Automated Wear Inspection of Gear Hob Tools Using Machine Vision

To reduce tool-related costs and support sustainable manufacturing strategies in gear production, reconditioning of worn gear hob tools is essential. However, this requires a reliable, objective, and cost-efficient method to assess tool wear, which is a task traditionally handled manually with limited repeatability and significant operator influence. This paper presents a fully automated inspection system architecture based on machine vision. A functional prototype demonstrates the system’s ability to consistently identify and evaluate the relevant metrics tooth length and wear. The proposed approach enables scalable, economical quality control in industrial regrinding workflows and contributes to transparent tool life-cycle management.