<p>There has been increasing demand
for 3D printed metals from aerospace & defense and automotive end-use
industries, due to their low manufacturing cost, and reduction in lead times. Although the significant
advancement in metal 3D printing promises to revolutionize industry, it is
constrained by a widespread problem: the cracks and other defects in the metal
3D printed parts. In this work, two major causes of defects in the laser
power bed fusion (L-PBF) process are focused: keyhole mode and spattering phenomena.
Both defect sources are highly dependent to the processing parameters. Although
extensive efforts have been made on experiments and computational models to
improve the quality of printed parts, the high experimental costs and large
computational intensity still limit their effect on the optimization of the
processing parameters. In addition, the uncertainty in the design process
further limits the accuracy of these models.</p><p>The aim of this thesis is to
develop a probabilistic design framework for reliability-based design in the
L-PBF process. The modeling framework spans physical models, machine learning
models, and probabilistic models. First, the keyhole mode and spattering
phenomena are simulated by physical models including computational fluid dynamics
(CFD) and smoothed particle hydrodynamics (SPH) methods, respectively. Then,
the data acquired by the physical models serve as the training data for machine
learning models, which are used as surrogates to alleviate the high
computational cost of physical models. Finally, the feasible design region is
computed by probabilistic models such as Monte Carlo simulation (MCS) and the
first order reliability method (FORM). The feasible design region can be used
assessing a satisfactory reliability not lower than the specified reliability
level.</p><p>The developed Gaussian process (GP) based machine learning
model is capable of predicting the remelted depth of single tracks, as a
function of combined laser power and laser scan speed in the L-PBF process. The
GP model is trained by both simulation and experimental data from the
literature. The mean absolute prediction
error magnified by the GP model is only 0.6 μm for a powder bed with
layer thickness of 30 μm,
suggesting the adequacy of the GP model. Then, the process design maps of two
metals, 316L and 17-4 PH stainless steel, are developed using the trained
model. The normalized enthalpy criterion of identifying keyhole mode is
evaluated for both stainless steels. For 316L, the result suggests that the
criterion should be related to the powder
layer thickness. For 17-4 PH, the criterion should be revised to
.</p><p>Moreover, a new and efficient
probabilistic method for the reliability analysis is developed in this work. It
provides a solution to address quality inconsistency due to uncertainty in the L-PBF
process. The method determines a feasible region of the design space for given
design requirements at specified reliability levels. If a design point falls
into the feasible region, the design requirement will be satisfied with a
probability higher or equal to the specified reliability. Since the problem
involves the inverse reliability analysis that requires calling the direct
reliability analysis repeatedly, directly using MCS is computationally
intractable, especially for a high reliability requirement. In this work, a new
algorithm is developed to integrate MCS and FORM. The algorithm finds the
initial feasible region quickly by FORM and then updates it with higher
accuracy by MCS. The method is applied to several case studies, where the
normalized enthalpy criterion is used as a design requirement. The feasible
regions of the normalized enthalpy criterion are obtained as contours with
respect to the laser power and laser scan speed at different reliability
levels, accounting for uncertainty in seven processing and material parameters.
The results show that the proposed method dramatically alleviates the
computational cost while maintaining high accuracy. This work provides a
guidance for the process design with required reliability.</p><p>The developed SPH model is used
to simulate the spattering phenomenon in the L-PBF process, to overcome the
limitation of commercial CFD packages, including their incapability of phase
change and particle sticking phenomena, which are however commonly seen in the
spattering process. The SPH model is capable to couple heat transfer, particle
motion and phase change. The sticking phenomenon observed in the experiment is
successfully reproduced by the SPH model using a similar scenario.</p><p>In summary, the modeling framework developed in this thesis
can serve as a comprehensive tool for reliability-based design in the L-PBF
process. The work is helpful for applying machine learning models in the
additive manufacturing field.</p><p>
</p><p><br></p><p>
</p><p> </p><p></p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12275342 |
| Date | 13 May 2020 |
| Creators | Lingbin Meng (8817110) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/MACHINE_LEARNING_AND_PROBABILISTIC_DESIGN_FRAMEWORK_FOR_LASER_POWDER_BED_FUSION_PROCESS/12275342 |
Page generated in 0.0025 seconds