Tailoring Material Properties Using Modulated Laser Beams

Manyalibo Matthews (16-ERD-042)


Focused laser light has long been used to transform materials in manufacturing processes to reduce cost, increase processing speed, and allow access to material property changes not possible with conventional processing. Examples include additive and subtractive manufacturing, spray and injection, friction-stir welding, polishing, and cladding and alloying. In particular, there is renewed interest in precision selective laser melting of high strength and refractory metals because of the widely recognized rapid-prototyping capabilities of this technique. In this process, scanning laser beams selectively melt and produce two-dimensional slices of a desirable pattern, growing the desirable part layer by layer. While seemingly simple, the process has many complex parameters: laser beam power, size, scan speed, powder material parameters, powder size and distribution over the ball sizes, and powder thickness. It would be highly desirable to develop methods to predict and demonstrate the optimal parameters of processing for new materials along every spatial point including the overhang area and edges. With this project, we reviewed the state of the art in local process control, analyzed the effect of varying beam shape, and demonstrated processing with a modulated laser beam. We found that temperature profiles associated with Laplace laser beam profiles showed slowest on-axis cooling as compared to monochromatic electromagnetic radiation Gaussian beams. This result can be exploited in future work to tune material properties through changes in temperature gradient and solidification velocities.

Background and Research Objectives

Research in additive manufacturing has gained tremendous momentum over the past decade because of the prospect of directly building complex three-dimensional parts (Figure 1) from computer-aided design files. During laser powder-bed fusion, processing parameters such as laser power, scan speed and pattern, and hatch spacing have typically been optimized to improve geometrical accuracy and reduce defect concentrations. In taking this macroscopic approach, however, the microstructure-property relationships underlying the performance disparities between conventionally machined and additively manufactured parts are often overlooked.

The ultimate goal of a priori parameter selection for tailored microstructures is in sight, with recent efforts made in electron-beam and laser additive manufacturing.1–6 Site-specific micro-structural control has numerous practical applications, such as in improving the fatigue life of a part by imposing deliberate textures at surfaces or stress-concentrating features, or in manufacturing components with functionally graded mechanical properties. In 2014, Körner et al. investigated the microstructural effect of varying “cross-snake” scan patterns every ten layers versus every single layer in Inconel alloy tensile samples.1 They found that columnar grains are formed when solidification occurs primarily in the building direction, while equiaxed grains are formed when the solidification direction varies frequently. In 2015, Dehoff et al. inscribed the letters “DOE” with highly misoriented equiaxed grains surrounded by columnar grains in an Inconel alloy block.2 This task was achieved by rapidly switching between point and line heat sources to manipulate local thermal gradients and solid–liquid interface velocities. Some microstructural control has also been demonstrated in laser additive manufacturing by varying laser power up to 1,000 W3 using multiple laser sources4 and varying scan strategies.5,6

We sought to understand and exploit the relationship between thermal environment and microstructure. Weld beads produced through powder bed additive manufacturing, as shown in Figure 1(b), are known to have complex and often uncontrolled microstructure. Our study described below has begun a pathway to construct mechanical parts that allow for variable material properties (e.g., brittle versus ductile) through the use of modulated laser energy.

Figure 1. (a) selective laser melting components showing the high degree of design flexibility afforded with this technique. (b) an electron back-scatter diffraction image showing the microstructure within a single track produced by scanning a laser through a steel 316-l metal powder bed.
Figure 1. (a) Selective laser melting components showing the high degree of design flexibility afforded with this technique. (b) An electron back-scatter diffraction image showing the microstructure within a single track produced by scanning a laser through a steel 316-L metal powder bed.

