Someone in the automotive industry recently said to me “the enthusiasm for adopting additive technology for automotive production is inversely proportional to a fundamental understanding of physics and material science.” This assessment could be considered a trifle harsh but like many sayings, or clichés, there is an element of truth. With that disclaimer, let’s take a look at additive technology in the context of automotive sector applications and arrive at a common understanding of the differences between rapid prototyping and additive manufacturing.
Additive manufacturing is not new to the automotive industry. In 1992, within GM, we had networked every one of our facilities which had rapid prototyping systems, from Harrison Radiator to Saginaw Grey Iron, to maximize machine utilization. We established an internal community of “super-users” and hosted a “GM Only” conference with hundreds of attendees, and had machine co-development initiatives with equipment suppliers, such as DTM, Stratasys and 3D Systems.
It might be accurate to say that the automotive industry was a critical business sector that provided the impetus in the ’90’s to make additive the “approach of choice” for producing many prototypes for product evaluation. The sheer volume of the automotive industry drove this to become mainstay and ubiquitous for this purpose.
The question remains: Why, having achieved the status of early adopter and in some cases early developer, did the momentum for this technology grind to a halt within automotive companies? The reason is that there were, and are, insurmountable roadblocks that have prevented expansion of usage beyond prototype applications.
These “showstoppers” are addressed here with a simple evaluation-driven strategy that we call CT-SAM. CT stands for Cost and Throughput. SAM represents Size, Accuracy and Material characteristics. We peel the SAM part of the onion first.
Size (S) as a roadblock is a relatively easy concept to address, the part either fits in the build envelope, or it doesn’t. For a prototype, parts can be scaled or assembled, but for production this rather defeats the purpose and since there are a lot of parts in a vehicle larger than the build envelope of most additive machines, size remains a fundamental challenge.
Accuracy (A) is used as an umbrella term to describe the product geometry/feature-related challenges that exist for additive manufacturing, such as surface roughness. Moreover, the term refers to the fact that parts are near net shape, which generally means there remains a need for post-processing and machining. This is not insurmountable but it is an investment in cost and part quality, which, although palatable in a prototype environment, is less acceptable for volume production.
Material characteristics (M) is a concept that is more complex than just material type. To an artist, or a sculptor perhaps, steel is steel but to those of us working in industries where part performance is critical there is a fundamental understanding that material characteristics vary greatly depending on the process by which they are created. Even if we just consider castings, a sand cast prototype cannot be used for product validation of an intended die cast production component since the solidification rate that defines the microstructure and resultant mechanical properties are vastly different.
Now enter additive manufacturing where the part may look like a casting but is fabricated by creating layer upon layer of multiple welds. This process introduces interfaces and chemistry dilutions so the microstructure/properties (essential for the prediction of performance) are completely different and as yet unpredictable. For metal parts to be used in production for structural applications, we would need to model, predict and control both the material characteristics during build and the part performance.
GE, for example, didn’t just get a commercial machine and print a metal part for an engine. It worked for years to create processes and standards in collaboration with the FAA to fully understand the material characteristics of these new parts and achieve part certification with the governing bodies responsible for ensuring flight safety—and the automotive industry will face a similar challenge. Thus, the aspect of material characteristics in solving M of these technical and financial equations in SAM-CT is not trivial.
Finally, CT is the cost and throughput of an AM process compared with a conventional process. Cost is the Achilles heel of any new technology that forays into the automotive space. In this industry, a penny a part savings is a major accomplishment—not to mention the capital expense of displacing the vast installed infrastructure and associated logistics. Cost is closely coupled with throughput. If it takes six weeks and $10000 to build a prototype but additive can make the part in two days at a cost of $1000, the decision is obvious: Use additive. We have used it for decades, saved money and streamlined product-development discussions.
When we move to production, the challenge for CT-SAM is immense. For example, we can fit an engine block into a metal powder machine (so S is OK) and we can machine the finished product (so A is ok) and, for arguments sake, let us assume we have a full understanding of the material characteristics (so perhaps M is fine). We have solved SAM (we can make it). Now we move to CT and the question becomes should we make it? The additive machines would build a block for about $25000 but the production costs with casting are about two orders of magnitude lower, so although we could build the part it would make zero economic sense.
This sounds depressing but we are missing one angle. If we limit ourselves to part substitution, then additive looks pretty unattractive for automotive production but maybe only if we build “the same old parts.”
One of the keys to determining whether this technology is a game changer will be the skill with which one is able to totally redesign a part or, better yet, an assembly of parts.
The current process of part substitution merely creates a physical entity of identical design at lower throughput and higher cost.
There has been great excitement with respect to the strides GE is making in using metal additive technology for its fuel injector. What is often missing in the story is the fact that GE’s breakthrough was the fact that it took a design made of about 17 parts, each of which had to be manufactured and then assembled. Thus, we can assume that the newly morphed product represented a considerable cost avoidance and presumably was in the same ballpark as the cumulative cost of all the pieces of the old assembly.
Without question, major breakthroughs will be needed in the material, machines and post-processing for the technology to venture into the automotive industry. A significant >10X reduction in final cost is required for additive part making to begin to move into production levels (even for volumes in the low thousands) to be truly considered “automotive manufacturing.”
The other end of the fulcrum will be a totally different mindset in part design that will be needed to exploit the opportunities AM provides in terms of component mass efficiency, which is not possible with current manufacturing that depends on essentially “orthogonal” tooling.
So perhaps the challenge from the automotive community to the additive system experts is twofold: Solve SAM with hardware and material innovation and develop design tools that help us reinvent parts and morph assemblies. Then perhaps we could make an engine 10 times lighter that only an additive process can make. And if we solve SAM and the related design challenges, the cost and throughput may solve themselves.