Additive Build Failure Cost Buffer
Every additive build carries scrap risk from recoater crashes, support collapse, or power events. Combine build time, fully loaded machine rate, material costs, and your historical failure probability to quantify the expected loss per build and the contingency reserve required to cover failed runs.
Expected loss is a statistical average; always review individual builds, qualification plans, and customer commitments when setting reserves.
Examples
- Example 1 — 36 hours, $145.00/hour machine rate, $820.00 material, 12% failure probability ⇒ Base build cost at risk: $6,020.00 (machine $5,220.00 + material $800.00) | Failure probability: 12.00% | Expected loss per build: $722.40 | Suggested contingency reserve: $722.40 | Budget including reserve: $6,742.40
 - Example 2 — 18 hours, $210.00/hour, $1,450.00 material, 6% failure probability ⇒ Base build cost at risk: $5,230.00 (machine $3,780.00 + material $1,450.00) | Failure probability: 6.00% | Expected loss per build: $313.80 | Suggested contingency reserve: $313.80 | Budget including reserve: $5,543.80
 
FAQ
How do I estimate failure probability for new geometries?
Start with historical scrap on similar materials, orientations, and support strategies. If unknown, run pilot builds and update the percentage once you have real data.
Can I include rework hours after a failed build?
Yes. Add requalification, cleaning, and reprint prep hours into the build duration or material field so exposure reflects the full reset cost.
What if I insure or warranty builds?
Subtract insured amounts from the expected loss before setting the contingency reserve, or treat premiums as part of the machine rate.
Does this work for metal and polymer printers alike?
The formula is agnostic—adjust the material and machine rates to match your process (LPBF, DED, SLA, MJF, etc.).
Additional Information
- Machine cost per hour should include labor and overhead so exposure reflects the full impact of a failed run.
 - Expected loss equals build exposure multiplied by the failure probability—use rolling scrap data for accuracy.
 - Contingency reserve covers a single run; scale it by the number of builds in a production batch to size program budgets.
 - Lower the failure probability after implementing QA, in-situ monitoring, or parameter tweaks to show savings from reliability initiatives.