EOS Smart Monitoring

End-to-End Process Monitoring and Quality Assurance for Industrial Metal Additive Manufacturing

EOS Smart Monitoring

In-Situ Process Insight and Closed-Loop Control for Industrial AM

EOS Smart Monitoring is the EOS software solution for in-situ process monitoring and quality assurance in industrial metal additive manufacturing.

It enables manufacturers to gain full visibility into the melt process, ensure stable series production, and significantly reduce scrap, build failures, and downstream quality assurance effort.

Built on EOSTATE Exposure OT and enhanced by Smart Fusion, EOS Smart Monitoring combines real-time process monitoring with automated process correction - enabling data-driven, closed-loop additive manufacturing.

EOS Smart Monitoring requires an active EOS Build Plan.

What Is EOS Smart Monitoring?

EOS Smart Monitoring is a subscription-based solution designed for industrial metal AM environments where process stability, reproducibility, and qualification are critical.

It provides continuous, layer-by-layer insight into the build process using optical tomography and enables manufacturers to monitor, analyze, and actively control the process during production.

By combining in-situ monitoring, automated exposure control, expert training, and consulting, EOS Smart Monitoring supports the transition from prototype builds to stable, industrial series production.

Solving Real Production Challenges

EOS Smart Monitoring helps manufacturers address key challenges in industrial metal additive manufacturing:

Increasing process stability and reproducibility
Reducing scrap rates and build failures
Minimizing costly downstream quality assurance (e.g. X-ray, CT scanning)
Supporting qualification and documentation in regulated industries
Accelerating product development and time to market

By enabling data-driven decisions during and after the build, EOS Smart Monitoring creates trust in AM processes and unlocks new, economically viable applications.

Key Capabilities of EOS Smart Monitoring

EOS Smart Monitoring provides a robust set of capabilities for in-situ process monitoring and quality assurance in metal additive manufacturing.

Real-time, layer-by-layer process monitoring using optical tomography
100% visibility into energy input and melt behavior during the build
Detection and analysis of process deviations
Data support for qualification, documentation, and traceability
Closed-loop process control through automated laser power adjustment
Seamless integration with the EOS software ecosystem, including EOS Build

The following core capabilities form the foundation of EOS Smart Monitoring and are explained in more detail below.

Process Monitoring and Closed-Loop Control

Core capabilities of EOS Smart Monitoring

EOSTATE Exposure OT – 100% Process Insight

EOSTATE Exposure OT is a tightly integrated hardware and software solution that uses optical tomography to monitor the additive manufacturing process in real time.

A high-resolution near-infrared camera records and measures the energy input during the build - independent of part size. The camera’s long exposure time enables detailed analysis of individual layers and melt behavior.

Based on project-specific, adjustable analysis algorithms, EOSTATE Exposure OT provides a powerful toolbox to identify process flaws and deviations throughout the build.

Key benefits include:

Reduced need for destructive testing
Lower cost compared to non-destructive inspection methods such as X-ray or CT scanning
Significantly reduced scrap rates and build failures
Valuable insights for future production cycles
Faster product development and parameter optimization

Smart Fusion – Automated Process Correction During the Build

Smart Fusion introduces a new level of control in metal additive manufacturing.

Using live data from EOSTATE Exposure OT, Smart Fusion automatically adjusts laser power during the build to ensure thermal conditions remain within the limits defined during pre-processing. By actively compensating for thermal deviations, Smart Fusion:

Reduces overheating and process instabilities
Minimizes part iterations
Improves consistency and reproducibility
Enables support-reduced and support-free part designs

Smart Fusion transforms process monitoring from passive observation into active, automated process control - a key enabler for closed-loop additive manufacturing.

Training and Enablement

EOS Smart Monitoring includes dedicated training and enablement offerings to ensure successful adoption of optical tomography and Smart Fusion in industrial production environments. These offerings support both new users and experienced AM professionals in building and expanding their process expertise.

Each EOS Smart Monitoring subscription per system includes:

Up to two e-learning seats for basic and advanced OT training
Advanced e-learning modules for Smart Fusion
One four-hour expert session on OT technology
One Smart Fusion Expert Session

Subscription Model for EOS Smart Monitoring

EOS Smart Monitoring is offered as a yearly subscription per EOS metal AM system, with a minimum initial commitment of two years.

Instead of a high upfront investment, customers gain access to:

Required EOSTATE Exposure OT hardware
Monitoring software and analytics
Smart Fusion functionality
Training and expert enablement

If Smart Fusion is not required after the initial commitment, it can be deactivated and reactivated later as business needs evolve.

Inactive subscriptions retain access to the OT hardware and limited functionality (e.g. historical data), while real-time monitoring, live analytics, training resources, and Smart Fusion remain unavailable.

EOS Software Plans - Metal (English)

PDF 854,06 kB

Download eos_plans_features_comparison.pdf
EOS Plans Customer Guide - Polymer and Metal (English)

PDF 385,40 kB

Download customer-guide_software-and-process-plans.pdf

SUCCESS STORY

voestalpine

Shorter Time to Market for AM Tooling Inserts

By integrating EOS Smart Monitoring into its production process, voestalpine gained deeper insight into the LPBF process and significantly reduced the number of required job iterations.

The result: