Model for Surface Finish Prediction and Optimization of Metal Additively Manufactured Parts
Navy STTR 2019.B - Topic N19B-T034
NAVAIR - Ms. Donna Attick - email@example.com
Opens: May 31, 2019 - Closes: July 1, 2019 (8:00 PM ET)
TECHNOLOGY AREA(S): Materials/Processes ACQUISITION PROGRAM: PMA275 V-22 Osprey
OBJECTIVE: Develop a modeling tool to rapidly predict the surface finish of metal additively manufactured (AM) parts as a function of AM process parameters and to determine AM processing and path planning to achieve optimal surface finish.
DESCRIPTION: Additive Manufacturing (AM) technologies, such as Laser Powder Bed Fusion (LPBF) process, have become increasingly important for the rapid production of industrial products. However, AM processes also pose challenges with associated features, such as defects and inherent surface roughness, which can degrade the fatigue performance. A significantly lower endurance limit was reported for specimens with inherent surface
roughness compared to polished ones [Ref 7]. Thus, the surface finish can influence the fatigue performance due to multiple stress concentrations. High cycle fatigue properties are especially dominated by surface finish [Ref 3].
There are several factors that can affect the surface finish of an AM part, such as powder distribution, energy density, build orientation, powder morphology, energy source, scan speed, hatch width, and staircase effect [Ref 4]. Although a fine powder granulation generally leads to better densities and surface qualities than a coarser material, the distribution of particle sizes can be even more important [Ref 4]. Some more in-depth physical phenomena, such as the balling process, can be commonly found as surface defects in which large drops, with size exceeding laser spot diameter, quickly spread out in droplets [Ref 6]. Geometric defects such as elevated edges disrupt the build process and distort subsequent surfaces [Ref 2]. The presence of overhangs or upwards facing surfaces produce different surface finish characteristics [Ref 2]. Although secondary processes, such as machining, may overcome the surface finish issue, the non-line-of-sight surfaces resulting from specific part geometries may not be accessible for machining.
A multi-physics modeling tool is needed to rapidly predict, in minutes to hours for a desktop environment, the surface finish of a metal AM part and to provide a strategy in AM processing/path planning to achieve the minimum roughness surface finish. There have been some efforts to model the surface finish of AM parts, such as modeling the roughness profile for the Fused Deposition Modeling (FDM) technology. This allowed the calculation of the roughness parameters as a function of the layer thickness and the deposition angle [Ref 1]. Another effort was carried out to describe the effects of partially bonded particles on the surface of an LPBF part [Ref 5], and work was also done to predict the roughness obtainable on AlSi10Mg processed by LPBF, taking into account the staircase effect, and the defects typical of this aluminum (Al) alloy. However, these models only address a portion of the issues affecting a part’s surface finish.
The Navy is seeking a more developed tool that captures the process parameters used by the machine. This modeling tool should be able to predict and optimize the surface finish, preferably through implementation of machine learning or another rapid convergence technique, based on key parameters such as, but not limited to, the part geometry, material properties (e.g., powder composition, particle size distribution, morphology), and initial AM processing parameters (e.g., powder distribution, energy density, build orientation, energy source, scan speed, hatch width, layer thickness, processing conditions). Users should be provided with an updated set of process parameters to achieve the optimal surface finish. Demonstration of the tool’s predictive and optimization capabilities should be on Ti-6Al-4V printed specimens. The tool should be designed to provide process parameters compatible with EOS powder bed machines.
PHASE I: Demonstrate the feasibility of a multi-physics modeling tool to predict the surface finish based on key process parameters. Predict the surface finish of some representative geometric features (overhangs, holes, radii, etc.) for typical LPBF processing. Compare the predicted surface finish of the test cases by printing Ti-6Al-4V samples to show the effectiveness of the model’s prediction capability. The Phase I effort will include prototype plans to be developed under Phase II.
PHASE II: Develop a full-scale multi-physics modeling prototype to rapidly optimize the surface finish based on various process parameters, including, but not limited to, powder distribution, energy density, build orientation, material properties, powder particle size distribution and morphology, energy source, scan speed, hatch width, layer thickness, part geometry, and processing conditions. Demonstrate the solution(s) in a real-world AM processing scenario and its possible transition into both military and commercial applications. Note: No Government test facility should be needed.
PHASE III DUAL USE APPLICATIONS: Develop standalone compliance with major powder bed machines (e.g., EOS, Renishaw, Concept Laser). The benefits in surface finish from this topic will directly benefit the aerospace, automotive, and energy industries utilizing AM by reducing the amount of post-process machining necessary to meet surface finish requirements for high performance parts.
1. Boschetto, A. and Giordano, V. “Modelling Micro Geometrical Profiles in Fused Deposition Process.” The International Journal of Advanced Manufacturing Technology, 2012, pp. 945-956.
2. Grasso, M. and Colosimo, B. “Process Defects and In Situ Monitoring Methods in Metal Powder Bed Fusion: A Review.” IOP Publishing Ltd., 2017. http://iopscience.iop.org/article/10.1088/1361-6501/aa5c4f/meta
3. Greitemeier, D., Dalle Donne, C., Syassen, F., Eufinger, J., and Melz, T. “Effect of Surface Roughness on Fatigue Performance of Additive Manufactured Ti-6A1-4V.” Journal of Materials Science and Technology, 2016, pp. 629- 634. https://www.tandfonline.com/doi/full/10.1179/1743284715Y.0000000053?scroll=top&needAccess=true
4. Spiering, A., Herres, N., and Levy, G. “Influence of the Particle Size Distribution on Surface Quality and Mechanical Properties in AM Steel Parts.” Rapid Prototyping Journal, 2011, pp. 195-202. https://www.emeraldinsight.com/doi/pdfplus/10.1108/13552541111124770
5. Strano, G., Hao, L., Everson, R., and Evans, K. “Surface Roughness Analysis, Modelling and Prediction in Selective Laser Melting.” Journal of Materials Processing Technology, 2013, pp. 589-597. https://www.sciencedirect.com/science/article/pii/S0924013612003366
6. Tolochko, N., Mozzharov, S., Yadroitsev, I., Laoui, T., Froyen, L., Titov, V., and Ignatiev, M. “Balling Processes During Selective Laser Treatment of Powders. Rapid Prototyping Journal, 2004, pp. 78-87. https://www.emeraldinsight.com/doi/pdfplus/10.1108/13552540410526953
7. Wycisk, E., Solbach, A., Siddique, S., Herzog, D., Walther, F., and Emmelmann, C. “Effects of Defects in Laser Additive Manufactured Ti-6A1-4V on Fatigue Properties.” 8th International Conference on Photonic Technologies LANE 2014, Physics Procedia 56 (2014), pp. 371-378. https://core.ac.uk/download/pdf/82733008.pdf
KEYWORDS: Surface Finish; Surface Roughness; Additively Manufacturing; AM; Modeling; Powder Bed Fusion