Analysing contact conditions in hard-on-hard hip replacements: effectiveness of current analytical methods and novel data-driven approach

TL;DR

This paper evaluates existing analytical contact models for hip implants, finds their limitations, and introduces a new data-driven neural network approach that better predicts contact conditions considering implant parameters.

Contribution

The study develops a novel ANN model for contact prediction in hip implants, improving accuracy over traditional analytical models and highlighting the importance of cup thickness.

Findings

Existing Hertz and Fang models fail during gait cycle

The ANN model accurately predicts contact conditions

Cup thickness significantly influences contact outcomes

Abstract

Contact mechanics models that can accurately estimate the contact conditions and, consequently predict wear must be developed for hip implants. This study analyses and verifies the existing Hertz and Fang analytical models applicable to hard-on-hard hip implants. The contact parameters, such as the maximum contact pressure, contact radius and the maximum deformation, are considered for the validation with FEM. Both analytical models fail to predict the contact conditions throughout a gait cycle. A novel data-driven ANN model is developed to comprehensively predict the contact conditions considering different input parameters. The analysis shows that cup thickness significantly affects the output contact conditions. Therefore, it is recommended to consider cup thickness in the analytical model interpretation for hard-on-hard hip implants.