Trosolwg
Rajesh Ransing
Dr Rajesh Ransing
Associate Professor
Mechanical Engineering
303
Trydydd Llawr
Adeilad Gogleddol Peirianneg
Campws y Bae
Trydydd Llawr
Adeilad Gogleddol Peirianneg
Campws y Bae
Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig
Dolenni Ymchwil
Meysydd Arbenigedd
- Dadansoddeg Gweithgynhyrchu
- Optimeiddio prosesau a dysgu peirianyddol
- Meintioli Ansicrwydd
- Meddwl yn seiliedig ar risg, FMEAs a rheoli gwybodaeth sefydliadol
- Dadansoddiad cerddediad a cherdded deudroed
- Llif hylif ar raddfa nano
- Peirianneg Gyfrifiannol
Uchafbwyntiau Gyrfa
Ymchwil
Gwella prosesau ac optimeiddio prosesau gweithgynhyrchu
Meddwl yn seiliedig ar risg a rheoli gwybodaeth sefydliadol
Meintioli ansicrwydd mewn gweithrediadau gweithgynhyrchu
Modelu cyfrifiannol dadansoddiad cerddediad
Dylunio a gweithgynhyrchu dyluniadau prosthetig/orthotig arloesol
Cyhoeddiadau
Uchafbwyntiau Ymchwil
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Al-Ebrahim, M. & Ransing, R. (2026). A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets. Journal of Intelligent Manufacturing, 0
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Pe, P. & Ransing, R. (2025). Graph network simulators (GNS) for modelling particle-based fluid flow with a given inlet velocity. International Journal of Numerical Methods for Heat & Fluid Flow, 35(9), 3053-3079.
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Ransing, R. (2024). Guest editorial: Advancing nanofluid dynamics – a special issue synthesis. International Journal of Numerical Methods for Heat and Fluid Flow, 34(2), 381-382.
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Ransing, R., Brown, S., Roshan, H., & Ransing, M. (2017). How to save money in a foundry production with opportunities offered by Industry 4.0 and ISO9001:2015 Standard.. In Plenary Lecture, Jubilee 25th International Scientific and Technical Conference "Foundry production and metallurgy” (pp. 56-64). BNTU.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63492
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Batbooti, R. & Ransing, R. (2023). A novel imputation based predictive algorithm for reducing common cause variation from small and mixed datasets with missing values. Computers and Industrial Engineering, 179, 109230
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Ransing, R. (2022). Thermal irreversibility demystified. International Journal of Numerical Methods for Heat and Fluid Flow, 33(2)
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Mahmoodi, P., Ransing, R., & Friswell, M. (2016). A novel mathematical formulation for predicting symmetric passive bipedal walking motion with unbalanced masses. Applied Mathematical Modelling, 40(5-6), 3895-3906.
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Giannetti, C. & Ransing, R. (2016). Risk based uncertainty quantification to improve robustness of manufacturing operations. Computers & Industrial Engineering, 101, 70-80.
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Ransing, R., Batbooti, R., & Giannetti, C. (2016). A quality correlation algorithm for tolerance synthesis in manufacturing operations. Computers & Industrial Engineering, 93, 1-11.
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Mahmoodi, P., Aristodemou, S., Ransing, R., Owen, N., & Friswell, M. (2016). Prosthetic foot design optimisation based on roll-over shape and ground reaction force characteristics. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Holl Ymchwil
Erthyglau Cylchgronau
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Al-Ebrahim, M. & Ransing, R. (2026). A scalable random forest (SRF) approach for non-linear predictive modelling using small manufacturing datasets. Journal of Intelligent Manufacturing, 0
-
Pe, P. & Ransing, R. (2025). Graph network simulators (GNS) for modelling particle-based fluid flow with a given inlet velocity. International Journal of Numerical Methods for Heat & Fluid Flow, 35(9), 3053-3079.
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Ransing, R. (2024). Guest editorial: Advancing nanofluid dynamics – a special issue synthesis. International Journal of Numerical Methods for Heat and Fluid Flow, 34(2), 381-382.
