Daniel Aguinaga

Papers

Total Citations

138

H-Index

About

Daniel Aguinaga is a leading researcher at the intersection of industrial automation, visual computing, and sustainable manufacturing, with a particular focus on advancing the Industry 4.0 paradigm. His most influential work, a 2019 paper on sustainable and flexible industrial human-machine interfaces (132 citations), established foundational frameworks for integrating Cyber Physical Systems and the Internet of Things into adaptable manufacturing environments. More recently, Aguinaga has pioneered deep learning-based quality control systems, developing a novel solder joint defect detector that leverages neural networks to enable Zero Defect Manufacturing in electronics production. His practical contributions include a sophisticated vision system for automatic inspection of solder joints on electronic boards, employing a dual-camera configuration with optimized lighting to achieve robust, real-time quality assessment. This work directly addresses the industry’s need for flexible, in-line inspection of 100% of produced parts. Aguinaga’s research is notable for bridging theoretical advances in computer vision with tangible industrial applications, helping to make smart factories more efficient, sustainable, and responsive to rapid technological change.