As additive manufacturing (AM) racks up more and more success stories, scrutiny on the process is becoming increasingly forensic in nature, with a focus on the areas that the technology must improve to allow it to compete on more equal terms with conventional manufacturing techniques.

Alexander Pluke, CEO, Additive Flow

As this decade progresses, the drive towards the use of AM as a replacement for conventional manufacturing technologies for key applications is set to continue, and will primarily be focussed on new and existing applications where the use of AM brings a significant ROI across the value chain by reducing the overall time and cost of manufacturing. As a general rule of thumb, for AM to be used as an efficient production technology for any given application it needs to demonstrate — as a minimum — a 10-fold improvement in cost and/or time savings to offset the cost and risk associated with converting from a traditional manufacturing process chain.

There are applications in the public domain that demonstrate this sort of ROI and value, There are suggestions that there are more applications in R&D yet to be revealed publicly and there are infinitely more that have not been thought of yet. Because as good as AM is as a solution within its niche (and it is still a niche within the global context of manufacturing) there are still barriers to adoption that are stalling uptake and growth of AM.

A key barrier to adoption is the disconnect within the necessary workflow for AM. Just consider for a moment, the digital tools required for the physical output from an AM process. AM is widely considered a digital manufacturing tool, rightly so. It’s also often cited as an essential component of Industry 4.0. Aside from popular buzzwords, what are we actually trying to achieve here? This is the crux of the conversation, in my opinion. Any given AM system is just one component within a complex digital workflow that involves the creation (design), optimization, manipulation and simulation of data. Subsequent to these disciplines, the data must then be converted into a machine-readable form by way of the “slicers or build processors” before achieving the physical goal — the required part(s). Even at the first stage, the creation of the design data, there are many options of traditional parametric design software solutions, as well as new generative design options that facilitate form and functionality optimization. Similarly, for design optimization and digital part simulation, there are many other options. the data required for a successful AM part will pass through many different stages on its journey. This journey is currently complex and requires serious know-how to navigate successfully through the various, often discrete, digital tools.

It is important to clarify a juxtaposition on complexity here.

AM enables complexity specifically in terms of part geometry and the physical building of complex shapes that are not possible with traditional manufacturing methods. Indeed, this is a positive USP of AM compared with traditional manufacturing methods. However, the digital workflow required to facilitate AM applications throws up levels of complexity that can prohibit adoption, and this is a real challenge and it requires a new approach.

The “good” complexity has led to advanced and enhanced levels of creativity and innovation for AM applications, where unprecedented levels of design freedom have resulted in new design methodologies for geometrically complex products. As mentioned there is now a plethora of examples of generative design software modules that can produce lattice structures and topologically optimized structures for optimal performance and light-weighting benefits.

It is within this narrow scope that the general discussion around optimization for AM applications often lies. I believe it must be extended to realize the latent potential of AM.

A broader view of optimization

It is fair to say today that when the AM community talks about optimization, they are generally referring to design optimization. Topology optimization, for example, seeks to achieve the best possible design for a structure by changing material distribution within the design space to achieve required functionality and maximize performance. Similarly, the proliferation in the (generative or otherwise) design of lattice structures, the production of which is singularly enabled by AM, has the potential to deliver applications with new functionality including high levels of energy absorption and stiffness, while at the same time offering light-weighting advantages and reduced material consumption. These are both positive benefits of new design thinking that are driving new and/or improved applications of AM. It goes without saying that optimization remains imperative as a digital design discipline, but what I am saying is that it needs to be moved on, with a more holistic approach and more connected digital tools that can apply a multi-parameter approach simultaneously.

In this way, a more holistic approach requires intelligent and multi-functional digital tools that can optimize the design geometry, material placement and AM process parameters in a single environment — in essence allowing designers and engineers to optimize their entire digital workflow and remove the unwanted complexities from it. This capability will unlock the next level of performance from AM and industrial 3D printing technologies. The rationale behind this is the foundation of Additive Flow and the development of our modular and multi-functional software designed to streamline the digital process chain, remove complexity, and add real value to AM applications.

A multifunctional approach

Adopting a multifunctional and holistic approach requires a flexible set of digital tools that can simultaneously consider material properties, geometry and material placement, hardware systems, and build parameters. Such an approach increases multi-property performance and optimizes the outcomes for AM applications, applicable across material systems (metal/polymer/ceramic) and combinations for compatible multi-material applications, within the boundaries of material science where defined materials and material properties can be combined through the manufacturing process. One solution uses a multi-physics, multi-property topology optimization approach.

Why?

To address common challenges experienced within the Design for Additive Manufacturing (DfAM) workflow and pre-production preparation, another option—FormFlow—is a broader type of optimization software that incorporates multiple functions enabled by specific physics-driven comparisons of the design, material, AM process parameters, and production output against key performance indicators (KPIs) as defined by specific application priorities.

These capabilities offer time- and cost-efficiencies to the entire digital workflow and go beyond the typical capabilities of discrete generative design, topology optimization, and design simulation software tools while incorporating all of these capabilities. FormFlow is AM-process agnostic with specialist solvers that account for the unique properties in AM materials and can be tailored to work with single- and/or multi-material designs.

Overcoming issues inherent in AM

FormFlow addresses some inherent characteristics within AM processes that can be easy to overlook. For example, AM processes in general exhibit variations in physical properties along the build axis (anisotropy), where the 3D printed part behaves differently depending on the direction of force applied to it. This usually means that 3D printed parts behave differently to forces applied perpendicular to the build direction.

If these are ignored, or the standard isotropic solvers on the market are used, optimization results will invariably be wrong, and in such a way key safety factors may be missed and/or true optimization opportunities lost. This anisotropy plays an even greater role within composite structures, where fiber orientation delivers enhanced engineering properties in particular directions. Algorithms ensure that the opportunities for anisotropic optimization can be realized for every application.

AM also allows for the adjustment of parameters throughout the printing process, or the application of different parameters to different regions of a part. This has the potential to unlock productivity, cost, and performance improvements when compared with traditional manufacturing processes.

However, the complexity of manually deciding which regions should have which property sets can be time-consuming and complex. FormFlow addresses this issue through multi-property optimization algorithms, the software having the capability to address different parameter sets within an optimization while enabling engineers to apply their parameter knowledge seamlessly across a split mesh.

Additive Flow
www.additiveflow.com

Newly launched, Additive Flow, London, U.K. is leading the way in the creation of digital solutions with the overall aim of accelerating innovation through the use of AM. The company exists to solve digital workflow issues that currently prevent users — and potential users — of AM from unlocking the potential of manufacturing technologies during the pre-production stages of the process chain. The company was co-founded by Alexander Pluke (CEO) and Charles Fried (CTO), who are leading a growing team that work at the intersection of architectural computation, Artificial Intelligence (AI), machine learning, and material science.