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Economics of Early Intervention in AM Quality

by Alex Benham

As an engineer, when I talk about PrintRite3D® from Sigma Labs, I tend to dive deep into the technical bits of what we do and how we do it. I’ll get into the intricacies of Bichromatic Planck Thermometry, laser optics, emission spectra, and metal AM defects. When you work on such a high technical level it can be hard to take a step back and analyze business-focused topics like economics. It is important though because, in the end, economics is what drives decisions. While the technical topics and products are exceptionally interesting, organizations don’t buy products because of their intrinsic value. They buy because it will save time and money… the realized value.

In additive manufacturing (AM), there are three main cost drivers: materials, process, and scrap. Labor can be considered as a fourth component, but I’ll ignore this for now. Production grade materials at this point are effectively commodities. It’s a race to the bottom by the suppliers so there’s not much to do there. The AM process has consistently gotten faster and better as the technology progressed, but not much has happened with the scrap rate.

Scrap rate is a measure of all materials/resources used in production compared to that of the bad (rejected) parts. Parts are scrapped because of any number of defects produced during the production process. Exterior surface defects (such as dimensional tolerance) are easy to identify and measure. Internal defects on the other hand are hard to distinguish and usually require expensive inspection methods like XCT scanning. The AM process is more susceptible to inner defects than subtractive machining which makes this a critical area to study. But how do we study it in an effective and cost-efficient way? This is where in-situ quality management comes in.

Figure 1: Average part cost breakdown in the aerospace bracket case study according to the as-is scenario.

Luckily enough, a research paper on this subject “A Cost Model for the Economic Evaluation of In-Situ Monitoring Tools in Metal Additive Manufacturing” by B.M. Colosimo, S. Cavalli, and M. Grasso has already studied this. A quick look at the chart above reveals that in the SLM Process (the build process) is expected to consume approximately 82% of the component cost (in this specific case an aerospace bracket). And of course, as the scrap rate increases, so does the cost. Reducing these two factors – SLM and scrap – through the use of an in-situ quality solution like PrintRite3D® substantially improves the business case economics. Monitoring allows for early intervention (cancelation of a build when a defect is identified), meaning less production occurs thus saving SLM process costs. To reduce the scrap rate, we need diagnostic tools to determine the root cause of defects in the SLM process.

Figure 2: Aerospace bracket case study: comparison of the part cost curve in the as-is scenario (dashed line) and the monitoring scenario (solid thick line) with 95% Bonferroni’s confidence intervals (dotted line) as a function of γ for different in-situ monitoring performances; the vertical red dotted line corresponds to the upper intersection between the confidence envelopes.

Continuing to investigate the aerospace bracket cost, we can compare the impact achieved by the addition of a monitoring solution. The charts above examine two extreme α and β * conditions – reality will be somewhere in between. To evaluate them we must identify a reasonable scrap ratio. In my experience in AM Production 0.30 (30%) scrap is reasonable. Expanding out from the scrap ratio to part cost, the simple addition of a monitoring solution can save $70-130 (6.7-12.4% savings in this example) per piece. These savings are the result of early intervention. Catching errors as they occur and no longer deploying capital resources to the non-compliant components; essentially cutting your losses. Of course, there are many factors involved here so please evaluate using your specific data and scenario.

Figure 3: Minimum scrap fraction for convenient use of in-situ monitoring for aerospace bracket case study; left panel shows the result for the monitoring scenario and the right panel shows the result for the monitoring & diagnosis scenario.

The addition of diagnosis capabilities allow for even further savings in terms of minimum scrap rate reductions. As mentioned, I used a scrap rate of 0.30 for AM Production in my past. With the charts above we see how the addition of monitoring plus diagnosis can drop that scrap rate drastically. Using 0.025α and 0.15β * values (the middle of the previous chart extremes) with monitoring alone, we can expect a minimum scrap of 0.08. Already a substantial improvement over 0.30. Adding diagnosis capabilities returns a minimum scrap of 0.016. This of course is a minimum, reality is a bit different – for aerospace applications, conservatively an overall reduction of 10% is to be expected. Again, use your own values to evaluate for your specific scenario.

Figure 4: Effect of Early Intervention on the SLM process cost via PrintRite3D®

AM has always been great at realizing amazingly complex designs, but it hasn’t always been so great about producing superior quality or economies of scale. However, after reviewing the data it is clear: for high value applications, simply installing a monitoring solution immediately returns financial savings. This is why automating quality checks earlier and during the process is so important – to allow for early intervention.

To learn more about how individual manufacturers and the entire AM industry benefits, read the informative article by Sigma Labs CEO, Mark Ruport about why the combination of radical collaboration and quality standards is so important.

Sigma Labs representatives would like to understand more about your needs and look forward to answering your questions about in-situ quality management, removing bottlenecks, reducing costs, and improving value in the industrial supply chain. Feel free to email us at  You can also learn more about in-process quality assurance (IPQA) at our Resources Center.

Note: Technical images in this article are from:

Colosimo, B.M., Cavalli, S., Grasso, M. (2020); “A Cost Model for the Economic Evaluation of In-Situ Monitoring Tools in Metal Additive Manufacturing”; International Journal of Production Economics 223; pp. 1-18



* α (alpha) and β (beta) are measures of reliability of a quality system. They are measured as a rate of false negative and false positive. For simplicity’s sake α is the release of a defective product – consider it as potential return of product, loss of reputation, a lawsuit. β is the opposite, the scrapping of a compliant product – consider it as lost revenue opportunity.

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