A data driven surrogate was developed to bridge the gap between finite element and multibody modeling and to expand the information available from a rigid multibody cartilage simulation. An indentation experiment performed on canine stifle cartilage was modeled in both paradigms with acceptable accuracy and the data were used to create the surrogate. Neural networks were found to adequately approximate the von Mises stress calculated by the finite element model based on force values provided from the multibody model with a correlation coefficient over 0.96.
It has been established that in order to accurately model a knee joint a reasonable approximation of the soft tissues present is necessary1. Models which include these soft tissue structures are able to better reproduce joint kinematics, loading, and analyze the impact of damage and pathological joint behavior1. Simulating the behavior of these tissues requires either a detailed understanding of materials properties that can be implemented via finite element models or the production of an empirical model that can be implemented inside other model frameworks2,3. This study explores the application of multibody (MB) modeling techniques in an attempt to capture the flexible behavior of biological tissues inside of a rigid body mechanics software, MD ADAMS (MSC software, Santa Ana, California), by tuning the performance to experimental data using design of experiments (DOE).
The meniscus is a crucial anatomical structure in the mechanics of vertebrate hind legs [3]. Menisci function primarily by distributing the tibio-femoral contact forces, and thereby reducing the stress in the articular cartilage of the knee joint. As the meniscus is a flexible body that undergoes large strains, it is typically ignored in rigid-body biomechanical simulations. One documented method of including this factor in the multi-body framework is to represent the menisci as discrete bodies connected by linear 6-axis spring and damper elements [2]. The difficulty arises in determining the stiffnesses and viscosities that correspond to the material properties of the real meniscus. Material properties have previously been determined by a design of experiments approach to match the force displacement behavior of a multi-body model to a linear finite element model. This study explores a method of determining the said properties from experimental data collected in a semi-physiological loading, where the force orientation is principally circumferential tension and compression in the other directions.
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