Scientific Approach and Accomplishments

Our focus was on understanding laser–material interactions that drive microstructural changes. The primary objectives of our study were (1) assess the existing state-of-the-art process control demonstrations, (2) develop thermal models to connect laser parameters and microstructure, (3) implement controls on available experimental platforms to spatially and temporally modulate laser light, and (4) demonstrate the ability to control microstructure and material properties using modulated laser light. Most of our goals were accomplished, although microstructure prediction and demonstration of beam shaping were not performed because of time constraints.
Assessment of Current Research Activities

The current theoretical understanding of how temperature gradients and solidification rates affect solidification microstructures and patterns is based on conventional metastable and rapid-solidification studies.7 Measuring the temperature gradients and solidification rates during laser powder-bed fusion remains experimentally challenging because of the localized nature of melting and the extreme rate of solidification. Myriad numerical efforts have been dedicated to predicting the gradients and rates, but modeling how they translate to particular solidification microstructures is still in the formative stages as phase-field models become computationally more efficient.8,9 These simulations are validated by necessarily complex experiments, such as those recently published by McKeown et al., combining in situ laser melting and dynamic transmission electron microscopy to identify growth-mode transitions in binary alloys.10 Real-time observations of laser-melted alloy solidification have been made using several techniques, including x-ray imaging,11 but simple binary systems are generally used as case studies. For multicomponent engineering alloys, particularly those that are polymorphic or multiphase, predictive models to describe solidification microstructures and patterns have yet to be established for laser heating conditions.

From classical solidification theory, cells and dendrites are bordered by regions of high solute and dislocation content as a result of lateral micro-segregation. Because plane-front solidification in alloys is only favored at very high temperature gradients and/or very low solidification rates, such as at the fusion boundary, perturbations in the planar solid–liquid interface develop and grow as cells or dendrites, rejecting solute atoms into the surrounding liquid phase. Pitting corrosion occurred preferentially in cell and dendrite cores during etching, most aggressively near the fusion boundary. This has previously been ascribed to molybdenum and chromium micro-segregation.12–15 Because the degree of micro-segregation is expected to increase with decreasing solidification rate,16 it can be inferred that solidification proceeds relatively slowly for some distance (up to ~40 μm) past the instability of the planar region. These slowly solidifying directional grains are terminated (or “pinched-off”) in the melt zone by more rapidly propagating grains (i.e., higher solidification rates) in the vicinity.

Because of their origins in discrete perturbations, cell and dendrites are also associated with low-angle boundaries and intragranular misorientations. Several studies have been dedicated to understanding how and to what extent these solidification defects affect the mechanical properties of additively manufactured materials.17,18 However, it should be noted that the features of cells and dendrites, including micro-segregation, can be greatly diminished by post-process annealing,19 while grain boundaries continue to persist and evolve. Keeping grain boundary strengthening and texture effects in mind, it is useful to consider the grain morphologies that form during laser additive manufacturing.

Many groups are actively pursuing microstructural control across a variety of build platforms and materials. Oak Ridge National Laboratory continues to demonstrate microstructure control using electron beam melting.2 A group at the Taiwan Industrial Technology Research Institute has developed an optical engine for controlling material grain microstructure, which is capable of modifying microstructure in a laser-based system.20 Carnegie Mellon University has demonstrated microstructure control through modifications in scan strategies.21 Several other groups are initiating efforts to dynamically control temperature changes and crystallization characteristics of parts in real time with the goal of making three-dimensional metal printing truly suitable for on-demand production.

Thermal and Microstructure-Process Modeling

Our modeling efforts were intended to focus on simplified powder models validated through comparison with full hydrodynamics cases that exist in the literature.22,23 Connection with microstructure for steel and Inconel alloys could then be made by assessing the thermal gradient field and the re-solidification rate, which then can be used to predict grain size and morphology. Because of time constraints, only thermal models were developed for this study. However, a recently granted exascale computing project that includes microstructural modeling through joint DOE and Department of Commerce collaboration will offer us further opportunities to explore microstructure tuning.

Figure 2 shows the results of our simulated beams shapes: Gaussian, Laplace, rectangular (flat top), and doughnut. The temperature rise was simulated using a linear heat flow equation solved with the COMSOL multiphysics code. The material properties for steel were taken as r = 8,027 kg/m3 for density, k = 16.25 W/(m • K) for thermal conductivity and Cp = 502 J/(kg • K) for heat capacity at constant pressure. The laser power was set to 3 W with a 1/e2 diameter of 25 mm. The results show that the temperature rise for the Gaussian beam was the fastest, while the Laplace beams heated more slowly. Specifically, the time to rise to 90% of the maximum temperature rise of about 800 K was 0.12, 0.33, 0.55 and 2.15 ms for the Gaussian, rectangular, doughnut, and Laplace beams, respectively. This shows that temperature changes can be affected by beam shape, which could potentially be used to affect material properties.

