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    Additive Manufacturing Blog

    Hands on quality -

    setting standards for process and material development

You’ve probably heard that one of the problems that you’ll face when switching to additive manufacturing is the lack of standards for quality assurance. While this is not necessarily true, the freedom that additive manufacturing offers has met the challenge that there isn’t yet a fully standardized set of requirements in additive manufactured parts.

Kevin Minet
Team Supervisor Metal Materials & Processes
Electro Optical Systems Finland Oy

Why is there a lack of standards in additive manufacturing?

The nature of additive manufacturing is one of fast progress and constant evolution. Not that long ago 3D printing was something that wasn’t seen outside of research and development. What was once almost exclusively used for prototyping, however, has now become a serious contender for product manufacturing.

In the past decade, the use of additive manufacturing has boomed globally. From biomedical research to space exploration: additive manufactured parts are at the forefront of scientific and electro-engineering ingenuity. However, this fast growth also means that standards of quality as outlined either by manufacturers or regulating bodies have had a hard time keeping up with all the new opportunities that additive manufacturing can bring. Designed around traditional and legacy methods of production, most testing isn’t fit for practice in the fast-moving world of additive manufacturing. 

Where once a one size fits all approach worked for quality assurance of products, now product variety and complexity, combined with the speed of turnaround that additive manufacturing can offer, means that there needs to be a more flexible method of ensuring quality without losing the benefits of additive manufacturing.

If Additive Manufacturing is constantly evolving, and the parts growing ever more unique, how can we create testing methods to accommodate for this?

Let’s look at the example of the aerospace industry. They are held to the highest standards of rigorous testing and quality assurance. Each part has to be precisely engineered to enable the finished result to be one that is fully optimized. Previous machining techniques are falling behind additive manufacturing as they are severely limited in what they can produce. The use of additive manufacturing can mean innovative parts that are no longer restricted by space or material; but how can we test these new parts when the standards of quality don’t exist? When traditional methods are no longer fit for practice, we need to turn to technological options instead.

What should I consider when I’m thinking about standards and quality assurance for my product?

In our whitepaper Hands-on quality, authors ​ Kristiina Kupi, Research & Development Engineer, and Kevin Minet, Research & Development Project Manager, explore the technology available on the market today, and how incorporating the EOS Quality Triangle into your own standards in quality assurance can help you to form a greater idea of standards of quality for your own production.

In summary…

  • The speed and flexibility of the additive manufacturing industry can be very beneficial to your business
  • Traditional testing methods and standards are no longer fit for practice due to this freedom and product variety
  • Using the EOS Quality Triangle outlined in this white paper you can assess what your own standards of quality should look like

Want to read more about the quality assurance tech options available to you, as well as what you should consider for your own quality standards?

You can download the complete white paper here:


Optimizing the Post-Processing area:

After optimizing the AM and powder management area, the post-processing area was the next challenging task. It involved seven different process stations with ten steps: 

  1. Part cleaning in DyeMansion* Powershot C
  2. Ultrasonic cleaning
  3. Surface finishing
  4. Ultrasonic cleaning
  5. Drying
  6. Surface treatment in DyeMansion* Powershot S
  7. UV treatment
  8. Coloring in DyeMansion DM60
  9. Surface treatment in DyeMansion* Powershot S
  10. UV treatment

(*DyeMansion is part of the EOS Ecosystem)

Additionally, there were a few challenges regarding the customer workflow. For example, the blasting stations (Powershot S and C) from our partner DyeMansion should only process half of the parts produced in one build (72 out of 144 parts), in other words, two lots per build. However, for all other stations such as the DyeMansion DM60 coloring system, surface finishing, drying, and so on, all jobs need to be processed, in other words, one lot per build.

Once the original model was extended to include the post-processing area, we focused on finding the optimum number of operators. Starting with the AM optimum of 1062 jobs per year, we ran five different setups with 1-5 operators. As seen in Table 3, 4 operators would be needed for the post-processing area to have considerably higher throughput than the previous number of workers. 4 additional jobs do not justify the additional cost (not shown here) of an additional operator (848 vs. 849 jobs).

  Number of operators Jobs built through AM Jobs produced through AM + Post processing
Exp1 1 1062 228
Exp2 2 1062 666
Exp3 3 1062 787
Exp4 4 1062 848
Exp5 5 1062 849

Table 3: No of operators for Post-Processing area and respective jobs

Analysis of the base Model:

Interestingly, the base model with the AM and Powder Management area and one operator produces 1065 jobs, but with the inclusion of the post-processing area we could only achieve 848 jobs – even with the increased post processing worker number up to 4! Having a 5th worker will also not significantly increase the number of jobs through the full production line. Hence, there must be a bottleneck somewhere in the post-processing area.

The simulation model easily helped identify the bottleneck in the surface finishing area. Do you see the high pile of undone jobs (or work in process) in Figure 1?

215 jobs or 30.900 parts still need to be processed in the surface treatment area. 

Optimization iteration 1:

One solution to remove the bottleneck at the surface grinding equipment could be to add another surface grinding machine while keeping the same number of workers and shifts. After doing the necessary modification to the simulation model, we realized that adding another surface finishing machine did not completely remove production line bottlenecks but rather moved it to the DyeMansion* Powershot S. Luckily the size of the bottleneck reduced to 57 jobs or 8.226 parts. Figure 2 shows the new bottleneck and throughput.

The impact of this optimization is as follows:

  • Utilization of machines is expected to be 46%, which, for a 5-day single shift for a 20 hour build after 9% planned & unplanned downtime and 10% part rejection, is very good
  • 15% decrease of CPP
  • There is an increase of 17% points in ROI due to this single change

Optimization iteration 2: 

For the second iteration we focused on the new bottleneck identified in iteration 1. We added one more DyeMansion PowerShot S and this solved the bottleneck. We couldn’t detect a shift like the one from the 1st iteration to the 2nd.

In Figure 3 you can see the result. Nearly all the initially manufactured 1062 jobs completed the manufacturing process in time with only a minor capacity loss of 0,01% due to the last job in the year being processed.

The impact of this optimization is as follows:

  • Additional 2% utilization of machines or total of 48%
  • Additional 3% or total 18% decrease of CPP
  • Additional 5% points increase in ROI due to this single change to a total of 22%

Adding the machines to have the highest throughput is only a partially optimized solution for serial production. The other important factors are the cost of adding machines and operators to determine if they have a positive impact on cost per part. 


The key message here is that scaling and optimizing your AM production strategy can be a complicated task. It is a unique challenge, tailored to a specific layout with different key parameters for each scenario. Not analyzing and taking your strategy into account can lead to incorrect conclusions. With our simulation expertise and 30 years of AM experience we can make a winning scaling strategy for you to achieve the highest machine utilization and lowest cost per part.


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