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Batbooti, R. & Ransing, R. (2023). A novel imputation based predictive algorithm for reducing common cause variation from small and mixed datasets with missing values. Computers and Industrial Engineering, 179, 109230
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Ransing, R. (2022). Thermal irreversibility demystified. International Journal of Numerical Methods for Heat and Fluid Flow, 33(2)
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Mahmoodi, P., Aristodemou, S., Ransing, R., Owen, N., & Friswell, M. (2016). Prosthetic foot design optimisation based on roll-over shape and ground reaction force characteristics. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
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Batbooti, R., Ransing, R., Ransing, M., & Ransing, R. (2016). A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis. Computers & Industrial Engineering
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Hafezi, F., Ransing, R., Lewis, R., & Ransing, R. (2016). The calculation of drag on nano-cylinders. International Journal for Numerical Methods in Engineering, 111(11)
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Charles, W., Mahmoodi, P., Ransing, R., Sazonov, I., & Friswell, M. (2016). Comparison of point foot, collisional and smooth rolling contact models on the bifurcations and stability of bipedal walking. European Journal of Computational Mechanics, 25(3), 273-293.
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Giannetti, C. & Ransing, R. (2016). Risk based uncertainty quantification to improve robustness of manufacturing operations. Computers & Industrial Engineering, 101, 70-80.
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Ransing, R., Batbooti, R., & Giannetti, C. (2016). A quality correlation algorithm for tolerance synthesis in manufacturing operations. Computers & Industrial Engineering, 93, 1-11.
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Mahmoodi, P., Ransing, R., & Friswell, M. (2016). A novel mathematical formulation for predicting symmetric passive bipedal walking motion with unbalanced masses. Applied Mathematical Modelling, 40(5-6), 3895-3906.
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Giannetti, C., Ransing, M., Ransing, R., Bould, D., Gethin, D., & Sienz, J. (2015). Organisational Knowledge Management for Defect Reduction and Sustainable Development in Foundries. International Journal of Knowledge and Systems Science, 6(3), 18-37.
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Arjunwadkar, S., Ransing, M., & Ransing, R. (2015). Seven Steps to Energy Efficiency for Foundries. Foundry Management and Technology, 143(3), 24-29.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24998
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Giannetti, C., Ransing, R., Ransing, M., Bould, D., Gethin, D., & Sienz, J. (2014). A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data. Computers & Industrial Engineering, 72, 217-229.
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Ransing, R., Giannetti, C., Ransing, M., James, M., Ransing, R., & Giannetti, C. (2013). A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects. Computers in Industry, 64(5), 514-523.
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Mahmoodi, P., Ransing, R., Friswell, M., Friswell, M., & Ransing, R. (2013). Modelling the effect of ‘heel to toe’ roll-over contact on the walking dynamics of passive biped robots. Applied Mathematical Modelling
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Hafezi, F., Ransing, R., & Ransing, R. (2013). Computational modelling of fluid structure interaction at nano-scale boundaries with modified Maxwellian velocity distribution. Applied Mathematical Modelling
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Ransing, R. (2011). Enhancing undergraduate student skills to meet research challenges: Case Studies and Examples. The Journal of Engineering Education, XII special issue – Research in State Engineering Colleges in – A Road Map, XXV(1-2)SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24897
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Ransing, R. (2009). An innovative extrusion die layout design approach for single-hole dies. Journal of Materials Processing Technology
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R., S., M., P., & Ransing, R. (2006). Optimization in castings—An overview of relevant computational technologies and future challenges. Metallurgical and Materials Transactions B, 37(6), 905-911.
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Harris, A., Hargreaves, A., Southworth, G., & Ransing, R. (2005). Editorial. School Leadership & Management, 25(4), 317-319.
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Ransing, R., Pao, W., Lin, C., Sood, M., Lewis, R., & Ransing, R. (2005). Enhanced medial axis interpolation algorithm and its application to hotspot prediction in a mould–casting assembly. International Journal of Cast Metals Research, 18(1), 1-12.
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Postek, E., Lewis, R., Gethin, D., Ransing, R., & Ransing, R. (2005). Influence of initial stresses on the cast behaviour during squeeze forming processes. Journal of Materials Processing Technology, 159(3), 338-346.
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Ransing, R., Sood, M., Pao, W., & Ransing, R. (2005). Computer implementation of Heuvers' circle method for thermal optimisation in castings. , 119-126.
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Ransing, R., Savino, S., Lewis, R., & Ransing, R. (2005). Numerical optimisation of tilt casting process. , 109-118.
