Tool wear is a critical factor affecting machining processes, impacting part quality, equipment downtime and production costs. The study focuses on the late stage of wear using a multi-axis grinding operation to simulate artificial tool wear conditions. Verification is carried out through microscopic analysis, process force measurements and finite element analysis. The study uses artificial intelligence (AI) techniques, including machine learning and neural networks, to monitor tool wear and optimize the wear process.