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FIELD INTELLIGENCE Smart Processes, Solutions & Strategies


Susan M. Smyth


Chief Scientist for Global Manufacturing General Motors


www.GM.com/index


What will it take for the metal AM show to go on in automotive?


S


omeone in the automotive in- dustry recently said to me “the enthusiasm for adopting additive


technology for automotive production is inversely proportional to a funda- mental 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 dis- claimer, let’s take a look at additive technology in the context of automo- tive sector applications and arrive at a common understanding of the differ- ences 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 ini- tiatives with equipment suppliers, such as DTM, Stratasys and 3D Systems. It might be accurate to say that


the automotive industry was a criti- cal business sector that provided the impetus in the '90’s to make additive the “approach of choice” for produc- ing 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


6


grind to a halt within automotive com- panies? The reason is that there were, and are, insurmountable roadblocks that have prevented expansion of us- age beyond prototype applications. These “showstoppers” are ad-


dressed 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.


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 understand- ing 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


One of the keys to determining whether this technology is a game changer in automotive will be the skill with which one is able to totally redesign a part or, better yet, an assembly of parts.


Size (S) as a roadblock is a rela-


tively 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 um-


brella 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 invest- ment in cost and part quality, which, although palatable in a prototype environment, is less acceptable for volume production.


product validation of an intended die cast production component since the solidification rate that defines the mi- crostructure and resultant mechani- cal properties are vastly different. Now enter additive manufacturing


where the part may look like a cast- ing but is fabricated by creating layer upon layer of multiple welds. This pro- cess introduces interfaces and chem- istry dilutions so the microstructure/ properties (essential for the prediction of performance) are completely dif- ferent 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 dur- ing 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


March 2017


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