| Prediction of welding residual stresses using machine learning: Comparison between neural networks and neuro-fuzzy systems J Mathew, J Griffin, M Alamaniotis, S Kanarachos, ME Fitzpatrick Applied Soft Computing 70, 131-146, 2018 | 84 | 2018 |
| Instantaneous vehicle fuel consumption estimation using smartphones and recurrent neural networks S Kanarachos, J Mathew, ME Fitzpatrick Expert Systems with Applications 120, 436-447, 2019 | 68 | 2019 |
| Prediction of residual stresses in girth welded pipes using an artificial neural network approach J Mathew, RJ Moat, S Paddea, ME Fitzpatrick, PJ Bouchard International Journal of Pressure Vessels and Piping 150, 89-95, 2017 | 39 | 2017 |
| Anomaly detection in time series data using a combination of wavelets, neural networks and Hilbert transform S Kanarachos, J Mathew, A Chroneos, M Fitzpatrick 2015 6th International Conference on Information, Intelligence, Systems and …, 2015 | 31 | 2015 |
| Reactor pressure vessel embrittlement: Insights from neural network modelling J Mathew, D Parfitt, K Wilford, N Riddle, M Alamaniotis, A Chroneos, ... Journal of Nuclear Materials 502, 311-322, 2018 | 29 | 2018 |
| Through-thickness residual stress profiles in austenitic stainless steel welds: A combined experimental and prediction study J Mathew, RJ Moat, S Paddea, JA Francis, ME Fitzpatrick, PJ Bouchard Metallurgical and Materials Transactions A 48, 6178-6191, 2017 | 22 | 2017 |
| Validated prediction of weld residual stresses in austenitic steel pipe girth welds before and after thermal ageing, part 1: Mock-up manufacture, residual stress measurements … MC Smith, O Muránsky, Q Xiong, PJ Bouchard, J Mathew, C Austin International Journal of Pressure Vessels and Piping 172, 233-250, 2019 | 19 | 2019 |
| Machine learning-based prediction and optimisation system for laser shock peening J Mathew, R Kshirsagar, S Zabeen, N Smyth, S Kanarachos, K Langer, ... Applied Sciences 11 (7), 2888, 2021 | 17 | 2021 |
| Probabilistic kernel machines for predictive monitoring of weld residual stress in energy systems M Alamaniotis, J Mathew, A Chroneos, ME Fitzpatrick, LH Tsoukalas Engineering Applications of Artificial Intelligence 71, 138-154, 2018 | 13 | 2018 |
| Analysis of surface roughness influence in non-destructive magnetic measurements applied to reactor pressure vessel steels G Vértesy, A Gasparics, JM Griffin, J Mathew, ME Fitzpatrick, ... Applied Sciences 10 (24), 8938, 2020 | 11 | 2020 |
| Magnetic Barkhausen Noise Method for Characterisation of Low Alloy Steel G Kadavath, J Mathew, J Griffin, D Parfitt, ME Fitzpatrick Pressure Vessels and Piping Conference 58967, V005T10A007, 2019 | 6 | 2019 |
| Validated prediction of weld residual stresses in austenitic steel pipe girth welds before and after thermal ageing, part 2: Modelling and validation Q Xiong, MC Smith, O Muransky, J Mathew International Journal of Pressure Vessels and Piping 172, 430-448, 2019 | 6 | 2019 |
| Modelling and measuring residual stresses in pipe girth welds: lessons from the Style Framework 7 project MC Smith, O Muransky, D Smith, SC Do, PJ Bouchard, J Mathew Pressure Vessels and Piping Conference 46049, V06BT06A075, 2014 | 6 | 2014 |
| Vacuum plasma etching of 1 wt% La2O3 dispersed tungsten J Mathew, RM Mohanty, R Sundaresan, V Sivan, K Balasubramanian Fusion engineering and design 85 (5), 824-827, 2010 | 6 | 2010 |
| Weld Residual Stress Profiles for Structural Integrity Assessment J Mathew PQDT-UK & Ireland, 2015 | 5 | 2015 |
| Prediction of pipe girth weld residual stress profiles using artificial neural networks J Mathew, RJ Moat, PJ Bouchard Pressure Vessels and Piping Conference 55713, V06BT06A075, 2013 | 4 | 2013 |
| Prediction of welding-induced residual stresses using a neural network approach J Mathew Welding and Cutting 15 (4), 232 - 233, 2016 | 3 | 2016 |
| A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data J Mathew, R Kshirsagar, DZ Abidin, J Griffin, S Kanarachos, J James, ... Scientific Reports 13 (9948), 2023 | 2 | 2023 |
| Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) Steel JM Griffin, J Mathew, A Gasparics, G Vértesy, I Uytdenhouwen, ... Applied Sciences 12 (8), 3721, 2022 | 2 | 2022 |
| Optimised neural network prediction of residual stress profiles for structural integrity assessment of pipe girth welds J Mathew, RJ Moat, PJ Bouchard Pressure Vessels and Piping Conference 46025, V005T11A028, 2014 | 2 | 2014 |
P John BouchardProfessor of Materials for Energy, The Open UniversityVerified email at open.ac.uk
