Deep learning-based temperature prediction during rotary ultrasonic bone drilling

Abstract

Bone drilling is a common but critical medical procedure in orthopedic surgeries used to treat fractured bones. During this procedure, the temperature of the bone increases due to generation of frictional energy. Temperature control has been a major challenge in bone drilling since its foundation. If this temperature increases over 47°C for 1 min, then it can result in permanent bone damage. To control the temperature elevation this study proposes a deep learning-based robust predictive model which has been trained and tested on data from pig bones. Excessive in-house testing has been done on pig femur bones to gather data and verify the results. Rotary ultrasonic bone drilling was the machining process used for drilling. Four independent variables which were rotational speed, feed rate, abrasive grit size, and vibrational ultrasonic power were varied and the temperature for each set of values was recorded. Multiple deep learning models were made and were compared on different error metrics. It was found that convolutional neural network 1D gave the least error over other models. The error generated by deep learning models was less than mathematical and experimental models.