Takehiro Hosoda

Papers

Total Citations

H-Index

About

Takehiro Hosoda is a researcher whose work lies at the intersection of intelligent manufacturing, robotic welding systems, and computational optimization. His primary contributions focus on enhancing the precision and efficiency of automated welding processes, a critical area for industries like shipbuilding and heavy fabrication. In his most cited work, "Sensing and control of weld pool by fuzzy-neural network in robotic welding system" (2002, 18 citations), Hosoda addresses the challenge of controlling weld penetration depth—a key determinant of weld quality—by developing a fuzzy-neural network model that indirectly infers penetration from visual sensor data, circumventing the difficulty of direct measurement. This innovative fusion of soft computing and real-time sensing has provided a foundational approach for adaptive control in robotic welding. Additionally, his paper "Optimum method of working assignment in some welding robots with genetic algorithm" (2002, 3 citations) tackles the logistical complexity of coordinating multiple robots in shipyard environments, using genetic algorithms to minimize task completion time. While his citation counts reflect a focused, niche impact, Hosoda’s work exemplifies the practical integration of artificial intelligence and robotics to solve tangible industrial problems, offering valuable insights for researchers in manufacturing automation and intelligent control systems.