Figure 2. simulated laser beam shapes and resulting on-axis temperature rise associated with laser heating of a steel plate. (a) gaussian, (b) laplace, (c) rectangular (flat-top), and (d) doughnut beam shapes. (e) on-axis temperature history for a 3-w, 25-mm 1/e<sup>2</sup> diameter laser beam operating at 1,070 nm in continuous wave mode.
Figure 2. Simulated laser beam shapes and resulting on-axis temperature rise associated with laser heating of a steel plate. (a) Gaussian, (b) Laplace, (c) rectangular (flat-top), and (d) doughnut beam shapes. (e) On-axis temperature history for a 3-W, 25-mm 1/e2 diameter laser beam operating at 1,070 nm in continuous wave mode.
Laser Intensity Modulation Platform

While direct temporal modulation of laser intensity is typically performed by laser gain modulation, we proposed decoupling the source and modulation by using an external cavity technique. Specifically, we implemented an acousto-optic modulator capable of hundredfold modulation rates that of currently employed internal (gain) modulation techniques. We performed tests using a sinusoidal modulated laser beam. Spatial modulation of the beam has been achieved using standard pi-shapers (flat-top, Airy, and donut beam profiles) and anamorphic prism pairs (elliptical beam) with a laboratory demonstration of the technique.

Demonstration of Modulated Selective Laser Melting

With our initial tests successfully conducted in the laboratory, the groundwork has been laid for demonstrating the technique on an open-architecture selective laser melting system. This open-architecture software platform being jointly developed by General Electric and LLNL will be used to dynamically vary laser parameters as a function of part position. A new collaboration with the University of California at Irvine, Davis, and at Berkeley was formed as a result of our successful results, and promises to enhance further exploration and demonstration of microstructure and meso-structure tuning. While spatial modulation was not attempted, we did demonstrate the implementation of temporal modulation by directly adjusting laser power via an analog input to the laser controller. Results were not conclusive in that single-track morphologies and amount of powder displacement (which can affect defect densities) did not vary appreciably for 5.33- to 26.67-kHz modulation frequencies.

Impact on Mission

The strategic benefits of improved material properties through control of the laser beam in the selective laser melting process are manifold. First and foremost, we will have the ability to directly validate model predictions, which will increase accuracy of models, decrease time spent on indirect and recursive validation, and allow vast exploration of material tuning. Our work will have strategic impact to Livermore programs, adding to the growth of the additive manufacturing portfolio across directorates and capitalizing on the vast investments in lasers and optics at LLNL. There also exists the potential for fast-prototyping of laser parameters and customization of the selective laser melting process, opening doors to unique Livermore selective laser-melting competencies and process development. The research generally builds on core competencies in advanced in situ diagnostics, lasers and optical materials, and advanced materials and manufacturing, as well as Livermore's strategic focus area of stockpile stewardship science.


With this project, we explored a new technique to control selective laser melting of metal powders through modeling and experimentation. A review was performed, identifying current state-of-the-art methods in both electron beam and laser-based additive manufacturing. We successfully simulated the effect of beam shape on temperature histories to show that a Laplace-shaped beam significantly delays temperature increases relative to a simple Gaussian beam. Because of time constraints, beam-shaping experiments were not attempted. However, experiments using a temporally modulated beam were performed, and results for frequencies tested showed a clear effect on single-track morphology. Improvements to the laser delivery system of LLNL’s metal additive-manufacturing machines can be enabled through our study. Spatial and temporal beam shaping can now be used to modify density and mechanical properties to produce topologically optimized parts. The resulting enhancement of LLNL’s metal additive-manufacturing capability is expected to have an impact on the Laboratory’s mission for the NNSA.


