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The Power of Connected Quality

Machine, Process, Part Health

Gain Consistency Across Your Site and Supply Chain with:


A standard framework for in process-quality


Deep instant insight into your machine sensor data


A uniform format, analytics package, and reporting tool


Dashboard visualization view showing key real-time metrics

About PrintRite3D

In heterogenous environments, proprietary quality control approaches of additive machine manufacturers often create inconsistency in quality assurance across manufacturing operations. Fragmented quality strategies lead to cost-control issues, increased scrap rates, and missed deadlines for in-house and contract manufacturers. Industries like aerospace, defense, medical, and automotive have become frustrated by the AM industry’s complicated, expensive, and time-consuming path to achieve efficient part qualification and standardization.

Our team at Sigma Additive Solutions has spent more than a decade creating and refining the technology to provide a framework for standards-based data exchange, metrics, analytics, and a home for standards-based qualification.

The launching of Machine Health and additional software-only modules for Machine and Process Health marks our software only approach to quality assurance by allowing users distinct sensors and images into a cohesive product suite. This act of sensor fusion – conjoining data types –provides improved confidence in defect detection, root cause analysis and mitigation – allowing everyone to interpret control charts and other data types/sources uniformly.

A core pillar to our mission is our are commitment to an open architecture philosophy. in which we fulfill the duty of a 3rd party agnostic option connectable to the broad install base of the additive industry.

Holistic Quality

Machine Health

Machine Health

Machine Log File Data

  • Convert varying machine log files and live-streaming API data to a standards-based format
  • In-app data analytics, monitoring, and dashboarding
  • Build to build and machine to machine data comparison
  • Automated reporting tools for ease of qualification and production
Process Health

Process Health

Camera-based Image Data

  • Camera and Thermal Camera modules for image based defect detection
  • Sigma training sets offer standards-based definitions for a defect detection library
  • Allows for use with 3rd party machine learning and artificial intelligence approaches, as well as Sigma’s sensor fusion approach
  • Correlates to layerwise machine health data with pixel correlation between cameras and melt pool data
Part Health

Part Health

Melt pool-based Part Data

  • Sigma’s proven melt pool monitoring and analytics applied to any OEM data, Sigma retrofit, or integrated hardware (like Novanta Firefly 3D)
  • Provides standards-based comparison tools across varying fleet monitoring
  • Links to Machine Health layer data and Process Health camera-based pixels for data fusion
  • Machine learning and artificial intelligence approaches included for defect detection at a part level (lack of fusion, porosity, etc.)

PrintRite3D Achieves Quality Through Data Fusion

Quality must be built into the product – it cannot be inspected in. Sigma is providing high fidelity tools for process engineers to quickly iterate, to understand, to isolate, and to reduce variance in their manufacturing methods while simultaneously meeting the production floor’s need to economically maintain that process and identify variance outside an acceptable process window.

Our focus is to correlate and collect key in-process data holistically in one location, creating a standard for acquiring and analyzing data, reporting and export format. All with the goal of answering three simple questions:

Is my machine ok?

Is my process ok?

Is my part ok?

Taking this a step beyond PrintRite3D data can be correlated and compared against other industry tools. This provides a path to integration of further pre- and post-process steps, enabling a one-user experience for full digital quality.

Sensor correlation with lack of fusion defects

Sensor Fusion
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