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EU funded (Grant no. 101092176)

DIAMOND
EU Horizon WASABI

WASABI is an intelligent digital assistance solution for the manufacturing sector, based on human-artificial intelligence collaboration. Through Natural Language Processing (NLP) technologies, it supports workforce upskilling and knowledge transfer, helping European SMEs and mid-caps achieve sustainability, innovation, and competitiveness goals in an accessible and sustainable way.

LOG XY will develop the Digital Intelligent Assistant (DIA) and ensure its integration and deployment within the WASABI White Label Shop (WWLS).

www.wasabiproject.eu

Digital Intelligent Assistant (DIA)

for Additive Manufacturing

DIA is an intelligent digital assistant that revolutionizes human-machine interaction in metal 3D printing. Directly integrated with EOS machines for LPBF (Laser Powder Bed Fusion) technology, it allows operators to interact with the production system through voice commands and receive real-time audio notifications.

🎤 The operator speaks to the machine

Thanks to integration with OVOS (Open Voice Operating System) and RASA, the operator can query the EOS machine simply by speaking:

  • "What is the current print status?"
  • "How far along are we with the current job?"
  • "Are there any anomalies detected so far?"
  • "What is the meltpool temperature?"
  • "Show me the current process parameters"

The system responds vocally in natural language, reducing the need to consult screens and complex interfaces.

🔊 The machine speaks to the operator

DIA continuously monitors the process and proactively communicates through voice alerts when it detects:

  • Meltpool anomalies (localized overheating)
  • Deviations from optimal parameters
  • Conditions that have historically led to defects
  • Need for operator intervention
  • Completion of critical process phases
Example:

"Warning: overheating detected in the central area of layer 847. High risk of crack formation. Parameter verification recommended."

🔍 Early identification

The predictive AI system analyzes real-time data from EOS machine sensors:

  • Meltpool analysis: monitors variations in pixel grayscale values
  • Pattern recognition: identifies typical "signatures" of critical conditions
  • Historical correlation: compares data with previous prints

🛡️ Defect prevention

The AI is trained to recognize conditions preceding the formation of:

  • Thermal stress cracks — abnormal temperature gradients
  • Porosity — meltpool instability
  • Layer delaminations — adhesion problems
  • Geometric deformations — residual stress accumulation

🧠 Continuous learning

Thanks to federated learning, the system:

  • Learns from every print executed
  • Progressively improves prediction accuracy
  • Adapts to specific materials and geometries
  • Maintains data privacy (no raw data leaves the facility)

Integrated Technologies

Component Function
LLM (Llama) Natural language understanding
OVOS Voice recognition and synthesis
RASA Conversational flow management
Predictive AI Sensor analysis and defect prediction
Federated Learning Privacy-preserving learning

Measurable Benefits

Indicator Improvement
Print failure reduction -20%
Defect identification accuracy +30%
Data interpretation time -30%
Operator intervention From reactive to proactive
GDPR compliant EU AI Act compliant EU Trustworthy AI guidelines End-to-end encryption Complete audit logging
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