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Bone surgery on the head requires the use of machining techniques for treatment. During machining, structure-borne noise and heat are generated, which are also proportionally introduced into the tissue close to the surgical site and thus transmitted to the auditory organ primarily through structure-borne noise. Aim of the project is the development of a self-learning AI-based assistance system for ear surgery. First experiments of the project investigate the influence of contact force and alignment of the burr on the generation of noise.

The bone substitute samples were milled using a drill with rose and diamond burr. All experiments were performed by the same surgeon, varying the tool, rotation speed, cutter orientation and applied force. The forces were recorded using a piezoelectric dynamometer mounted below the specimen holder. The vibrations introduced into the sample were rec-orded close to the process using a three-axis accelerometer and the airborne noise generated during milling was recorded using a microphone and preamplifier. The data records were acquired uniformly via a DAQ and stored on a PC.

At high speed and steep alignment of the milling cutter, the airborne noise increases sharply while hardly any material is removed. In contrast, if the tool is optimal aligned, volume removal can be made much more efficient and quieter. Fur-thermore, the experiments show that a high force input results in a high noise level. To improve the validity of the ex-periments, the tests will be repeated on temporal bone specimens to ensure consistent material behavior.

Overall, careful balancing of these factors is important to minimize noise exposure during skull milling. Optimal posi-tioning of the drill by fine-tuning the angle and contact force, as well as reducing the speed and selecting the appropri-ate cutter, can help to control the noise level and reduce the stress for the patient and the surgical team.