BreitbahnDED
BreitbahnDED investigates wider LMD tracks and their industrial validation.
- investigate wider and more uniform DED/LMD tracks
- evaluate productivity together with the process window
- plan validation before industrial use
Research & projects
Research for industrial metal additive manufacturing.
Exafuse discusses publicly funded projects, collaborative research and development themes here without publishing confidential project data.
Public collaborative project
BreitbahnDED investigates wider LMD tracks and their industrial validation.
The work focuses on rotating multispot optics, wider deposition tracks, process stability and the validation needed before industrial use.
Public collaborative project
EIS-KW investigates additive tooling concepts and heat management.
The project considers additive manufacturing across process boundaries: SLM/LPBF for compact functional geometry, LMD for local material addition, and combined process chains with finishing and inspection.
Additive tooling route
EIS-KW investigates additive tooling concepts and heat management.
- investigate SLM/LPBF for compact functional geometry
- consider LMD where local material addition is useful
- evaluate manufacturing, finishing and inspection together
Research work
Collaborative projects and process data answer different engineering questions.
Collaborative projects examine new manufacturing approaches within a defined scope. Monitoring and data work deepen process understanding; both still require application-specific validation.
Public collaborative projects
BreitbahnDED and EIS-KW address specific questions in deposition width, tooling concepts, heat management and industrial validation.
Monitoring and process data
Image and sensor data are used to investigate process behaviour and support inspection planning. Part acceptance remains a separate decision.
Understanding maturity
Research evidence moves toward an application in distinct stages.
The stages distinguish research direction, demonstrator evidence and part-specific qualification. They are not certification or acceptance grades.
-
01
Research question
Define the objective, measured variable, material or process question and unresolved assumptions.
-
02
Representative trial
Use a coupon or demonstrator to examine the approach under defined conditions.
-
03
Published project context
Approved project results show what was observed within a specific trial or application context.
-
04
Part-specific qualification
Define material, geometry, process, finishing, inspection and acceptance criteria for the actual application.
If the material, geometry, process route or acceptance criteria change, transferability must be reviewed again.
What research makes visible
Monitoring and data work explain process behaviour, but do not replace part release.
Melt-pool and image data can reveal anomalies and make trials easier to compare. Physical inspection and agreed acceptance criteria remain necessary for release.
Monitoring and AI
Monitoring and data work explain process behaviour, but do not replace part release.
- use melt-pool and image data for process understanding
- prioritize anomalies without automatic release claims
- interpret outputs against metallography, surface or dimensional checks
Exafuse role
Exafuse connects process development, measured data and application-specific validation.
Research results support engineering assessment. Suitability for a specific component is still decided from the part data and inspection requirements.
Process understanding
Monitoring and image data help investigate LMD, SLM and combined process chains; they do not replace part acceptance.
Validation
Measured data are interpreted alongside physical inspection, metallography or dimensional checks.
Application context
Transfer to a component still depends on material, geometry, functional surfaces and acceptance criteria.
Research themes
Additive manufacturing is treated as a process, material and validation question.
The work ranges from wider deposition tracks and additive tooling concepts to process signals, AI-assisted analysis and physical inspection.
01 / Monitoring
Melt pool and process signals
Sensor and image data help Exafuse understand LMD processes more clearly. They do not replace part release.
02 / AI AI as decision supportModels can prioritize signals and review triggers, but they still need inspection, metallography and engineering review.
03 / SLM / LPBF Additive tooling conceptsEIS-KW belongs to additive manufacturing as a whole: SLM / LPBF, LMD, cooling concepts, material route and validation.
04 / DED-LB/M Wider deposition tracksBreitbahnDED examines more productive DED/LMD tracks for larger areas, repair and coating.
05 / Validation Inspection and boundariesResearch stays credible when each monitoring or process claim says what it can show and what it cannot prove.
Research articles
Technical articles from current research topics.