  1. Körner, C., et al., “Tailoring the grain structure of IN718 during selective electron beam melting.” MATEC Web Conf. 14, 8001 (2014). http://dx.doi.org/10.1051/matecconf/20141408001
  2. Dehoff, R. R., et al., “Site specific control of crystallographic grain orientation through electron beam additive manufacturing.” Mater. Sci. Technol. 31, 931 (2015).
  3. Niendorf, T., et al., “Highly anisotropic steel processed by selective laser melting.” Metall. Mater. Trans. B. 44, 794 (2013). http://dx.doi.org/10.1007/s11663-013-9875-z
  4. Niendorf, T., et al., “Functionally graded alloys obtained by additive manufacturing.” Adv. Eng. Mater. 16, 857 (2014). http://dx.doi.org/10.1002/adem.201300579
  5. Thijs, L., et al., “Fine-structured aluminium products with controllable texture by selective laser melting of pre-alloyed AlSi10Mg powder.” Acta Mater. 61, 1809 (2013).
  6. Thijs, L., et al., “Strong morphological and crystallographic texture and resulting yield strength anisotropy in selective laser melted tantalum.” Acta Mater. 61, 4657 (2013). http://dx.doi.org/10.1016/j.actamat.2013.04.036
  7. Kurz, W., and D. J. Fisher, Fundamentals of solidification, 4th ed., CRC Press, Boca Raton, FL (1998).
  8. Asta, M., et al., “Solidification microstructures and solid-state parallels: Recent developments, future directions.” Acta Mater. 57, 941 (2009). http://dx.doi.org/10.1016/j.actamat.2008.10.020
  9. Badillo, A., and C. Beckermann, “Phase-field simulation of the columnar-to-equiaxed transition in alloy solidification.” Acta Mater. 54, 2015 (2006). http://dx.doi.org/10.1016/j.actamat.2005.12.025
  10. McKeown, J. T., et al., “In situ transmission electron microscopy of crystal growth-mode transitions during rapid solidification of a hypoeutectic Al–Cu alloy.” Acta Mater. 65, 56 (2014). http://dx.doi.org/10.1016/j.actamat.2013.11.046
  11. Clarke, A. J., et al., “X-ray imaging and controlled solidification of Al-Cu alloys toward microstructures by design.” Adv. Eng. Mater. 17, 454 (2015). http://dx.doi.org/10.1002/adem.201400469
  12. Qian, M., and J. N. DuPont, “Microsegregation-related pitting corrosion characteristics of AL-6XN superaustenitic stainless steel laser welds.” Corros. Sci. 52, 3548 (2010).
  13. Salinas-Bravo, V. M., and R. C. Newman, “An alternative method to determine critical pitting temperature of stainless steels in ferric chloride solution.” Corros. Sci. 36, 67 (1994). http://dx.doi.org/10.1016/0010-938X(94)90109-0
  14. Lee, D. J., et al., “Pitting corrosion behavior on crack property in AISI 304L weld metals with varying Cr/Ni equivalent ratio.” Mater. Des. 30, 3269 (2009). http://dx.doi.org/10.1016/j.matdes.2009.01.023
  15. Saeidi, K., et al., “Austenitic stainless steel strengthened by the in situ formation of oxide nanoinclusions.” RSC Adv. 5, 20747 (2015). http://dx.doi.org/10.1039/C4RA16721J
  16. Zhang, Y., and J. Li, “Characterization of the microstructure evolution and microsegregation in a Ni-based superalloy under super-high thermal gradient directional solidification.” Mater. Trans. 53, 1910 (2012).
  17. Saeidi, K., et al., “Hardened austenite steel with columnar sub-grain structure formed by laser melting.” Mater. Sci. Eng. A 625, 221 (2015). http://dx.doi.org/10.1016/j.msea.2014.12.018
  18. Wang, Y. M., et al., Defect- and heterogeneity-controlled strength and deformation mechanisms in additively manufactured steels. (In press).
  19. Casati, R., et al., “Aging behaviour and mechanical performance of 18-Ni 300 steel processed by selective laser melting.” Metals 6, 218 (2016). http://dx.doi.org/10.3390/met6090218
  20. “Optical engine for material grain microstructure-controlling additive manufacturing technology.” ITRI Today 82 (2015).
  21. Gockel, J., et al., “Integrated melt pool and microstructure control for Ti-6Al-4V thin wall additive manufacturing.” Mater. Sci. Tech. 31(8), 912 (2016).
  22. Khairallah, S. A., et al., “"Simulation of the main physical processes in remote laser penetration with large laser spot size." AIP Adv. 5, 47120 (2015).
  23. King, W., et al., “Overview of modelling and simulation of metal powder bed fusion process at Lawrence Livermore National Laboratory.” Mater. Sci. Tech. Lond. 31(8), 957 (2015).