire arc additive manufacturing (WAAM) process parameters, while the cost function values are represented by the color
WAAM process deposition parameters were optimized to improve the quality of the deposit shape and the productivity of the WAAM
Summary
Additive manufacturing (AM) for metals has been a topic of interest in recent years as a means to improve the productivity of manufacturing processes in various industries, including in the aerospace, shipbuilding, and automotive industries. Confirmed that the surface waviness of a wall component decreased when the average arc current increased during a gas metal arc welding (GMAW) WAAM process using low-carbon steel wire. Hastelloy X alloy wire on 304 stainless steel in a TIG-based WAAM process They reported that the travel speed and current had the most significant impact on the quality of surface roughness. Ou et al [12] developed a three-dimensional heat transfer and fluid flow model of WAAM for H13 tool steel deposits and conducted a study on the uniformity of the resulting deposits They calculated the deposit shape and size depending on arc power, travel speed, and the wire feed rate. The output data are evaluated based on the change in three major process parameters: the wire feed rate, travel speed, and interpass time. The obtained LM images were converted to binary images using MATLAB, and the EA ratio, height difference, and deposit angle were measured using the binary images
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