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How Beam Shaping and Simulation Software Work Together to Optimize LPBF Printing 

Allyce Jackman, Senior CFD Engineer, Flow Science and Rob Martinsen, Chief Technology Officer, nLIGHT Inc.

The melt pool is a magical place. Peer closely enough into this miniature world and you’ll find a realm of unexpected wonders. A laser produces tiny volcanic plumes of vapor and molten metal from the bed below. A shimmering, superheated globule of liquefied titanium or heat-resistant superalloy follows the beam, painting a slightly elevated track through the sand-like surface. Globules of molten feedstock float upward like ejecta from the universe’s tiniest asteroid strike, then splash onto the surface, freezing instantly.

Despite all this enchantment, however, specialists in laser powder bed fusion (L-PBF) and other laser-based metal printing technologies are well aware—as are those who procure the parts it produces—that these phenomena often lead to defects during the additive manufacturing (AM) process.

Such defects include porosity from keyhole instabilities and lack-of-fusion, thermal stresses that can distort the workpiece, resolidified spatter that cause re-coater crashes, inconsistent powder morphology and particle size distributions, which can all result in failed builds. Because of this, it is crucial to gain a robust understanding of the mysterious place we call the melt pool, followed by steps to control the factors that contribute to these seemingly random events.

Keyhole porosity defects compared to ring beams operating at either the same power
Figure 1: Side view X-ray image time-series showing the propensity for Gaussian beams to generate keyhole porosity defects (top row) compared to ring beams operating at either the same power (middle row) or more than 25% higher power (bottom row). Image courtesy of Prof. L. Chen, University of Wisconsin in cooperation with Argonne National Lab.

Under the Surface

Thermal cameras and other types of in-situ monitoring technology provide excellent insights into metal AM processes, but their range is limited, and attributing parameter sensitivity to specific physical phenomena is outside of their scope. Using predictive modeling through computational fluid dynamics (CFD) software, it is possible to gain a fundamental understanding of stable melting processes and the conditions that result in defects during printing.

With CFD, researchers, equipment builders, OEMs and contract manufacturers can obtain a comprehensive understanding of the melt pool as well as the environmental conditions within the build chamber, providing technical insight to help optimize build settings, path planning strategies, laser parameters, and even the equipment itself. One example of this comes from laser manufacturer nLIGHT, a customer and collaborator of CFD software developer Flow Science, who used the advanced simulation capabilities in FLOW-3D AM as well as a code developed by the University of Munich to optimize their beam profiles in unexpected ways. More on this shortly.

One area where simulation plays an increasingly relevant role—a topic of special interest to anyone involved with metal AM—is mastery of the ever-expanding library of alloys that end-users and equipment providers must contend with. Each new alloy requires the development of unique laser parameters (power, scan speed, modulation, spot size, and intensity profile) that are highly contextualized to the local geometry being printed – cantilevers, contours, skin-up vs. skin-down surfaces, bulk material melting, fine lattice, support structure, and so on.

Laser powder bed simulation
Figure 2: Melt pool simulations in FLOW-3D AM with different beam shapes plotting temperature

The common industry approach of applying manual, trial-and-error methods to quantify these parameters requires an enormous amount of tedious work and expense. It’s also error-prone, leaving manufacturers with questions about part integrity that no amount of CT scanning or other non-destructive testing will resolve. Now imagine multiplying these efforts by the need to test multiple materials and part geometries to determine the best parameters for a given application is a formidable amount of work by any measure. This is a routine practice today among mechanical engineers, designers and process engineers that consume enormous amounts of cost and schedule to geta metal AM part into production, creating significant bottlenecks in innovation and delaying time-to-market for critical components.

CFD simulation simplifies these efforts while offering valuable insights and actionable information about each parameter’s contribution to print quality. It allows manufacturers to quickly and cost-effectively identify the optimal process window for a particular material, machine, and laser parameter set. Furthermore, CFD simulations make it possible to avoid unstable process conditions where critical defects arise, such as keyhole porosity, providing unprecedented transparency to what is normally a closed manufacturing process.

The result? Manufacturers have greater confidence that the first part will be a good part, and that subsequent print jobs will yield positive outcomes. Better yet, build cycles not only become more predictable, but also more productive, with improved workpiece quality and fewer qualification runs. When printing titanium, nickel-based superalloys, refractory metals, and other costly materials, reducing the number of non-yielding trial parts becomes quite compelling.

One Ring Beam to Rule Them All

nLIGHT released their AFX laser in 2020, knowing that it had the potential to revolutionize metal printing by enabling huge gains in melt pool control. But they wanted more than anecdotes to communicate to their customers – they wanted to share the thermal physics that govern the value of laser beam shaping. Just before the release, the nLIGHT team worked with FLOW-3D AM to simulate the AFX laser and find out exactly why multicore beams were producing such optimal results. What they found was quite remarkable.

Through simulation, they compared the spatial distribution of intensity (same total power), from Gaussian (left) to a ring profile (right), and saw profound changes in evaporation pressure and surface tension pressure across the melt pool. These physical properties correlate to the relative size and quantity of particle spatter ejected from the melted region under the laser. Connecting the dots between what they observed in experiments and what they predicted with models they closed important knowledge gaps of the underlying physics, and ultimately led to better lasers and significantly more productive printing processes.

