DMG MORI and Sigma Labs Improving Additive Manufacturing Quality Case Study
Companies that use AM have little room for error in the quality of their final product, even as they are pressured to innovate more and deliver faster. This case study explores how two industry leaders, Sigma Labs and DMG MORI, have teamed up to enhance quality control in the AM process.
Assessment of the Variability of the Laser Powder Bed Process through In-Process Inspection
Additive Manufacturing by Laser Powder Bed is well-established in several sectors as a manufacturing method of metallic components. However, it has some limitations due to some quality and repeatability concerns for critical applications with high fatigue and damage tolerance requirements. The complexity of the process and its multi-physical nature make it difficult to anticipate the presence of defects and leads to some unexpected defects. Therefore, Oerlikon AM GmbH is assessing in-process monitoring solutions in order to analyze in real-time the process, identify the sources of variability and for the tuning of the Key Process Variables.
Thermal Calibration of Commercial Melt Pool Monitoring Sensors on a Laser Powder Bed Fusion System
Sigma Labs recently collaborated with NIST to outline how to calibrate PrintRite3D® sensors to a traceable temperature standard. As a result, Sigma has performed a first-ever standards calibration with the renowned National Institute of Standards and Technology. This now opens the door to machine-to-machine calibration and enables machine matching for the Additive industry. Sigma Labs is achieving the milestones necessary for certification and industry-wide acceptance and has become a lead author in developing the melt pool monitoring standard within the ASTM international framework which is expected to go to ballot in Q4 2020. This enables a pathway to adoption by additional international standard bodies such as ISO and JSA in 2021.
PrintRite3D® Machine Learning Models and IPQM® Prediction Identifies Common Anomalies
Sigma’s Labs’ PrintRite3D® software, using machine learning, can accurately predict the size and position of anomalies previously identified by CT scanning. Prediction metrics like these can make it possible in the future to close the loop on controlling the build process. When production managers can predict anomalies, they can decide whether to interrupt or adjust the build process or even redesign the part. This will ultimately save time, money and materials, enhancing the manufacturer’s bottom line.
Sigma Labs - ANSYS: Combination of Modeling and Thermal Sensing to Understand Additive Manufacturing Processes
For this study, Sigma Labs teamed with ANSYS to compare data collected by Sigma Labs’ coaxial thermal sensor response with ANSYS modeling as methods of monitoring additive manufacturing quality. Thermal effects of scan strategy in critical locations were predicted by ANSYS modeling; these thermal effects were experimentally validated using PrintRite3D® in-situ thermal process monitoring data. Validated thermal models in combination with thermal measurement can offer valuable insight to part design and manufacturing. Disagreement between modeling and monitoring can highlight important changes in the process that affect part quality.
PrintRite3D® Alerts for Anomaly Detection
Sigma Labs’ PrintRite3D® software provides additive manufacturers a vital tool to allow them to better understand production issues when they arise immediately and in real time. This allows rapid and detailed investigation and adjustment by process engineers, thus saving time, money and minimizing material waste.
Data Registration and Machine Learning for Anomaly Detection
Sigma’s PrintRite3D® software, using machine learning, can accurately predict the size and position of anomalies previously identified by CT scanning, as well as others not previously identified. This means PrintRite3D can be used as a new, near-real time quality measurement to supplement other measurements, like CT scanning.
Support Structure Optimization Using PrintRite3D®
Additive manufacturing companies can use less metal powder, reduce machine time and material waste, and remove the part more easily when they use Sigma Labs’ PrintRite3D® software to optimize the design of support structures upon which parts are built. They can minimize material waste, save time and money using PrintRite3D®.
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
Sigma Labs’ proprietary Thermal Energy Planck (TEP™) metric identifies variances in the production of 3D metal parts in real time. Thus, using TEP™ allows for intervention and adjustment of the manufacturing process in real time, making the process more efficient and less costly. This in turn saves time, money and can ensure product quality in metal additive manufacturing.
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® 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.
The Relationship Between Melt Pool Monitoring Metrics and Archimedes Density
Metal additive manufacturing today leaves skilled practitioners with little to no insight into the process. Nor does it provide in-process feedback about part quality. Until now. Sigma has demonstrated that its quality metric TED™ exhibits a strong correlation to both global energy density and Archimedes’ density, both established and trusted measures of part quality in the metal additive manufacturing industry.
In-Situ Process Mapping using Thermal Quality Signatures™ during Additive Manufacturing with Titanium Alloy Ti-6Al-4V
Companies that want to use 3D manufacturing face quality and reliability challenges, as well as cost factors in certifying their parts. Sigma’s proprietary software, as demonstrated in this case study, addresses these challenges. Further, the software can be used to eliminate the traditional trial and error approach to process mapping and validation, shortening the time and reducing the cost associated with today’s “make-and-break” approach to certifying AM part quality.
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.
Machine Learning: A Game Changer for Additive Manufacturing Quality Assurance.
Read this informative 3D Metal Printing Magazine article from our CTO, Darren Beckett. Machine learning in any environment, including metal additive manufacturing (AM), depends on gathering quality data and then using the right system to validate that data. Like most other processes, the old expression “garbage in, garbage out” applies.