Name
Tech. Session XI - 177
Date & Time
Thursday, June 26, 2025, 10:30 AM - 10:55 AM
Description
Metal Binder Jetting (MBJ) has emerged as a promising additive manufacturing (AM) technology for the efficient production of large quantities of high-quality industrial components. Nevertheless, questions remain regarding its capacity to compete with other AM technologies in terms of delivering high-precision, repeatable, and stable outputs. This study addresses these gaps by conducting a comprehensive experimental investigation on the performance of a cutting-edge turnkey MBJ system, toward the optimization of 17-4PH stainless steel parts’ quality. Focus is given on the impact of the powder pre-processing and critical printing parameters, including powder temperature, chamber humidity, and layer thickness. The findings of this study present significant contribution to both scientific understanding and industrial application. The results reveal that layer thickness is the most influential parameter for achieving superior density and hardness, providing a roadmap for the optimization of the part performance. A 50 μm layer thickness yields approximately 10% higher hardness and more consistent density across parts. Simultaneously, the study demonstrates that there are coupled roles of additional parameters, including humidity conditions, that cannot be ignored to ensure robust, and high-precision production. The chamber humidity control could lead to absolute density improvements of about 1.5% on average. These results underline the potential of MBJ to deliver high-precision and stable production, provided that the process parameters are carefully managed. This work highlights MBJ’s competitive advantage and its capability to address industrial demands for robust and repeatable additive manufacturing solutions.
Location Name
Regency F
Full Address
Hyatt Regency
220 N Main St
Greenville, SC 29601
United States
Session Type
Technical Session
Paper #
NAMRC 177
Author List
Jose Galarza, Jorge Barron Jr, Luis Jimenez, Tamer Oraby, Jianzhi Li and Farid Ahmed
Paper Title
A machine learning approach to detect pores in laser powder bed fusion additive manufacturing
Presenter Name
Jose Galarza
Session Chair
Hariharan Krishnaswamy, Farid Ahmed
Presenter Email
jose.galarza01@utrgv.edu