Innovative Research Award
Miroslav Petrov
Technical University of Sofia, Bulgaria
Miroslav Petrov is a researcher affiliated with the Technical University of Sofia whose academic work focuses on advanced manufacturing technologies, intelligent production systems, and data-driven engineering. His recent publication demonstrates the application of artificial intelligence techniques for predicting deposition geometry in wire-arc additive manufacturing, supporting improved precision and process optimization for large metallic structures. This interdisciplinary approach integrates manufacturing science, computational modelling, and machine learning to address practical industrial challenges.[1]
Abstract
The research activities of Miroslav Petrov emphasize intelligent manufacturing through the integration of artificial neural networks, support vector machines, and additive manufacturing technologies. His published work investigates predictive modelling techniques capable of improving dimensional accuracy and production efficiency for metallic components. The research contributes to digital manufacturing by combining experimental validation with computational optimization methods suitable for industrial applications.
Keywords
Wire-Arc Additive Manufacturing, Artificial Neural Networks, Support Vector Machines, Manufacturing Intelligence, Metallic Components, Process Prediction, Machine Learning, Digital Fabrication.
Introduction
Manufacturing industries increasingly rely on intelligent computational methods to improve productivity and product quality. Predictive modelling enables engineers to estimate process outcomes before fabrication, reducing waste and enhancing repeatability. Research in this field supports the development of advanced production systems aligned with Industry 4.0 principles.
Research Profile
Petrov’s scholarly profile reflects interdisciplinary expertise combining manufacturing engineering, artificial intelligence, and computational modelling. His collaboration with international researchers illustrates the growing importance of intelligent analytical methods for solving complex engineering problems involving metallic fabrication and process optimization.[1]
Research Contributions
His featured publication presents a geometrical prediction framework for copper-coated solid-wire deposition using artificial neural networks and support vector machines. The study demonstrates how data-driven models can accurately estimate deposition characteristics, thereby supporting process control, manufacturing efficiency, and the fabrication of complex metallic structures.
Publication
- Geometrical Prediction of Copper-Coated Solid-Wire Deposition by Wire-Arc Additive Manufacturing Based on Artificial Neural Networks and Support Vector Machines. Metrology, 2026.
Research Impact
The presented research advances predictive manufacturing by demonstrating how machine learning can enhance deposition quality and production reliability. Such approaches support industrial automation, minimize experimental iterations, and strengthen digital manufacturing workflows for large-scale metallic fabrication.
Award Suitability
The interdisciplinary nature of this research, together with its practical engineering applications and integration of artificial intelligence into manufacturing science, aligns with evaluation criteria commonly associated with international recognition for innovative scientific contributions and technological advancement.
Conclusion
Miroslav Petrov’s research demonstrates the value of combining computational intelligence with advanced manufacturing processes. His contribution supports more efficient fabrication strategies for complex metallic components while promoting innovation in predictive engineering and intelligent production technologies.
External Links
References
- Crossref. (2026). Geometrical Prediction of Copper-Coated Solid-Wire Deposition by Wire-Arc Additive Manufacturing Based on Artificial Neural Networks and Support Vector Machines.
https://doi.org/10.3390/metrology6010018