Augmented Handheld Additive Manufacturing

Abstract

What if repairing a damaged composite panel could be as precise and repeatable as manufacturing a new one? Despite the widespread use of composite materials in the aerospace, automotive, and renewable energy sectors, in situ repair methods remain largely manual and often fail to restore the original mechanical integrity. The increasing use of fibre-reinforced polymers as a result of their high strength-to-weight ratio, fatigue, and corrosion resistance increases the need for more advanced and reliable repair solutions. This demand motivates the development of semi-automated systems to improve both repeatability and performance in composite repair.

This thesis investigates the feasibility of a semi-automated handheld robotic system for in situ material deposition, leveraging human guidance alongside robotic precision. To this end, a prototype handheld extrusion device was developed, integrating onboard optical flow sensors for real-time positional correction. Its control architecture enables user-guided motion while autonomously compensating for tracking errors, combining human adaptability with the repeatability of robotic control to ensure consistent material deposition.

To evaluate system performance, a surface-level defect modelled as a 175 mm long crack was introduced into a wooden substrate. Material was deposited into the defect under controlled conditions using the prototype, completing the process in 35 s. The device successfully performed real-time correction and deposition, demonstrating the feasibility of augmented handheld additive repair. Average accuracies of 1.05 mm in x and 2.45 mm in y were achieved after movements of about 300 mm in global x and 70 mm in global y. This level of precision demonstrates the basic capabilities of the system, but targeted improvements will be required to meet the demands of industrial deployment.

While developed for composite repair, the underlying technology is versatile and applicable across various domains. Its ability to deposit functional materials with spatial precision suggests potential use cases in structural health monitoring and field-deployable additive manufacturing. Future extensions, such as support for curved surfaces, vision-based localisation, and integrated non-destructive testing methods, could significantly enhance system capability. These developments may enable intelligent, semi-autonomous platforms that combine human intuition with robotic accuracy for material deposition in complex field environments.