Additive manufacturing of a monel alloy via directed energy deposition for marine and offshore applications - Chen, Ze (Nanyang Technological University, 2024)
The metal additive manufacturing (AM) market is projected to expand at a compound annual growth rate of around 20% from 2022 to 2030, with an estimated value of approximately USD 12 billion by the end of 2030. Despite the significant growth potential of metal AM technology, its adoption has been slow in the traditional marine and offshore sectors due to a lack of comprehensive study on the relationship between processes, microstructure, and properties of AM materials for marine and offshore applications. This thesis aims to expedite the acceptance and qualification of additively manufactured metal marine and offshore parts through a methodical investigation of Monel K-500 alloy, a widely employed Ni-based alloy with exceptional corrosion resistance and mechanical properties. The powder-fed laser-assisted directed energy deposition (L-DED) process is utilized due to its rapid deposition rate, unique capabilities of producing large-sized parts and repairing valuable components, and significant potential for in-situ alloying functions. These advantages render L-DED particularly desirable for the marine and offshore industry. First, the research investigates the optimal processing window for powder-fed L-DED processed Monel K-500 samples through microstructure analysis and mechanical testing. The results indicate that the optimal combination of process parameters, using Ytterbium-doped fiber laser with a principal wavelength of 1070 nm, involves a laser power of 1200 W and a scanning speed of 1800 mm/min. Full densification can be achieved within medium to high power levels (1200 to 1800 W). The fabricated Monel K-500 alloys show ultimate tensile strengths above 550 MPa and superior ductility up to 60%. The formation and evolution of grain structure reveal that the lower laser power results in finer equiaxed grains, while higher power leads to coarser grains exhibiting a strong <001> texture. The anisotropy in mechanical properties is attributed to the direction of grain boundaries, which is the main strengthening resource for metallic materials. The tensile strength along the horizontal direction of the sample is approximately 20% greater than that along the vertical direction. The precipitation of Ni3(Al, Ti), Al4Ni3, and Al3Ni with particle sizes ranging from 50 to 100 nm significantly diminishes the anisotropic behavior upon the post-process heat treatment. Second, the research also investigates the feasibility of repairing damaged Monel K-500 components using L-DED to deposit the Monel K-500 on the wrought materials of the same grade. The microstructure and mechanical properties of the bonding interface between the newly deposited material and the original material are analysed, as well as the influence of heterogeneous microstructure on tensile properties under two different loading directions. The study provides insights into designing optimal L-DED processes to achieve high performance for repaired metallic parts. Third, to enhance the strength of Monel K-500, Stellite 6 is introduced as a reinforcing material via a multi-material L-DED process. The distribution of those two materials is manipulated to create various heterogeneous microstructures. This work also uses mixed powder to investigate the in-situ alloying of Monel K-500 and Stellite 6 through the L-DED system. The microstructure and mechanical properties of the reinforced samples printed by both single powder and mixed powders are thoroughly analysed. This work provides valuable guidance for modifying marine and offshore alloys during L-DED fabrication to produce new composite materials with tailored properties. Finally, Machine Learning (ML) techniques are employed to screen process parameters and establish process-to-property relationships for Monel K-500 alloy fabricated using L-DED. The study utilizes surface images to predict density and strength over 120 sets of process parameters using ML techniques. Convolutional neural network (CNN) feature visualization depicts the abstract features from surface images. The demonstration software is created based on the ML model for identifying the process window and qualifying parts. By analyzing images, ML has demonstrated exceptional effectiveness in modelling intricate nonlinear relationships between printed surfaces and the density and tensile strength. The regression model demonstrates a commendable fit to the data, yielding mean absolute percentage errors of 0.18% and 1.77% in the prediction of density and strength, respectively, for block samples. This research contributes valuable insights into utilizing Monel K-500 in the field of AM for marine and offshore applications. The outcomes offer valuable perspectives on the viability, process parameters, and mechanical characteristics associated with the fabrication, repair, and alloying reinforcement of Monel K-500 through the L-DED technique.