This thesis aims to investigate the manufacturability of nickel-based superalloys, IN625 and IN718, using the laser powder bed fusion (LPBF) process. The study provides a better understanding of the process-structure-property of nickel-based superalloys, their fatigue life, and subsequent post-processing.
First, the process-structure-property was investigated by selecting a wide range of process parameters to print coupons for IN625 and IN718. Next, a subset of process parameters was defined that would produce high relative density (>99%), low surface roughness (~2 μm), and a low tensile RS.
Second, a multi-scale finite element model was constructed to predict the temperature gradients, cooling rates, and their effect on RS. At constant energy density, RS is affected by scan speed, laser power, and hatch spacing, respectively.
Third, the optimum set of parameters was used to manufacture and test as-built and shot-peened samples to investigate the fatigue life without costly heat treatment processes. It was found that shot peening resulted in a fatigue life comparable to wrought heat-treated unnotched specimen. Additionally, IN625 had a better fatigue life compared to IN718 due to higher dislocations density as well as the absence of γ´ and γ´´ in IN718 due to the rapid cooling in LPBF.
Finally, the effect of post-processing on dimensional accuracy and surface integrity was investigated. A new approach using low-frequency vibration-assisted drilling (VAD) proved feasible by enhancing the as-built hole accuracy while inducing compressive in-depth RS compared to laser peening, which only affects the RS. These favorable findings contributed to the scientific knowledge of LPBF of nickel-based superalloys by determining the process parameters optimum window and reducing the post-processes to obtain a high fatigue life, a better dimensional accuracy, and improved surface integrity. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27431 |
| Date | January 2022 |
| Creators | Balbaa, Mohamed |
| Contributors | Elbestawi, Mohamed, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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