Jack C. Chaplin
Papers
9
Total Citations
208
H-Index
7
About
Jack C. Chaplin is a manufacturing systems researcher whose work sits at the intersection of artificial intelligence, digital twins, and reconfigurable production systems. His research addresses one of modern industry's most pressing challenges: designing manufacturing environments that are intelligent, adaptive, and capable of responding dynamically to changing production demands. Chaplin's most influential contribution, "A Framework for Manufacturing System Reconfiguration and Optimisation Utilising Digital Twins and Modular Artificial Intelligence" (2023), has accumulated an impressive 130 citations, reflecting its significance in advancing beyond static digital twin architectures toward modular, scalable solutions. This work, alongside complementary frameworks from 2022, establishes him as a leading voice in AI-driven manufacturing reconfiguration. His research extends into plug-and-produce manufacturing apps, self-configuring robotic platforms, and energy optimization for industrial robots — tackling both flexibility and sustainability in production systems. Earlier work on cyber-physical assembly systems and the Evolvable Assembly Systems project demonstrates his long-standing commitment to transformable manufacturing, with notable applications in high-complexity sectors such as aerospace. His 2025 paper on self-learning digital twins further signals his forward-looking research trajectory. Collectively, Chaplin's publications offer students and researchers a cohesive and practical roadmap for building the intelligent factories of tomorrow.
Research Focus
Key Achievements
Top Papers
- 1
- 2Towards Modular and Plug-and-Produce Manufacturing Apps16 citations · 2022
- 3Online and Modular Energy Consumption Optimization of Industrial Robots14 citations · 2023
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- 9Context-Aware Plug and Produce for Robotic Aerospace Assembly4 citations · 2021