With this piece, I will highlight some of the key misconceptions repeated ad nauseum around additive manufacturing (AM). This developed from conversations with industry insiders. My intent is not to ruffle feathers, but to point out pitfalls which might be avoided for the sake of the health of the industry.
Pitfall 1: Beware of 3D Printed Panaceas
Nothing can replace any/every manufacturing process in use today. Every method has its merits when properly scoped for the application at hand, and each method has its limitations and drawbacks, 3D printing is no exception. Several processing steps are required to improve any part in manufacture. The idea that you can have a 3D printer producing fully finished, functional, production grade components without additional work is a noble goal but far from current realities.
If the Starship Enterprise suddenly showed up and dropped off a functioning, replicator making anything from a cup of tea to an airplane engine - we would not use the products of such a device without several years (decades?) and tens or hundreds of millions of dollars in testing. Maybe.
Re-read that, and let me say that in another way: if a 3D printed panacea of perfect production appeared, we wouldn’t trust it without some serious scrutiny.
Avoiding the Pitfall: Might we stop asking for perfect 3D printer, or suggesting that we are on the verge of producing one?
Pitfall 2: AM ⊆ M
AM is a subset of manufacturing, full stop. Emphasizing adoption of AM to replace or disrupt any or all current production methods is naive and misplaced at best, downright ignorant or obstinate at worst.
Framing AM in opposition to the broader suite of manufacturing tools is doomed to failure as this vision cannot presently deliver. If it could deliver this ‘disruptively’ we would not automatically trust it (see Pitfall 1 above)!
Where AM can deliver a competitive solution, it will be accepted. When unsuitable, you’re trying to bang a square peg into a round hole - while charging a premium.
Best to avoid this pitfall by not threatening to undermine the rest of manufacturing; AM is here to enhance manufacturing, not to ‘disrupt’ it.
Pitfall 3: Solutions in Search of Problems
Having problems to solve is often unavoidable, but being a solution in search of a problem is a trainwreck in the making.
Despite being a “disruptive new way to make something”, many AM methods are often not well scoped at the start. Once hitting the mainstream, the law of the instrument takes effect and these processes are offered as solutions, whether suitable or not. AM startups must have someone scouting for clients and needs in the market for which their printed parts may provide a competitive solution.
This issue of being a ‘solution in search of a problem’ is even more painful for software-based AM startups. Many of these organizations do not have a clear understanding of market needs and their value propositions are inadequate as a result.
One answer to this pitfall is to avoid expecting too much from new AM processes / software until core applications and relevant partners are identified, or until software functionality and utility are demonstrated to be relevant to the industry at hand.
Pitfall 4: Barriers to Adoption
What really are the barriers to adoption of AM? The desire to ‘accelerate adoption of AM’ methods has led to much public hand-wringing without a clear consensus. Let’s look at some of the “usual suspects”:
-Is the equipment holding us back? Machine improvements (faster, bigger, multimaterial, color, better surface roughness, etc.) are always desirable. They are also likely to set the clock back on qualification and certification. Perhaps blaming the machines is not the way forward, even as improvements will open new doors.
-Is our talent pool too small, or mis-deployed? Not a novel problem as it occurs in every technical field. This certainly has some role to play in AM, but cannot be the overriding factor.
-What about ‘limited creativity’? Exceptional creativity is great to have, but will not allow you to skip validation and testing requirements. Expense must be considered.
Has anyone noticed that machine performance (speed, surface quality, etc.) and staffing limitations are easy targets to blame while institutional, structural, and organizational factors are often overlooked or avoided?
What about our propensity as an industry to avoid sharing data publically? This is often cited as a limiting factor in private conversations, but rarely addressed.