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Ransing, R. & Ransing, R. (2004). International Journal of Numerical Methods of Heat and Fluid Flow: Editorial. , 14SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24509
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Ransing, R., Lewis, R., & Gethin, D. (2004). Using a deformable discrete-element technique to model the compaction behaviour of mixed ductile and brittle particulate systems. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY OF LONDON SERIES A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 362(1822), 1867-1884.
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W.K.S, P., R.S, R., R.W, L., C, L., & Ransing, R. (2004). A medial-axes-based interpolation method for solidification simulation. Finite Elements in Analysis and Design, 40(5-6), 577-593.
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R.W., L., R.S., R., W.K.S., P., K., K., J., B., & Ransing, R. (2004). Alternative techniques for casting process simulation. International Journal of Numerical Methods for Heat & Fluid Flow, 14(2), 145-166.
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Gethin, D., Lewis, R., Ransing, R., & Ransing, R. (2003). A discrete deformable element approach for the compaction of powder systems. Modelling and Simulation in Materials Science and Engineering, 11(1), 101-114.
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Javier, B., Rajesh, R., & Ransing, R. (2003). 10th anniversary conference of ACME-UK. Communications in Numerical Methods in Engineering, 19(9), 657-657.
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Lewis, R., Ransing, M., Ransing, R., & Ransing, R. (2002). An approach for casting defect analysis employing finite element design optimisation, media axis transformation and neural networks. , 15SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24520
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Cox, I., Lewis, R., Ransing, R., Laszczewski, H., Berni, G., & Ransing, R. (2002). Application of neural computing in basic oxygen steelmaking. Journal of Materials Processing Technology, 120(1-3), 310-315.
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D.T, G., R.S, R., R.W, L., M, D., A.J.L, C., & Ransing, R. (2001). Numerical comparison of a deformable discrete element model and an equivalent continuum analysis for the compaction of ductile porous material. Computers & Structures, 79(13), 1287-1294.
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Lewis, R., Manzari, M., Ransing, R., Gethin, D., & Ransing, R. (2000). Casting shape optimization via process modelling. , 21SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24525
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R.W, L., M.T, M., R.S, R., D.T, G., & Ransing, R. (2000). Casting shape optimisation via process modelling. Materials & Design, 21(4), 381-386.
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R.S, R., D.T, G., A.R, K., P, M., R.W, L., & Ransing, R. (2000). Powder compaction modelling via the discrete and finite element method. Materials & Design, 21(4), 263-269.
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R.W., L., R.S., R., & Ransing, R. (2000). The optimal design of interfacial heat transfer coefficients via a thermal stress model. Finite Elements in Analysis and Design, 34(2), 193-209.
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Ransing, R., Lewis, R., Gethin, D., & Ransing, R. (1999). Lewis-Ransing correlation to optimally design the metal-mould heat transfer. , 9SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24528
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Ransing, R., Gethin, D., Lewis, R., Rowe, R., Roberts, R., & Ransing, R. (1999). Modelling and simulation of the compaction of pharmaceutical powders. , 51SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24527
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R.S., R., R.W., L., D.T., G., & Ransing, R. (1999). Lewis‐Ransing correlation to optimally design the metal‐mould heat transfer. International Journal of Numerical Methods for Heat & Fluid Flow, 9(3), 318-334.
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Lewis, R., Ransing, R., & Ransing, R. (1998). Correlation to describe interfacial heat transfer during solidification simulation and its use in the optimal feeding design of castings. , 29SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24532
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Ransing, R., Lewis, R., & Ransing, R. (1998). Recognising patterns in rejection data for diagnosis. , 10SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24530
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R., W., R., S., & Ransing, R. (1998). A correlation to describe interfacial heat transfer during solidification simulation and its use in the optimal feeding design of castings. Metallurgical and Materials Transactions B, 29(2), 437-448.
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Ransing, R., Lewis, R., & Ransing, R. (1997). Optimal design of die coating thickness using the Lewis-Ransing correlation. , 9SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24535
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R., S., R.W., L., & Ransing, R. (1997). A semantically constrained neural network for manufacturing diagnosis. International Journal of Production Research, 35(9), 2639-2660.
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R., W., R.S., R., & Ransing, R. (1997). A semantically constrained Bayesian network for manufacturing diagnosis. International Journal of Production Research, 35(8), 2171-2188.