These pages explain monitoring, AI-assisted control and neural-network image processing without exposing confidential model data, process parameters or customer results.
Article
Melt-Pool Monitoring in Laser Metal Deposition: What Process Images Can and Cannot Prove
Melt-pool monitoring in LMD is useful when it helps engineers see process behavior, compare signals with physical evidence and decide what needs closer review. It cannot by itself establish final part quality.
QualificationMetal AM routeDfAM and OEM
Article
AI in Laser Metal Deposition Process Control: From Decision Support to Closed-Loop Claims
AI in Laser Metal Deposition is strongest as decision support, process-image interpretation and structured technical review. It becomes risky when model outputs are treated as autonomous quality release or closed-loop control...
QualificationMetal AM routeDfAM and OEM
Article
Computer Vision for Melt-Pool Monitoring in LMD: Dataset Drift, False Positives and Practical Limits
Computer vision can improve melt-pool monitoring in LMD, but only when teams control dataset drift, false positives, false negatives and the gap between image patterns and physical part quality.
QualificationMetal AM routeDfAM and OEM
Article
How to Validate AI Outputs Against Physical Inspection in Laser Metal Deposition
AI outputs in Laser Metal Deposition become credible only when they are tested against physical inspection. The validation route has to connect model outputs to dimensional checks, microscopy, metallography and documented...
QualificationMetal AM routeRFQ and buying
Article
Thermal and Coaxial Melt-Pool Monitoring for LMD
Exafuse uses thermal and coaxial melt-pool monitoring to make LMD process behavior measurable during development, while keeping inspection and metallography in the validation loop.
QualificationMetal AM routeDfAM and OEM
Article
Powder-Stream and Nozzle Diagnostics for Laser Metal Deposition
Powder-stream diagnostics help Exafuse see whether an LMD nozzle is delivering a focused, symmetric and repeatable powder cone before a full deposition trial is run.
Metal AM routeQualificationDfAM and OEM
Article
Line Scanning and Robot Path Preparation for LMD Cladding
Exafuse uses scanner-supported geometry capture to connect measured surfaces with LMD cladding and contour-following robot path preparation.
Metal AM routeWear and corrosionQualification
Article
Reduced-Order Modeling for LMD Process Decisions
Reduced-order process models help Exafuse screen candidate LMD conditions and plan more useful experiments without replacing physical validation.
Metal AM routeQualificationDfAM and OEM
Article
Standoff and Height Sensing for LMD Process Stability
Standoff and height sensing help Exafuse treat process-head distance as a measurable LMD signal instead of a hidden setup risk.
QualificationMetal AM routeDfAM and OEM
Recommended next steps
Pick the next page by what you need to decide.
Continue with a relevant service, case study, technical article, FAQ or feasibility request.
01
Connect research with a process question
Use public research pages to understand the technical direction, then define what still needs component-specific trials and validation.
ArticleResearch Spotlight: BreitBahnDED and Why Wider Beads Matter for Industrial ProductivityPublic infrastructure projectDuisburg Bridge Components: More Than 750 kg Produced with LMDArticleLarge Structural LMD for Bridge Components: Lessons from the Duisburg Bridge ProjectPageTechnology and equipmentServiceQuality
Metal AMStart manufacturing RFQ2 more related items are available through the linked hub pages.
02
Discuss a controlled feasibility study
Provide the component objective, material, available reference data and the decision that the study should support.
ArticleFunctionally Graded Materials with DED/LMD: When a Gradient Outperforms a Single AlloyPublic infrastructure projectDuisburg Bridge Components: More Than 750 kg Produced with LMDArticleLarge Structural LMD for Bridge Components: Lessons from the Duisburg Bridge ProjectInternal demonstratorFunctionally Graded LMD Pipe DemonstratorFAQDesign for LMD: geometry, machining and inspection accessServiceMetal AM: LMD & SLM
Metal AMStart manufacturing RFQOne more related item is available through the linked hub pages.