Shot peening is a well-established surface treatment that enhances mechanical performance by inducing plastic deformation, grain refinement and compressive residual stresses. This study evaluates the effects of shot peening on wire arc additively manufactured (WAAM) AZ80 magnesium alloy, benchmarked against wrought Al6082 aluminium—a widely used aerospace material. WAAM AZ80 was assessed in two states: a baseline variant (P1) with significant retained process-induced defects, and an improved version (P2) produced via refined processing parameters. Microstructure, hardness, surface roughness, residual stress and fatigue behaviour were systematically analysed. Shot peening induced notable grain refinement in both materials without causing phase transformations. Surface hardness increased by 18% in Al6082, and by 37% and 45% in WAAM AZ80 (P1 and P2, respectively), mainly due to work hardening and grain boundary strengthening. Increased surface roughness was observed across all conditions. Al6082 exhibited peak compressive residual stresses of ~ 280 MPa at depths > 100 µm, whilst WAAM AZ80 reached ~ 110 MPa at ~ 75 µm. Fatigue life improved by 44% in Al6082, and by 100–350% depending on the orientation of residual defects caused by the AM process in the WAAM AZ80 (P1) specimens. Shot peening proved effective in mitigating the influence of surface and near-surface defects. Densified WAAM AZ80 (P2) samples also exhibited significantly improved fatigue performance after shot peening, reaching the test run-out of 10 ⁶ cycles without failure. The improvements in WAAM AZ80 are largely attributed to reduced surface-connected porosity and delayed crack initiation. Whilst Al6082 retains superior fatigue properties, shot-peened WAAM AZ80 shows strong potential as a lightweight alternative for weight-sensitive aerospace applications involving cyclic loading.

The aviation industry has proposed multiple solutions to reduce fuel consumption, air pollution, and noise at airports, one of which involves deploying electric trucks for aircraft towing between the stand and the runway. However, the introduction of tow trucks results in increased surface traffic, posing challenges from the perspective of air traffic controllers (ATCOs). Various solutions involving automated planning and execution have been proposed, but many are constrained by their inability to manage multiple active runways simultaneously, and their failure to account for the tow truck battery state of charge during assignments. This paper presents a novel system for taxi operations that employs autonomous tow trucks to enhance ground operations and address deficiencies in existing approaches. The system focuses on identifying conflict-free solutions that minimise taxi-related delays and route length while maximising the efficient use of the tow trucks. The algorithm operates at a strategic level and uses a centralised approach. It has the capacity to cater for multiple active runways and considers factors such as the tow truck battery state of charge and availability of charging stations. Furthermore, the proposed algorithm is capable of scheduling and routing tow trucks for aircraft taxiing without generating traffic conflicts.

The aviation industry has proposed multiple solutions to reduce fuel consumption, air pollution and noise at airports, one of which involves deploying electric trucks for aircraft towing between the stand and the runway. However, the introduction of tow trucks results in increased surface traffic, posing challenges from the perspective of Air Traffic Controller's (ATCO). Various solutions involving automated planning and execution have been proposed, but many are constrained by their inability to manage multiple active runways simultaneously, and their failure to account for tow truck battery state-of-charge during assignments. This paper presents a novel system for taxi operations that employs autonomous tow trucks to enhance ground operations and address deficiencies in existing approaches. The system focuses on identifying conflict-free solutions that minimize taxi-related delays and route length while maximising the efficient use of the tow trucks. The algorithm operates at a strategic level and uses a centralised approach. It has the capacity to cater for multiple active runways and considers factors such as tow truck battery state-of-charge and availability of charging stations. Furthermore, the proposed algorithm is capable of scheduling and routing tow trucks for aircraft taxiing without generating traffic conflicts.