Geometrically parameterized reduced-order finite element model for guitar soundboard shape optimization

The art of guitar craftsmanship has been a highly valued tradition, passed down through generations of renowned luthiers. While this centuries-old craft remains celebrated, the quest to define the tone quality of a guitar remains an enduring mystery. As a result, replicating the sound profile of highly valued guitars, renowned for their exceptional sound quality among musicians, has become a common goal among luthiers. Although geometrically identical copies were produced, audible differences were observed, which can mainly be attributed to the natural variation in the wood. The objective of this work is to develop a guitar soundboard shape optimization methodology to compensate for material variability in terms of eigenfrequencies and eigenmodes, extending beyond the exceptional skills and intuition of luthiers. The significant computational demands arising from the numerous model evaluations required during shape optimization, present a notable challenge. To address this, we present a novel approach employing parameterized shell finite element models with affine parameter dependency, integrated with a mesh morphing technique, which enables the application of parametric model order reduction. This approach substantially enhances computational efficiency in guitar soundboard optimization, while preserving both parameter dependence and mesh topology. This method can be efficiently applied to identify the optimal geometry of a guitar soundboard, taking into account the effects of material parameter variations through virtual prototyping. Although this study is primarily focused on guitar soundboards, the approach is also adaptable to other string instruments and their complete structures, as well as structures with sufficient esemblance to guitar soundboards.