Abstract:
:Dynamic occlusal loading during mastication is clinically relevant in the design and functional assessment of dental restorations and removable dentures, and in evaluating temporomandibular joint dysfunction. The aim of this study was to develop a modelling framework to evaluate subject-specific dynamic occlusal loading during chewing and biting over the entire dental arch. Measurements of jaw motion were performed on one healthy male adult using low-profile electromagnetic field sensors attached to the teeth, and occlusal anatomy quantified using an intra-oral scanner. During testing, the subject chewed and maximally compressed a piece of rubber between both second molars, first molars, premolars and their central incisors. The occlusal anatomy, rubber geometry and experimentally measured rubber material properties were combined in a finite element model. The measured mandibular motion was used to kinematically drive model simulations of chewing and biting of the rubber sample. Three-dimensional dynamic bite forces and contact pressures across the occlusal surfaces were then calculated. Both chewing and biting on the first molars produced the highest bite forces across the dental arch, and a large amount of anterior shear force was produced at the incisors and the second molars. During chewing, the initial tooth-rubber contact evolved from the buccal sides of the molars to the lingual sides at full mouth closure. Low-profile electromagnetic field sensors were shown to provide a clinically relevant measure of jaw kinematics with sufficient accuracy to drive finite element models of occlusal loading during chewing and biting. The modelling framework presented provides a basis for calculation of physiological, dynamic occlusal loading across the dental arch.
journal_name
Ann Biomed Engjournal_title
Annals of biomedical engineeringauthors
Woodford SC,Robinson DL,Edelmann C,Mehl A,Röhrle O,Vee Sin Lee P,Ackland DCdoi
10.1007/s10439-020-02688-6subject
Has Abstractpub_date
2021-01-06 00:00:00eissn
0090-6964issn
1573-9686pii
10.1007/s10439-020-02688-6pub_type
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