The aim of this thesis was to develop and optimize deep learning models specifically designed for the identification of tool wear on microscopic images of cutting tools and cutting tool edges. Cutting tool wear has an impact on dimensional accuracy and surface quality of parts, ultimately affecting the costs associated with meeting part quality criteria.To accomplish this objective, the creation of a tool wear model based on empirical tool life trials was conducted. An outcome of the trials was the generation of a dataset of images, which were then utilized to develop a deep learning model...