Additive manufacturing (AM), also popularly known as 3D printing, is a manufacturing technology that has been evolving since the late 1980s—but can finally be said to be proving its worth in many fields. It is a layer-by-layer process by which material, metal, plastic, alloy, or a combination is fused or bonded to produce the desired part.
Recently, the technique has been more widely popularized as advancements in the technology have brought 3D printing to the desktop and the home through the Maker movement. Significantly, the scope of AM is now expanding beyond rapid prototyping into industrial applications of both tool-product and direct-part production.
Hardly a day goes by when you don’t see an article in the news about a novel use of the technology, whether it is to print a prosthetic arm, design your favorite chocolate toppings, manufacture a bridge on-site, or print an entire car itself. However, significant challenges still exist in the reliability and predictability of a number of AM processes that act as barriers to part certification and a much more widespread adoption in industry.
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So, the main question to ask ourselves in our community is, “How can simulation help get reliability back into AM designs? Can we design parts so they print right the first time?” Let us explore. There are a number of key areas where simulation can play an important role in 3D printing: generating a functional design, generating lattice structures, calibrating the material, optimizing the manufacturing process, and in-service performance.
AM is unique in offering designers freedom from traditional manufacturing constraints, allowing them to take their designs to new heights to meet engineering requirements without sacrificing part strength or performance. Lightweighting is an example of this: parts can be created with a minimum of material necessary to meet specified functional requirements. Creating these kinds of designs is now feasible thanks to the proven technology of robust, nonlinear topology optimization that SIMULIA offers through the Tosca suite of products.
AM also provides the ability to create parts with extremely sophisticated internal lattice structures that would not be possible through traditional manufacturing techniques. Such lattices allow for additional weight reduction beyond that provided solely through topology optimization. Additional SIMULIA capabilities, to be released later this fall through Tosca, will enable users to introduce lattices into their structures as well as size those lattices to create fully functional parts.
A critical aspect in any AM process is to be able to characterize the underlying material that is being used. Typically, with metal alloys for example, a high-intensity laser is applied to a powder bed along a CAD-software-guided path, fusing the metal layer-by-layer to build the part. The metal melts locally and, as the heat-source moves on, solidifies with the previous layer to create the fused part. The phase transformations, the cooling rates, and other machine-specific parameters such as print speed guide the metallurgy and the micro-structures that develop.
Such parts can be stronger than those made with traditional manufacturing methods such as casting but the variabilities in mechanical properties can be significant. Hence there is a need to capture the multi-scale and multi-physics nature of the manufacturing process. Here is where the Abaqus user-subroutine framework is already enabling researchers and industry to model the physics of the micro-mechanics behavior while leveraging Abaqus as the global solver for the macro-behavior of the parts.
Aside from material characterization, the 3D-printing manufacturing process itself can introduce significant gaps between the as-designed and the as-manufactured part. In the as-designed part the design is without stresses or distortions and assigned with standard material definitions. However, during AM, which is generally a thermal process today, residual stress build-ups, part distortions, and material variations can arise.
Here is where Isight can provide us with a powerful tool to study the effects of manufacturing process parameters such as the deposition path, build orientation, and heat intensity. The tool can then be applied to optimize residual stresses, reduce part distortions, and alter material behavior to meet the in-service conditions of the part—whether those are static loads, dynamic loads, vibrations, or any of the other engineering issues that you are already solving using Abaqus.
Ultimately, what is sought for components operating under in-service loading conditions is the fatigue life of the part. fe-safe®’s deep integration with Abaqus will enable fatigue life evaluation for additively manufactured components as the material data is developed in research.
So to answer our initial questions, yes, simulation has great potential to improve the quality and therefore support the growth of additive manufacturing in every industry, with a breadth of SIMULIA tools that can address many of the issues currently arising as the technology approaches maturity.
What to learn more about Dassault Systèmes’ simulation solutions for additive manufacturing? Visit: go.3ds.com/Print2Perform
