By Alex Benham Picture this: You’re lying in bed, fast asleep, when your phone rings.…
by Darren Beckett, Sigma Labs
In his July article, Why a Focus on Quality is Needed to Grow the Additive Manufacturing Industry, my colleague Ron Fisher talked about how quality control is one of the inhibitors slowing the potential growth of the 3D metal printing industry, especially in additive 3d metal printing. Ron made some great points in the article, one of which is that the cost-benefit analysis of AM is highly application specific and incorporates an element of uncertainty, compounded by the lack of well-rounded AM knowledge filtering throughout the industry.
I couldn’t agree with the statement more, and it is in fact why Sigma Labs exists as a company: to provide more certainty to the AM process, especially as it pertains to quality control, as well as increase the knowledge base of the industry. This is what our CEO, Mark Ruport, means when he talks about the practice of “radical collaboration.” This is our way of supporting the industry, our customers and our partners.
Whether you are talking about additive 3d metal printing or more traditional forms of metal manufacturing, there are different ways to approach the quality mandate. However, since much of the output of AM is mission critical (e.g. airplane, auto or medical parts), the stakes to get it right are especially high. This is where the post-build vs. in-process question comes into play.
My company comes down solidly on the in-process part of the argument, and in this, we are following in the footsteps of no less an authority than W. Edwards Deming. As reported in A Lean Journal, Deming stated: “Inspecting to pull out the failed items from the production before a customer sees them is a path to failure. When companies do this, they are trying to inspect quality into the product. However, 100% inspection has been shown to be only about 80% to 85% effective. If the process is this bad, the process needs to be improved.”
The article also includes this powerful quote from Deming, taken from his book, Out of Crisis, “Inspection does not improve the quality, nor guarantee quality. Inspection is too late. The quality, good or bad, is already in the product. As Harold F. Dodge said, “You cannot inspect quality into a product.”
Many additive manufacturing process engineers have purchased a number of different AM printers, with a wide variance of control methods. There is a lot going on from machine to machine even within a certain species of machine. That’s why we felt it was really important to offer the industry a de-facto third-part melt pool monitoring standard.
In-line or in-situ inspection is not a new concept. The semiconductor industry has been applying this concept for years, as has Toyota, Tesla and other auto manufacturers. But the challenges are magnified in the world of 3D defect morphology and metrology and pushes us deeply into high speed computing and machine learning for additive printing.
Identifying Quality Problems
To drive up quality standards, end users and printer manufacturers want to see lack of fusion, they want to see keyhole anomalies, they want to see gas porosity, and they are really interested in inclusions. Each of these defect modes tend to represent themselves in different porosity morphologies but they are some of the detection requirements that we have the pleasure of working with while developing our in-process melt pool monitoring.
Keep in mind that many the 3D metal printing machines out there were not originally designed for melt pool monitoring, at least coaxially. The coatings on the particular lenses have to be taken into account, meaning that there is a unique variance that’s introduced as a result of the inherent optics that are within that particular machine.
Another challenge faced by the AM industry when it comes to quality is the high cost and potential inefficiencies of the CT scanning process. For example, dealing with material density. Certain materials are only dense to a certain thickness and beyond this, there is a phenomenon called beam hardening that can create problems. Setting up the templates for the CT process can be quite expensive and increasing volumes can make this technology cost prohibitive.
What can you learn now by adopting in-situ process monitoring technology? Well, you can begin to learn about the signatures, the anomalies, and pathologies associated with your particular build, your machine, and their location. In addition, by forming a solid knowledge of these challenges and beginning to adopt the in-situ practices, you will be able to use this knowledge to improve part quality in real time, while decreasing waste and increasing throughput.
Process Monitoring and Beyond
I shared this diagram at a recent webinar to illustrate how the evolution of our system is lowering the cost while increasing the accuracy of the QA process. The good news is that we are not only following the growth path of the additive manufacturing industry, we are helping it to evolve and scale.
While the list of challenges is substantial, so are the great feelings that my team of engineers and I experience as we set about solving them one by one. As we hear from our customers and partners on a frequent basis, the Sigma Labs system is solving real-world problems and getting better and better. Not a bad reason to get out of bed every morning.