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R., S., M., N., R., W., & Ransing, R. (1995). ICADA: Intelligent computer aided defect analysis for castings. Journal of Intelligent Manufacturing, 6(1), 29-40.
Llyfrau Awdurol
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Nawi, N., Ransing, R., Salleh, M., Ghazali, R., Hamid, N., & Ransing, R. (2010). Database Theory and Application, Bio-Science and Bio-Technology
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Dyson, P., Ransing, R., Williams, P., & Williams, P. (2008). Fluid Properties at Nano/Meso Scale Wiley.
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Ransing, R., Ransing, M., Lewis, R., & Ransing, R. (2003). On the limitations of neural network techniques for analysing cause and effect relationships in manufacturing processes - A case studySU Repository: https://cronfa.swan.ac.uk/Record/cronfa24515
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Ransing, R., Ransing, M., Lewis, R., & Ransing, R. (2003). The importance of an adaptive gain variation method in back-propagation neural network learning algorithmsSU Repository: https://cronfa.swan.ac.uk/Record/cronfa24513
Cyfraniadau Cynadleddau
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Ransing, R., Brown, S., Roshan, H., & Ransing, M. (2017). How to save money in a foundry production with opportunities offered by Industry 4.0 and ISO9001:2015 Standard.. In Plenary Lecture, Jubilee 25th International Scientific and Technical Conference "Foundry production and metallurgy” (pp. 56-64). BNTU.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63492
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Ransing, M. & Ransing, R. (2014). “If Only My Foundry Knew What it Knows ...”: A 7Epsilon Perspective on Root Cause Analysis and Corrective Action Plans for ISO9001:2008. In Proceedings of the 61nd Annual Technical Conference on Investment Casting, Covington, KY, USA, October 5-8, 2014SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24537
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Roshan , H., Giannetti, C., Ransing, M., & Ransing, R. (2014). “If only my foundry knew what it knows …”: A 7Epsilon perspective on root cause analysis and corrective action plans for ISO9001:2008. In World Foundry Congress 2014SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24479
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Paine, A., Ransing, R., Gethin, D., Sims, G., Richley, J., Boissevain, L., Lewis, M., & Ransing, R. (2013). Challenges faced by a small ship yard in integrating computer aided design and production processes - A real life case study.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24491
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Nazri, M., Rajesh, S., Meghana, R., & Ransing, R. (2008). A New Method to Improve the Gradient Based Search Direction to Enhance the Computational Efficiency of Back Propagation Based Neural Network Algorithms. In 2008 Second Asia International Conference on Modelling & Simulation (AMS)IEEE.
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Nawi, N., Ransing, M., Ransing, R., & Ransing, R. (2007). Improving the gradient based search direction to enhance training efficiency of back propagation based neural network algorithms.
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Nazri, N., Meghana, R., Rajesh, R., & Ransing, R. (2006). An Improved Learning Algorithm Based on The Broyden-Fletcher-Goldfarb-Shanno (BFGS) Method For Back Propagation Neural Networks. In Sixth International Conference on Intelligent Systems Design and ApplicationsIEEE.
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Ransing, R., Sood, M., & Ransing, R. (2005). Review of optimization methods for casting simulation.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24503
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Ahmad, R., Gethin, D., Lewis, R., Ransing, R., Postek, E., & Ransing, R. (2005). Thermal optimisation of the squeeze-forming process using genetic algorithms.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24502
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Lewis, R., Postek, E., Ransing, R., Han, Z., Gethin, D., & Ransing, R. (2004). A finite element model of the squeeze casting process.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24511
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Malan, A., Lewis, R., Ransing, R., & Ransing, R. (2003). Modeling Coupled Heat and Mass Transfer in Drying Capillary Hygroscopic and Non-hygroscopic Porous Materials.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24516
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Ransing, R., Pao, W., Lewis, R., & Ransing, R. (2003). Solidification and thermal stress simulation using medial objects.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24514
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X., S., R., W., D., T., R., S., R., C., & Ransing, R. (2002). Discrete-Finite Element Modelling of Pharmaceutical Powder Compaction: A Two-Stage Contact Detection Algorithm for Non-Spherical Particles. In Discrete Element MethodsAmerican Society of Civil Engineers.
