Bio-Inspired Robotic Tap Testing: An Innovative Approach for Nondestructive Testing of Wooden Structures

Abstract

Tap testing is an effective way of characterizing material conditions and flaws in various materials, including wood. Given its versatility and widespread usage, wood requires thorough inspection to assess its quality, identify potential defects, and ensure the safety and durability of wooden structures across diverse applications. This technique has the advantage of being simple, efficient and inexpensive. The tap testing method, when performed manually, requires an operator to tap each point of the structure using a hand-held object (e.g., a coin or tap hammer). Consequently, the precision of this test is highly reliant on the inspector's subjective interpretation of the vibrational acoustic response. In order to overcome this drawback, a bio-inspired tap testing approach with augmented objectivity of signal analysis has been proposed. The pioneer tap testing is inspired by an animal named aye-aye recognized for its unique acoustic-based foraging behavior called 'tap-scanning' or 'percussive foraging'. The aye-aye's near-field versatile acoustic sensing capabilities enable it to locate small cavities beneath a tree bark with complex materials. Current work describes a quantitative and instrumented robotic tap test system that creates repeatable mechanical impacts using a biomimetic approach. Two specimens were utilized to validate the effectiveness of this biomimetic approach. One of the specimens possessed identical diameter flat bottom holes but of varying depths, and the other had different diameters at positions of the same thickness from the test surface. Biomimetic tap scanning was applied over the defect-free and damaged areas of the specimens utilizing the 3D printed animal pinna and head in the experimental setup. The findings indicated that the biological characteristics of the animal's external auditory organs including the pinna and ear cannel substantially enhanced the system's sensitivity in detecting artificial defects within wooden blocks. This enhancement was primarily attributed to a notable improvement in the signal-to-noise ratio. Moreover, the outcomes demonstrated that the head and external ear structure exerted a superior discriminating factor for damage detection compared to both the pinna with ear canal configuration and the microphone-only setup within the experimental framework. The underlying cause behind this heightened discriminating factor remains undetermined and warrants further investigation by the research team.