Abstract
This paper proposes an analytical model that uses historical damage dimension data to deduce physical impactor characteristics (size and energy) that has caused a certain resulting damage. Maintenance tasks occur in operations due to impact, however the source of the damage caused in the event remains in most cases unknown. Consequently, by inferring what has caused a certain type of damage from the distribution of the damage type and severity relative to impactor types, maintainers can be better prepared in terms of what to expect from a given impactor source. The developed model introduces a novel transition deformation region between the local deformation and the global plate deflection, allowing for fast and accurate predictions of the impact event. Using the known aluminium structural properties and damage dimensions, the damage data is converted into impactor data. The model is applied in a case study using 120 fuselage dent damages dimensions (length, width, and depth) from a Boeing 777 fleet. The results show that the model deduces impactor characteristics for 94% of the considered damages, ranging up to 240 J and 110 mm for impactor energy and radius respectively.
Highlights
Airworthiness, in terms of the external structure, is challenged daily by damage occurrence caused by impact events
The usual approach of determining the damage resulting from a certain impactor, characterised by material, size, and energy, has been reversed to test the degree to which damage dimensions can be used to deduce and identify the source characteristics of the damage
The deductive approach captures the variability in the impact threat characteristics
Summary
Airworthiness, in terms of the external structure (fuselage and wing skin), is challenged daily by damage occurrence caused by impact events. Current state-of-the-art considers impact damage and the effect it has on aircraft maintenance, as demonstrated by Chen et al showing that impacts cause more than 50% of all aircraft structural damage such as dents, delamination and holes (Chen et al, 2014) These types of damage vary in size (diameter and depth) (Chen et al, 2014), requiring temporary or permanent repairs based on severity. The focus of the present work is attributed to low-speed impact damage which covers tool and equipment drops, walk traffic during maintenance, luggage drops, and some ground collisions These correspond to the majority of reported impact events in service. The analytical model is reverse engineered to deduce the impactor characteristics (radius and energy) from the permanent damage as an input These two steps are respectively referred to as the inductive problem (i.e. determine the damage) and deductive problem (i.e. estimate the impactor). By comparing specific impact cases with the analytical model and the computational model, the MIDAS-M range of applicability is determined
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