| A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing M Papananias, TE McLeay, M Mahfouf, V Kadirkamanathan computers in industry 105, 35-47, 2019 | 36 | 2019 |
| Inspection by exception: A new machine learning-based approach for multistage manufacturing M Papananias, TE McLeay, O Obajemu, M Mahfouf, V Kadirkamanathan Applied Soft Computing 97, 106787, 2020 | 21 | 2020 |
| An intelligent metrology informatics system based on neural networks for multistage manufacturing processes M Papananias, TE McLeay, M Mahfouf, V Kadirkamanathan Procedia CIRP 82, 444-449, 2019 | 18 | 2019 |
| Uncertainty evaluation associated with versatile automated gauging influenced by process variations through design of experiments approach M Papananias, S Fletcher, AP Longstaff, AB Forbes Precision Engineering 49, 440-455, 2017 | 15 | 2017 |
| Developments in automated flexible gauging and the uncertainty associated with comparative coordinate measurement AB Forbes, M Papananias, AP Longstaff, S Fletcher, A Mengot, K Jonas European society for precision engineering and nanotechnology, 2016 | 13 | 2016 |
| A Bayesian information fusion approach for end product quality estimation using machine learning and on-machine probing M Papananias, TE McLeay, M Mahfouf, V Kadirkamanathan Journal of Manufacturing Processes 76, 475-485, 2022 | 9 | 2022 |
| Modelling uncertainty associated with comparative coordinate measurement through analysis of variance techniques M Papananias, S Fletcher, AP Longstaff, A Mengot, K Jonas, AB Forbes euspen, 2017 | 8 | 2017 |
| Development of a new machine learning-based informatics system for product health monitoring M Papananias, O Obajemu, TE McLeay, M Mahfouf, V Kadirkamanathan Procedia CIRP 93, 473-478, 2020 | 5 | 2020 |
| A novel method based on Bayesian regularized artificial neural networks for measurement uncertainty evaluation M Papananias, S Fletcher, AP Longstaff, A Mengot european society for precision engineering and nanotechnology, 2016 | 5 | 2016 |
| A probabilistic framework for product health monitoring in multistage manufacturing using unsupervised artificial neural networks and Gaussian processes M Papananias, TE McLeay, M Mahfouf, V Kadirkamanathan Proceedings of the Institution of Mechanical Engineers, Part B: Journal of …, 2022 | 4 | 2022 |
| An interpretable machine learning based approach for process to areal surface metrology informatics O Obajemu, M Mahfouf, M Papananias, TE McLeay, V Kadirkamanathan Surface Topography: Metrology and Properties 9 (4), 044001, 2021 | 4 | 2021 |
| Evaluation of automated flexible gauge performance using experimental designs M Papananias, S Fletcher, AP Longstaff, A Mengot, K Jonas, AB Forbes euspen, 2017 | 4 | 2017 |
| Combined numerical and statistical modelling for in-depth uncertainty evaluation of comparative coordinate measurement M Papananias University of Huddersfield, 2018 | 3 | 2018 |
| Improving the dynamic performance of five-axis CNC machine tool by using the software-in-the-loop (SIL) platform S Sztendel, M Papananias, C Pislaru Euspen, 2015 | 3 | 2015 |
| Development of a novel multibody mechatronic model for five-axis CNC machine tool M Papananias, S Sztendel, C Pislaru EUSPEN, 2015 | 3 | 2015 |
| Right-first-time manufacture of sustainable composite laminates using statistical and machine learning modelling A Barouni, M Papananias, K Giasin, A Saifullah, ZY Zhang, C Lupton, ... 10th ECCOMAS Thematic Conference on Smart Structures and Materials, 911-922, 2023 | | 2023 |
Visakan KadirkamanathanProfessor of Signal and Information Processing, University of SheffieldVerified email at sheffield.ac.uk
