Yoshinori Oka

Papers

Total Citations

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About

Yoshinori Oka is a leading researcher in industrial automation and manufacturing quality control, with a particular focus on robotic painting systems. His most cited work, "Design of a Database-Driven Quality Predictor for Painting Systems" (2022), addresses critical challenges in modern manufacturing, specifically the labor shortages driven by demographic shifts in aging societies. Oka's major contribution lies in developing a database-driven predictive model that enhances painting quality for 6-axis industrial robots used in excavator manufacturing. By integrating real-time data analysis with robotic control, his work enables consistent, high-quality finishes while reducing reliance on skilled human painters. This innovation directly supports the transition toward fully automated painting lines in heavy machinery production. Though his citation count is currently modest at 2, the practical significance of his research—combining robotics, data science, and manufacturing engineering—positions it as a foundational reference for future studies in smart factory quality assurance. Oka's work exemplifies how applied research can solve pressing industrial workforce challenges through intelligent automation.