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About

Dmitry Fominykh is a researcher specializing in robotic process automation, welding quality control, and intelligent manufacturing systems. His work sits at the intersection of industrial robotics and computational modeling, with a particular focus on ensuring the reliability and safety of automated welding processes in man-machine production environments. Fominykh's most significant contributions center on developing mathematical models and algorithms to minimize critical event combinations that can lead to quality failures or accident situations in robotic welding systems. His 2018 paper on quality assurance in robotic welding complexes, his most cited work with 12 citations, introduced novel optimization criteria for reducing the probability of dangerous process states. Complementing this, his 2017 work on preventing critical event combinations, garnering 10 citations, established foundational frameworks for safety-oriented control in human-robot production systems. A recurring theme across his research portfolio is the application of system dynamics modeling to describe and regulate welding process behavior under unstable or risky conditions. His later publications from 2019 further refined quality-driven control strategies using goal function optimization. Collectively, Fominykh's contributions provide engineers and researchers with practical algorithmic tools for improving both the safety and product quality of robotic manufacturing workflows, establishing him as a focused contributor to intelligent industrial automation research.