Melt pool simulations and AFX ring profiles
Figure 3: Melt pool simulations in FLOW-3D AM comparing Gaussian distribution (left) and AFX ring profile (right) a),b) velocity vectors, c),d) surface tension pressure distribution, e),f) recoil pressure distribution.

As it turns out, much of the spatter, soot and melt pool instability is due to the classic Gaussian laser beam profile associated with all single mode lasers used for powder bed fusion machines. While Gaussian beams produce the smallest possible spot size on the powder bed, they become problematic during those parts of a build when productivity is prioritized over fine scale resolution. Since Gaussian beam profiles have a centrally placed peak intensity, the concentrated heating they produce can be highly disruptive. By redistributing the peak intensity to the perimeter rather than the center, nLIGHT has largely eliminated the extreme temperature gradient and overheating that occurs with these profiles.

High-speed videography during powder bed fusion melting process
Figure 4: Side views from high-speed videography during powder bed fusion melting process showing the higher levels of soot, spatter, and evaporated condensate with the Gaussian intensity profile of a standard single mode laser (top) compared to a ring intensity distribution (bottom) from the nLIGHT AFX laser. Both images are associated with the same alloy (AlSi10Mg) and laser parameters (600W in a 400um focused spot size, scanned at 1.1m/s). Images courtesy of Michael Rasch, Friedrich Alexander University Erlangen- Nürnberg.

The Need for Speed

And yet, there’s much more to this picture than reduced soot and spatter in the build chamber and the production of high-quality parts. A key consideration for a manufacturing business is cycle time and its associated impact on part cost, and here again, shaped laser beams do not disappoint—as users of the technology can attest, what’s perhaps most intriguing about beam shaping technology is the leverage it has to unlock productivity. For as long as metal AM has existed, users have asked for greater speed. Equipment manufacturers have responded by adding more lasers (and scanners) that must all work in harmony to print parts as quickly as possible. Unfortunately, the presence of so many lasers adds a great deal of complexity to machine and scanner control the build processor and, of course, equipment investment and operating costs. It’s for this reason that metal 3D printer providers have begun replacing multiple standard Gaussian beam lasers with fewer beam-shaping lasers in their large format machines. This approach serves two functions. The first is significant simplification of the build paths, the machine, and the overall process. Secondly, part costs go down with faster print speeds . What once took days to produce now takes hours, along with an improvement in part quality. By creating a new class of laser, one that can rapidly switch between a small Gaussian beam and 6 larger ring profiles, nLIGHT essentially unlocked the conflicting trade for powder bed fusion between precision and productivity – both are now enabled by the same machine.
Factory calibrated beam shapes
Figure 5: Factory-calibrated beam shapes from the nLIGHT AFX laser. Gaussian beam on the far left for contour exposures and printing fine scale geometry; six ring profiles have a progressive amount of power coming from the ring waveguide which leads to an effective grey-scale in spot size up to 3x the diameter of the Gaussian beam.
While beam shaping enables machines to print parts more efficiently, the parameter space has increased with more variables, and as with nLIGHT’s AFX laser, there are now at least 6 more beam modes to factor into optimization. Finding process windows that leverage optimal beam shapes takes even more time than using a traditional Gaussian, which is why simulation is playing an increasing role in helping engineers reduce development timelines.

Shaping the Future

AM solution provider EOS has taken notice. The leading equipment manufacturer signed an agreement with nLIGHT in June 2024 to equip some 3D printer models with the company’s AFX programmable beam-shaping lasers. According to a press release, the two will also “implement a series of complementary laser-based technologies to enhance the light engines of EOS 3D printers for industrial-scale production applications,” and “customers will be able to access different beam profiles on EOS software to achieve improved 3D printing productivity.” A more recent example comes from AMCM, part of the EOS Group, which in late 2024 installed an M290-1 Custom 3D printer equipped with an nLIGHT AFX laser at the Center for Design and Manufacturing Excellence (CDME) in Columbus, Ohio, reportedly the first of its kind in the United States. Beam shaping technology also has applications beyond metal AM. The battery market is using nLIGHT lasers for high-speed welding of various components. So is the sheet metal industry, where adjustable beam characteristics are paving the way toward higher throughput, better edge quality and reduced costs. Looking forward, advanced simulation software like FLOW-3D AM will continue to open new doors for every aspect of metal AM technology. An increasing number of end users will leverage it to optimize their products and throughput. Equipment manufacturers will continue to unravel AM’s secrets, developing increasingly efficient lasers and 3D printers. And OEMs will use CFD software to design processes that lead to stronger parts with fewer defects in less time with – you guessed it – less overall cost. Beyond these immediate uses, there’s also the future potential for integrating simulation tools and machine learning into a closed-loop environment. The compute load for real-time simulation is currently far too high for production use, but there exists the possibility of in-situ adjustments to laser shape and build parameters, further increasing AM efficiency and part quality. It’s an exciting time, one that will only grow more dynamic as the industry embraces these highly advanced technologies. Stay tuned.

This article originally appeared on SME Media’s 2025 Additive Manufacturing Industry Report.

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