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Digiquam - huvud

DIGIQUAM - digital platform for additive manufacturing

DIGIQUAM is a software for additive manufacturing that enables the collection and analysis of data along the whole AM value chain. It features state of the art quality control and machine learning algorithm for process optimisation and defect detection. This digital platform can be connected to a 3D printer and perform real-time or batch analysis.

Challenge in AM

Current analytical solutions for AM (Additive Manufacturing) are often specific to a single step of the whole process such as simulation or manufacturing and have limitations in what they collect and analyze. They operate as data silos where the data collected is rarely shared to other steps of the AM value chain, hindering the traceability and quality assurance of printed parts and limiting our understanding of the AM process and its numerous parameters. To solve this issue, the digital platform DIGIQUAM collects and analyses the data from all the AM process steps and uses the power of big data analytics to improve the manufacturing process and traceability.

The project

Digiquam is the result of a 1 year EIT Manufacturing project involving 4 European partners (Prima, Lortek, RISE and Chalmers). The platform is a software that can be connected to a 3D printer to perform real-time analysis or can operate as a stand-alone and perform batch analysis. It introduces state of the art features such as:

Results from a computer vision algorithm for deviation detection. The original image (left) is compared to its CAD model (center left) and results in a deviation detection image (center right) where pixels in red show deviations. Finally a 3D model is made from these images (right). The part at the top shows very little deviation while the part at the bottom shows important ones at its core.
  • Prediction: Powder error prediction algorithm
  • Monitoring: real time sensor monitoring
  • Optimisation: real time optimal parameters recommendation algorithm
  • Slicer: advanced slicer allowing the slicing of multiple stl at once to extract layer design data.
  • Quality control: real time comparison of a printed component to its CAD based on images taken during the printing process and enabling the possibility for deviation detection
  • Recoating monitoring: real time recoating monitoring based on post recoating images enabling the possibility to diagnose recoating issues
  • 3D modelisations: Export of the 3D modelisations in standard txt files for further analysis in CAD software.
  • Post-processing optimisation

The platform collects the data from the various steps of the process and uses AI and machine learning algorithms to guide the user through potential optimisations or error predictions during printing. Moreover, the quality control module can ensure that every printed layer is identical to the CAD by providing 2D and 3D visualisations enabling fast and precise deviation detections.

The future of AM software

DIGIQUAM is part of the next generation of AM software coming on the market that feature artificial intelligence extensively. The platform can assist the operators in every step of the process, from design to defect detection during printing to optimized post-processing. It improves quality control and traceability, and help engineers and customers to better understand their print. DIGIQUAM brings the industry one step closer to live defect correction and will help AM manufacturers to design machine that can adapt their parameters during manufacturing.

The platform is still under development and next release will extend its quality control capabilities by identifying which defects can be treated by post-processing and how they should be treated. If you would like to try the platform and collaborate on research project involving DIGIQUAM, feel free to contact the team.

DIGIQUAM - Presentation

DIGIQUAM - Fact sheet (pdf, 267.74 KB)

DIGIQUAM - Presentation (pdf, 1.63 MB)

Summary

Project name

DIGIQUAM

Status

Active

Region

Other than Sweden

RISE role in project

Koordinator

Project start

Duration

1 år

Total budget

647 459€

Partner

Chalmers, Lortek, Prima Additive

Funders

EIT Manufacturing, EIT Manufacturing

Project members

Supports the UN sustainability goals

7. Affordable and clean energy
8. Decent work and economic growth
9. Industry, innovation and infrastructure
12. Responsible consumption and production
17. Partnerships for the goals