Assembly Quality Diagnosis of Planetary Gear Sets



Soonyoung Han Jiung Huh Hae-Jin Choi


Product quality is one of the most important factors to be considered in manufacturing industries. Autonomous production line rapidly increases its productivity; however, quality check becomes a difficult problem and a smart system for quality assurance is indispensable in the modern production line In this study, we developed a transmission error based machine learning algorithm to check the quality of planetary gear assemblies and identify the defective parts in the planetary gear sets.

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