Bhatt, Alisha; (2024) Multi-Scale additive manufactured embedded sensors for self-cognitive metal parts. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Metal additive manufacturing (AM) techniques like laser powder bed fusion can print highly complex geometries, ideally with internal sensors. Depending on the component shape these internal sensors, may only be accessible and monitored from the outer surface of the component. Hence, placing or printing of the sensors inside the component requires the sensor to communicate information from outside with remote sensing. Ideally, sensors would be added during printing; however, during LPBF very high temperatures (> 3347℃) are reached causing thermal damage to sensors. Two sets of results are presented. Firstly, four sensors were successfully designed and embedded using two types of novel sensors. An embedding methodology was developed and validated for strain monitoring in Ti-6Al-4V components. A powder protective layer was introduced to prevent damaging the sensors during the laser scanning process. An optimal 1 mm powder protective layer was determined using computational analysis and validated through three-point flexural bench testing. A 1 mm powder protective layer was effective for the strain gauges that were printed using direct ink write (DIW) with glass fibre (GF) reinforced phenolic backing and tripropylene glycol diacrylate (TPGDA) backing. Surface roughness affects the mechanical performance and durability of LPBF components. The surface topology requirements also vary on component application. The evolution mechanisms of surface roughness during LPBF are not well understood due to a lack of in situ characterisation methods. Therefore, the second set of experiment focused on defect dynamics are quantified using synchrotron X-ray imaging and ex situ optical imaging and explain the evolution mechanisms of side-skin and top-skin roughness during multi-layer LPBF of Ti-6Al-4V. Then a surface topology matrix was developed that accurately describes surface features. The results suggest that the proposed process can open new avenues for LPBF technology to realise metal components with a self-cognitive ability using integrated sensors and highlight the need for hybrid smart manufacturing to meet the demands of multiple sectors e.g., biomedical and aerospace.

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