<![CDATA[This paper implements an automated gear sorting framework that combines machine vision with in-line mass testing on a SCARA robot platform for industrial quality inspection. Gear flaws like missing tooth profiles or weight variations compromise transmission reliability, often leading to unexpected mechanical breakdown. The authors address this by pairing a vision-based defect algorithm - which catches missing teeth - with a load cell that flags hidden structural voids or density loss. Unlike single-camera systems that miss internal flaws, our approach brings optical and weight metrics together inside one SCARA architecture for multi-modal inspection. The cell ties image processing and kinematic models directly to end-effector control, driving real-time pick-and-place sorting. Benchtop testing over 50 consecutive runs showed 100% sorting accuracy under a ±1 g weight tolerance, maintaining consistent repeatability. Adding physical mass validation to visual checks significantly lowers the chance of passing defective internal parts. Using an Epson LS3-401S SCARA robot with ±0.02 mm repeatability, the prototype confirmed its operational stability in controlled tests, proving its potential for production-line integration.]]>
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