Freeform shape clustering for customized design automation

Abstract

Automation can provide significant performance improvements in digital manufacturing systems that customize shapes of implants and prosthetic devices to the anatomy of a patient. The challenge, however, lies in the ability of an automatic solution to adapt to anatomical variations of a given object category. This paper presents a hierarchical framework that generalizes the digital design of anatomical surface models in terms of a small number of prototypes. The latter are derived from the local shape information of constituent parts via shape matching and clustering and then associated with one operation that dictates how a shape undergoes modification. We demonstrate the proposed technique through application to typical hearing aid design operations with promising results.