How to approach design in Additive Manufacturing in 4 tips (Part 2)

In our last post we discovered the first two tips to address design in additive manufacturing. Do you want to know what are the following? Read! 

3. Maximize profit using specific techniques

There are different techniques and tools to maximize the design impact for additive manufacturing (topological optimization, lattice structures, generative design, etc.). In my opinion, one of the most important techniques is multifunctional optimization, or also called multiphysics. In some cases it will correspond to topological optimization – or in mass – when what is sought to lighten is the weight. But in other cases, the objective sought may be to optimize the transmission of heat, or improve the flow in ducts according to certain paths or channels, or achieve a certain response in frequency in a reflector or an antenna … In those cases the objective function to be optimized will be another, different from the mass, or it will be a combination of several. The applications are numerous and it is important to have the right tools. 

Another interesting technique is the use of lattice-type lattice structures. They are very useful not only to lighten and modify the density of a part, but even to locally modify the behavior of a component, providing properties to the solid different from those of the material with which it is manufactured. 

An important aspect of additive manufacturing that we must take into account in the design phase is that, in some processes, such as in additive manufacturing in metal or in FDM processing of certain high melting temperature plastics, the thermal gradients that are generated and the heating and cooling processes can produce important distortions in the parts or introduce strong residual stresses that influence their final behavior or emerge. such as microcracks or other defects. In these cases, it is essential to integrate the simulation of the manufacturing process into the design phase to predict these effects and adapt the design of the “part + supports” assembly to ensure that they behave as expected and reduce the risk of defectology appearing in the final part. The simulation also serves to establish the process parameters that provide an optimal compromise solution in quality and productivity. The alternative to this process is trial-and-error learning, and that is not feasible for high value-added processes or high-cost raw materials. 

We can benefit from flexibility in design for additive manufacturing to improve the final performance of our parts. At this point there are multiple examples and extensive literature, although one of my favorite applications has to do with the combined optimization of several functionalities. For example, in redesigning a hydraulic system block to be manufactured by additive manufacturing, internal channels can be designed to optimize fluid flow in a way that improves efficiency and requires lower power pumps. In addition, the weight and volume of the assembly can also be substantially reduced, and this weight reduction allows the use of a material of somewhat higher cost, but with greater resistance to corrosion, so the durability, reliability and maintenance of the assembly is favored. Another interesting example is the design of molds for injection or for the curing of composite parts. Internal cooling channels can be integrated with the appropriate geometries to optimize heat flow, but in turn the internal structure or support structure of the mold can be designed to compensate for its expansion or contraction so that it does not affect the geometry of the part. This is a very economical and interesting alternative to the current solution of machined metal molds.