Automating the deburring of laser-cut sheet metal parts remains challenging due to the need for both reliable part detection and controlled material removal. This paper presents the development and experimental validation of a vision-guided robotic grinding system for automated deburring of laser-cut metal components. The system integrates a collaborative robot, a custom grinding end-effector, a load-cell-based force control system, and a machine vision setup using optimized backlighting and contour detection. Qualitative results showed complete burr, dross, and heat-affected zone removal in a single pass for both mild steel and stainless-steel specimens. Reliability testing yielded a 96% success rate across 50 processed parts. The results confirm that integrated machine vision and force-controlled robotic grinding can provide a viable alternative to manual deburring and expensive big machines in sheet metal manufacturing, offering improved consistency, lower physical workload, and c ompetitive processing time.