Repository logo

Automated recognition and correction of warp deformation in extrusion additive manufacturing

Published version



Change log



Warp deformation is a common error encountered in additive manufacturing. It is typically caused by residual internal stresses in the manufactured part that arise as material cools. These errors are challenging to prevent or correct as they build over time and thus are only visible long after the actions that caused them. As a result, existing work in extrusion additive man- ufacturing has attempted warp detection but not correction or prevention. We report a hybrid approach combining deep learning, computer vision, and expert heuristics to correct or prevent warp. We train a deep convolutional neural network using diverse labelled images to recognise warp in real-time. We compute five metrics from detection candidates to predict the severity of warp deformation and proportionately update print settings. This enables the first demon- stration of automated warp detection and correction both during printing and for future prints.



Extrusion 3D printing, Warp deformation, Machine learning, Computer vision, Error detection and correction

Journal Title

Additive Manufacturing

Conference Name

Journal ISSN


Volume Title


Elsevier BV
Academy of Medical Sciences (SBF005\1014)
Department for Business, Energy and Industrial Strategy (EP/V062123/1)
Engineering and Physical Sciences Research Council (2274909)
Engineering and Physical Sciences Research Council (EP/N509620/1)
This work has been funded by the Engineering and Physical Sciences Research Council (EP- SRC) PhD Studentship EP/N509620/1 to Douglas Brion, Royal Society award RGS/R2/192433 to Sebastian Pattinson, Academy of Medical Sciences award SBF005/1014 to Sebastian Pattin- 600 son, Engineering and Physical Sciences Research Council award EP/V062123/1 to Sebastian Pattinson, and An Isaac Newton Trust award to Sebastian Pattinson.