Automated real-time monitoring of AM

Posted on 05 Jan 2016 and read 2821 times
Automated real-time monitoring of AMAdditive-manufacturing (AM) solution provider EOS (www.eos.info), which has a UK subsidiary in Warwick (EOS Electro Optical Systems Ltd), has launched a new process-monitoring and analysis system.

Called Eostate Melt-pool Monitoring, it is an add-on to the EOS M 290 DMLS (direct metal laser sintering) system and paves the way for complete part traceability, as well as automated surveillance and analysis of the melt pool during the DMLS build process — for every spot, layer and part.

The system expands EOS’s comprehensive portfolio of monitoring solutions for AM by adding high-performance on-line monitoring and thereby ensuring even greater transparency of the complex build process (the technology is targeted at R&D and manufacturing customers with demanding quality requirements).

Reliable quality assurance tools play an important role in boosting trust in this new technology. Eostate Melt-pool Monitoring allows part quality assurance to be moved from post-process to in-process, not only supporting better risk management but also reducing time and costs for quality assurance and — as a consequence — overall cost per part.

The key elements of the system, which observes the light emitted by the melt pool, are a pair of photo-diodes located on-axis and off-axis, a camera adapter, a specialised signal amplifier and spectral filters to separate process light from reflected laser light.

The associated software offers automatic data-error correction and real-time process visualisation and evaluation. For data analysis, the system’s Analysis Toolbox visualises data in 2-D or 3-D mappings and allows the evaluation of indication clusters.

From the collected data, conclusions for the resulting quality in the final part can be drawn. For this, customers define corresponding parameters (MPM Parameters) using the Analysis Toolbox and can set their thresholds according to their particular quality requirements.

Live monitoring during the build process of a real part helps to automatically identify error indications based on these parameters.

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