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Lewis, R., Cross, J., Gethin, D., Ransing, R., Manzari, M., Rahman, N., & Ransing, R. (1998). Recent developments in heat transfer and porous media.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa24531
Cynnyrch Ymchwil Arall
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Ransing, R. & Ransing, R. (2010). Back to basics: Process optimisation using in-process dataSU Repository: https://cronfa.swan.ac.uk/Record/cronfa24493
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Ransing, R. & Ransing, R. (2010). Challenges in investment casting process controlSU Repository: https://cronfa.swan.ac.uk/Record/cronfa24492
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Nazri, M., Meghana, R., Rajesh, S., & Ransing, R. (2007). Research and Development in Intelligent Systems XXIII
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Ransing, R., Ransing, M., & Ransing, R. (2007). Innovations in machine learning and defect diagnosticsSU Repository: https://cronfa.swan.ac.uk/Record/cronfa24496
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Ransing, R. & Ransing, R. (2006). Software solutions: Recall with X1Recall.comSU Repository: https://cronfa.swan.ac.uk/Record/cronfa24500
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Ransing, R. & Ransing, R. (2005). 'Thinking' software set to revolutionise defect analysisSU Repository: https://cronfa.swan.ac.uk/Record/cronfa24506
Goruchwyliaeth
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Providing Alternative Energy using Advanced Cryogenic Insulation (cyfredol)
PhDGoruchwyliwr arall: Dr Michael Clee -
A diagnostic framework for graph-based systems under distribution shift: locality, consistency, and stability (cyfredol)
PhDGoruchwyliwr arall: Prof Mike Jennings -
Evolution of computational tools and experimental methods for the development of passive prosthetic feet. (dyfarnwyd 2022)
EngDGoruchwyliwr arall: Dr Nick Owen -
An investigation into the sterilisation capabilities of biopolymers and the introduction of an organisational knowledge management methodology for the medical device industry. (dyfarnwyd 2022)
MScGoruchwyliwr arall: Dr Will Harrison
Modiwlau a Addysgir
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EG-2015 Mechanical Engineering Practice 1
Each of the four hands-on experiments is self-contained. Students keep an individual logbook of experimental observations and results across all four experiments, assessed pass/fail for completeness. One experiment is selected at random for a full technical report; which experiment is not confirmed until the end of Teaching Week 8 of Teaching Block 2, so students must maintain a consistently high standard of logbook record-keeping across all four. Students write their own first draft of the report and may then use AI tools to enhance it, in line with the module's declared AI-use policy. The report is due in Teaching Week 9 of Teaching Block 2, the week teaching resumes after Easter. A short class test of reflective questions, sat under invigilated conditions in Teaching Week 10 of Teaching Block 2, checks the student's understanding of their own report and of any AI-assisted changes made to it; the class test result is used to scale the report mark
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EG-3102 Applied Mechanical Engineering Principles
This module develops the ability to analyse and solve multi-domain engineering problems using a systems approach. Students build and evaluate simplified models of mechanical, fluid and electromechanical systems to support engineering decisions. Emphasis is on identifying system interactions, estimating performance, analysing sensitivity, and assessing trade-offs under constraints. The module links experimental data, simulation outputs and engineering judgement to produce clear, defensible conclusions.
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EG-M364 Advanced Manufacturing Systems
Addressing global challenges, from clean energy to sustainable transport, requires engineers with essential skills for the future employment market. High-value sectors like nuclear, aerospace, and automotive, demand a new generation of technical leaders who understand the full product lifecycle of a manufactured component from material design, through processing, to in-service performance and reliability. This module provides a system-level perspective, following a component from material design to in-service reliability, developing the capability to critically assess the most suitable advanced manufacturing route. Students will learn to use fundamental metallurgical tools, such as composition phase diagrams and temperature-transformation diagrams, to help predict the influence of time and temperature on the resulting microstructure. This is fundamental to tailoring a material's final mechanical properties for a specific application. The curriculum focuses primarily on metal Additive Manufacturing (AM), exploring process physics, defect formation, and Design for AM (DfAM). This is contextualised against other manufacturing routes of critical components, including single crystal casting and net-shape HIP for nuclear components. This will be put in the context of in-service performance using industrial reliability and quality frameworks (Six Sigma, FMEA, RCM, SPC), to design, manage, and assure the complex, high-integrity systems of tomorrow.