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Drilling is a widely employed technique in machining processes, crucial for efficient material removal. However, when applied to living tissues, its invasiveness must be carefully considered. This study investigates drilling processes on polyurethane foam blocks mimicking human bone mechanical properties. Various drill bit types (118° twist, 135° twist, spherical, and conical), drilling speeds (1000–1600 rpm), and feed rates (20–80 mm/min) were examined to assess temperature elevation during drilling. The Taguchi method facilitated systematic experiment design and optimization. Signal-to-noise (S/N) ratio and analysis of variance (ANOVA) identified significant drilling parameters affecting temperature rise. Validation was conducted through confirmation testing. Results indicate that standard twist drill bits with smaller point angles, lower drilling speeds, and higher feed rates effectively minimize temperature elevation during drilling.

Surgeons face a significant challenge due to the heat generated during drilling, as excessive temperatures at the bone–tool interface can lead to irreversible damage to the regenerative soft tissue and result in thermal osteonecrosis. While previous studies have explored the use of machine learning to predict the temperature rise during bone drilling, this in vitro study introduces a comprehensive approach by combining the Response Surface Methodology (RSM) with advanced machine learning techniques. The main objective lies in the comprehensive evaluation and comparison of support vector machine (SVM) and random forest (RF) models specifically for the optimization of the bone drilling parameters to prevent thermal bone necrosis. A total of 27 experiments were conducted using a multi-level factorial method, with analysis performed via the Minitab software version 19.1. Performance metrics such as the mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were used to assess model accuracy. The RF model emerged as the most effective, with R2 values of 94.2% for testing and 97.3% for training data, significantly outperforming other models in predicting temperature fluctuations. This study demonstrates the superior predictive capabilities of the RF model and offers a robust framework for the optimization of surgical procedures to mitigate the risk of thermal damage.

This study aims to summarize the current state of scientific knowledge on factors that contribute to heat generation during the bone drilling process and how these aspects can be better understood and avoided in the future through new research methodologies. Frictional pressures, mechanical trauma, and surgical methods can cause thermal damage and significant micro-fracturing, which can impede bone recovery. According to current trends in the technical growth of the dental and orthopedic industries’ 4.0 revaluation, enhancing drill bit design is one of the most feasible and cost-effective alternatives. In recent years, research on drilling bones has become important to reduce bone tissue damage, such as osteonecrosis (ON), and other problems that can happen during surgery. Reviewing the influence of feed rate, drill design, drill fatigue, drill speed, and force applied during osteotomies, all of which contribute to heat generation, was a major focus of this article. This comprehensive review can aid medical surgeons and drill bit makers in comprehending the recent improvements through optimization strategies for reducing or limiting thermal damage in bone drilling procedures used in the dental and orthopedic industries.