AdditiveLab – Unleashing the Power of Additive Manufacturing Simulation

 Additive manufacturing comes with a number of challenges. Some of these are related to thermal effects during the manufacturing process, which include the management of cooling rates to ensure homogeneity of material properties. Simulation with AdditiveLab can help optimize pauses within layers (dwell times) or beam path design, to ensure homogeneous cooling rates and the manufacture of high-quality designs. 

The importance of homogeneous cooling rates. 

During the powder bed additive manufacturing (AM) process, each individual layer is exposed to heating and cooling cycles over a long period of time. While a layer is being made, it is exposed to the heat of the laser. After exposure to the laser, the layer cools a little until new dust is deposited for the next layer. When the next layer is exposed to heat, the previous layer undergoes a thermal cycle again when exposed to the elevated temperatures of the next layer. 

Proceso de fabricación de la primera y segunda capa
Manufacturing process of the first layer (left) that is exposed to laser heating; it is then cooled and partially exposed to heat from the manufacture of the second layer (right).

Heat dissipation from the top layer through the manufactured structure becomes more difficult if the prepared manufacturing configuration limits the heat flow. For example, if the cross-sectional area of the configuration increases with the manufacturing height; a typical scenario, in the worst case, would be to manufacture an upside-down cone. 

estructura cónica fabricada al revés
Conical structure manufactured upside down, which limits the heat dissipation on the motherboard.

Why is this so important? 

In the case of metallic materials, different crystalline structures are formed depending on the cooling rate. Different crystal structures give rise to different general properties of the material and, for example, define whether a material is more ductile or more fragile, and allows for less or greater elongation. In high-level engineering applications, controlled solidification (cooling) is used to create materials specifically tailored to certain applications. For example, in the case of certain metal materials, fast cooling rates allow for increased hardness. In addition, the more the manufacturer controls the thermal process and cooling rates, the better it can manipulate the crystalline structures to its liking and ensure homogeneous and flawless material properties in the manufactured design. 

This is especially important in the case of geometries subjected to dynamic loads, such as engine valves, which must be manufactured flawlessly to ensure their durability. Check out the following typical valve geometry: 

Example of cyclic-symmetrical valve geometry with variable cross-sections along valve height (right) and simulated mean temperatures throughout the manufacturing process indicating thermal flow bottlenecks that limit heat dissipation.

A simulation of the valve with AdditiveLab (check out the image above, right) revealed the problem that arose with the previous simple conical structure; thinner cross-sections beneath larger cross-sections limit heat flow through the valve and cause inhomogeneous average temperatures as well as different cooling rates. Different cooling rates will lead locally to different material properties, essentially to a valve that will have different material properties in the thinner sections than in the thicker sections. 

How to ensure the homogeneity of material properties during the manufacturing process? 

código de ejemplo de un script de Python
Sample code for a Python script to optimize dwell times during the manufacturing process

This is where AdditiveLabRESEARCH software comes in handy. With AdditiveLab’s thermal simulation module and Python API, the user can define all kinds of optimization problems, including optimizing dwell times to ensure homogeneous cooling rates in the manufactured design throughout the construction process. In other words, AdditiveLab can be used to determine which layers of longer or shorter pauses are necessary to ensure process continuity without allowing unwanted temperature buildup. 

To demonstrate this in the case of the valve, we have created a Python script that automatically adjusts the intralayer pauses to ensure homogeneous cooling rates and avoid temperature accumulations. The main sections of this script include the preparation and execution of subsequent thermal simulations and the definition of an error function that determines the difference in cooling rates throughout the valve design

temperaturas medias simuladas a lo largo del proceso de fabricación
Simulated average temperatures throughout the manufacturing process, indicating thermal flow bottlenecks that limit heat dissipation with the original process (left) and an improved situation with a more homogeneous distribution of average temperatures in the optimized process (right).

Once the optimization was completed, the comparison of the average temperatures calculated throughout the construction process revealed a more homogeneous distribution of the average temperatures in the optimized process (right) compared to the default process with constant dwell times between each layer (left). 

tiempos de permanencia para la estrategia de fabricación
Dwell times for the original manufacturing strategy (left) and the optimized dwell time strategy (left), with the longest dwell times indicated in red.

With this optimization strategy using AdditiveLab, manufacturers can improve the outcome of their manufacturing process and ensure the generation of suitable parts for high-performance applications that need a high level of material quality. 

Here are some examples of how at AdditiveLab we use simulation optimizations to improve the manufacturing process. 

Want to know more about AdditiveLab’s process simulation software? Take a look here

If you prefer, you can contact us, we will be happy to help you!