ProjectOASIS

Additive manufacturing processes require a high level of control to guarantee the quality of the parts produced. Despite recent technological advances, process monitoring remains a critical challenge, particularly when it comes to identifying the occurrence of defects in real time.

The OASIS project follows on from the Surveillance project, which demonstrated the potential of monitoring systems for additive manufacturing processes while highlighting several technical and scientific barriers:

• difficulty interpreting sensor data
• lack of real-time decision-making tools for the operator
• no direct link between machine observation and intelligent defect analysis

In this context, OASIS aims to strengthen the operator’s role by placing them at the centre of an augmented monitoring system based on intelligent data analysis and in-process image acquisition.

3-year project, from January 2020 to December 2022

The OASIS project aims to make monitoring of additive manufacturing processes more efficient, more intuitive and more reliable.

Its main objectives are to:

• Develop a defect library based on in-process image acquisition and analysis
• Implement an analytical monitoring system for additive manufacturing processes
• Use data from melt-pool cameras to detect defects
• Develop image analysis algorithms to automate anomaly identification
• Build a control interface based on operator experience
• Enable remote process monitoring with relevant, actionable indicators

The overall objective is to guarantee the quality of manufactured parts while strengthening the operator’s analysis and decision-making capability.

As project coordinator, IREPA LASER is developing an approach focused on intelligent process monitoring and human-machine interaction.

Monitoring through image acquisition
The project uses image capture systems at the melt pool to track the physical phenomena of additive manufacturing in real time.

This data makes it possible to:

• detect defects during manufacturing
• characterise anomalies
• feed a knowledge base on observed defects

Development of an intelligent defect library
A structured database is created to:

• catalogue the defects observed
• link visual signatures to process parameters
• improve understanding of defect formation mechanisms

Development of analysis algorithms
Image analysis methods are developed and optimised to:

• identify defects automatically
• improve detection robustness
• increase the reliability of monitoring systems

Operator interface and augmented monitoring
A dedicated interface is designed to enable the operator to:

• monitor processes remotely
• receive relevant indicators in real time
• draw on their experience to interpret the data

This approach strengthens the human role in the control loop while providing intelligent tools.

Experimental validation
The solutions developed are validated through test campaigns and analysis of their effectiveness under representative conditions.

The OASIS project is run by a small, complementary consortium:

• IREPA LASER – Project coordinator, development of monitoring and analysis tools
• CIRTES – Technological and scientific partner

The project is funded under Région Grand Est and FEDER schemes:

• Total project cost: €371,509
• FEDER (ERDF) funding: €129,038
• Co-funding Région Grand Est + FEDER: ~70% of the budget

The OASIS project delivers tangible benefits for the additive manufacturing industry.

Improved part quality
Early defect detection reduces non-conformities and improves production reliability.

Enhanced process monitoring
The systems developed enable more efficient and more accurate real-time supervision.

More responsive operators
The dedicated interface improves decision-making through clear, actionable indicators.

Lower production costs
Fewer rejects and fewer non-productive trials contribute to better cost control.

Towards augmented additive manufacturing
The project lays the foundations for a new generation of processes in which the operator is assisted by intelligent monitoring systems.