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DARPA Study Validates PrintRite3D® Quality Control Process for Certification of Metal Parts

A six-year project with DARPA that relied on Sigma’s PrintRite3D® technology has drawn three key conclusions. First, PrintRite3D® demonstrates and ensures process consistency and product quality in metal additive manufacturing. Second, this technology can also monitor additive manufacturing equipment health. Finally, PrintRite3D® can be used to certify quality and certify components without destructive testing or CT scanning – saving time, money and materials.


The relationship between In-Process Quality Metrics & Computational Tomography (CT) in Additive Manufacturing of Metal Parts

What if you could see and analyze the structure of a 3D-printed part while it was being made? You could then have high confidence in the manufacturing process. You’d also be able to adjust the process in real time to further assures quality. Sigma Labs has developed a method to do just that, with results comparable and complementary to CT testing, as this case study demonstrates.


In-situ Melt Pool "Thermal Signature" Defect Detection of Recoater Failure Using Co-Axial Planck Thermometry

The drive to certify and qualify additively manufactured metal parts is heralding new methods of quality assurance that will ultimately allow AM end users to take the much needed “leap of faith” that is required to foster confidence in AM. Sigma labs has developed such a methodology that mines and digitalizes “thermal signatures” of the melt pool disturbances and respective discontinuities using emission spectroscopy. The evolution of thermal digital signature advances the digital thread that is much needed by certification and standard authorities.


Evaluation of Quality Signatures™ using In-Situ Process Control during Additive Manufacturing with Aluminum Alloy AlSi10Mg Part 2

This document reports on the second in a series of experiments to demonstrate the capabilities of Sigma Labs, Inc. (Sigma) PrintRite3D INSPECT® software to determine the effect of intentional changes in an independent process input variable (laser power) on dependent or response data mined in-situ on a layer to layer and part to part basis.


Process Mapping for Metal Additive Manufacturing Using In-Process Quality Metrics - A Gateway to Closed Loop Melt Pool and Process Control

This study reports on quantitative, in-process quality metric™ (IPQM®) data based on interrogation of “attributes of the process”, i.e., the melt pool, not “attributes of the part.” These IPQM®s are inferred from in-process dynamical behaviors of the melt pool at a scan, layer and part level. Effects on the melt pool energy balance are first considered and understood before mining sensor trace data from the melt pool for representative in-process thermal-history metric data. This metric data is then used to generate real-time 2D trend plots of melt pool behavior as a function of process input variables (laser power and laser scan speed). An alloy-specific process map is generated for a titanium-base alloy using variations in laser power, laser scan speed, quantitative post-inspection data (density), and the associated, independently measured in-process quality metric™ data from the melt pool.


In-Situ Process Mapping using Thermal Quality Signatures™ during Additive Manufacturing with Titanium Alloy Ti-6Al-4V

The current study was designed with three experimental purposes in mind: 1) establish a material specific process map using in-situ Thermal Quality Signatures™ and ex-situ physical property data; 2) draw a one-to-one correlation between in-situ dependent data quality metrics and ex-situ dependent data property metrics; and 3) set the stage for closing the loop by affording the opportunity to self-correct the process in real time.


Evaluation of Quality Signatures™ using In-Situ Process Control during Additive Manufacturing with Aluminum Alloy AlSi10Mg - Part 1

This build was designed to establish a correlation between in-process dependent data mined from in-situ sensor raw traces signals, independent process input variables for example laser power, and post-process dependent data measured during destructive metallographic testing for porosity of as-built specimens.


Sigma Labs welcomes your questions as well as opportunities for demonstration and collaboration. Please contact our technical team for more information.

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