Kandice Barros Ribeiro
University Lecturer
ksbari@utu.fi +358 29 450 3406 +358 50 308 1780 Työhuone: B6016 ORCID-tunniste: https://orcid.org/0000-0001-7005-8783 |
Advanced manufacturing processes, metal additive manufacturing, process monitoring, artificial intelligence.
Dr. Ribeiro specializes in process monitoring and data analysis, with expertise in metal additive manufacturing, laser processes and hybrid manufacturing (additive + subtractive). Her strong background and passion for manufacturing drive her pursuit of more sustainable solutions in advanced processes such as laser Directed Energy Deposition (DED-LB). Dr. Ribeiro is continually focused on pushing the frontiers of science in the field of Smart Manufacturing, with an emphasis on sustainable engineering practices.
Research interests are within the areas of:
- Advanced manufacturing processes
- Metal additive manufacturing
- Process monitoring
- Signal processing
- Artificial intelligence
Current teaching responsibilities regards the courses:
- KTEK0054 Robotics
- KTEK0053 Mechatronics
- KTEK0011 Digital Factory
- KTEK0065 Hands-on Smart Systems
- Assessment of laser weld bead quality via acoustic emission and optical coherence tomography (2025)
- IOP Conference Series: Materials Science and Engineering
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Evaluation of Acoustic Emission as a Predictor of Laser Power in Laser Welding (2025)
- IOP Conference Series: Materials Science and Engineering
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Acoustic emission of laser welding on structural steels for quality assurance (2024) Hsu, Li-Wei; Barros Ribeiro, Kandice Suane; Parchegani Chozaki, Saeid; Libutti Núñez, Henrique Hiram; Moreira Bessa, Wallace; Salminen, Antti
(O2 Muu julkaisu ) - An analytical model for estimating process parameters input in L-DED based on bead geometry (2024)
- Manufacturing letters
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Evaluation of different surface topography measurement methodologies for characterizing weld beads in shipbuilding (2024)
- Procedia CIRP
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Hybrid FE-ML model for turning of 42CrMo4 steel (2024)
- CIRP Journal of Manufacturing Science and Technology
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - (2024)
- Procedia CIRP
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Abrasive and non-conventional post-processing techniques to improve surface finish of additively manufactured metals: a review (2023
- Progress in Additive Manufacturing
(A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä) - A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition2023
- International Journal of Advanced Manufacturing Technology
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Real-time prediction of deposited bead width in L-DED using semi-supervised transfer learning (2023)
- International Journal of Advanced Manufacturing Technology
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - In-process chatter detection in micro-milling using acoustic emission via machine learning classifiers2022
- International Journal of Advanced Manufacturing Technology
- Micro-machining of additively manufactured metals: a review2022
- International Journal of Advanced Manufacturing Technology