Materials Processing Feedback & Control Publications

• C. M. Ryan, A. Parnell, C. Mahoney, Real-Time Anomaly Detection for Univariate Time Series Data with Trend and Seasonal Effects, IEEE Access. (submitted).
• J. Tobin, M. Zhang, Scalable and adaptable density-based clustering using level set and mode-seeking methods, Pattern Recognition, 2022. (submitted).
• A. George, B. Eviston, D. Mourtzis, N. Papakostas, Modelling the geometrical complexity of parts for configuring additive manufacturing processes, CIRP Annals - Manufacturing Technology. (submitted).
• M. Zhang, A. Parnell, D. Brabazon, A. Benavoli, Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing, Journal of Intelligent Manufacturing, 2022. (submitted).
• M. Badiane, P. Cunningham, An empirical evaluation of kernels for time series, Artificial Intelligence Review, 55, 2022.
• M. Zhang, M. Revie, J. Quigley, Saddlepoint approximation for the generalized inverse Gaussian Levy process. Journal of Computational and Applied Mathematics, 2022.
• M. A. Obeidi, M. Monu, C. Hughes, D. Bourke, M.N. Dogu, J. Francis, M. Zhang, I.U. Ahad, D. Brabazon, Laser beam powder bed fusion of nitinol shape memory alloy (SMA). Journal of Materials Research and Technology, 14, 2021.
• E. Prado, E. O’Neill, B. Hernandez, A.C. Parnell, R.A. Moral, Discussion of: "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects, Bayesian Analysis, 5, 2021.
• Y. Tu, Z. Liu, L. Carneiro, C.M. Ryan, A.C. Parnell, S.B. Leen, N.M. Harrison, Towards an Instant Structure-Property Prediction Quality Control Tool for Additive Manufactured Steel using a Crystal Plasticity Trained Deep Learning Surrogate. Materials & Design, 213, 110345, 2021.
• M. Ali, A. George, N. Papakostas, Utilizing robotic process automation technology for streamlining the additive manufacturing design workflow, CIRP Annals - Manufacturing Technology, 70, 2021
• D. S. Egan, C.M. Ryan, A.C. Parnell, D.P. Dowling, Using in-situ process monitoring data to identify defective layers in Ti-6Al-4V additively manufactured porous biomaterials, Journal of Manufacturing Processes, 64, 2021.
• A. C. Doyle, D.S. Egan, C.M. Ryan, A.C. Parnell, D.P. Dowling, Application of the STRAY statistical learning algorithm for the evaluation of in-situ process monitoring data during L-PBF additive manufacturing. Procedia Manufacturing, 54, 2021.
• E. B. Prado, R.A. Moral, A.C. Parnell, Bayesian additive regression trees with model trees, Statistics and Computing, 31, 2021.
• X. Liu, A. Mileo, A Deep Learning Approach to Defect Detection in Additive Manufacturing of Titanium Alloys. 2021 International Solid Freeform Fabrication Symposium. University of Texas at Austin, 2021. 
• M. Kariminejad, D. Tormey, S. Huq, J. Morrison and M. McAfee, Comparison of Intelligent Approaches for Cycle Time Prediction in Injection Moulding of a Medical Device Product, 2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI), 2021.
• N. Papakostas, A. Newell, A. George, An Agent-Based Decision Support Platform for Additive Manufacturing Applications, Applied Sciences, 10, 2020.
• R. Lyons, A. Newell, P. Ghadimi, N. Papakostas, Environmental impacts of conventional and additive manufacturing for the production of Ti-6Al-4V knee implant: a life cycle approach, International Journal of Advanced Manufacturing Technology, 112, 2020.
• V. Mahato, M.A. Obeidi, D. Brabazon, P. Cunningham, Detecting voids in 3D printing using melt pool time series data, Journal of Intelligent Manufacturing, 33, 2020.
• V. Mahato, M.A. Obeidi, D. Brabazon, P. Cunningham, An evaluation of classification methods for 3d printing time-series data, IFAC PapersOnLine, 53, 2020.
• A. Newell, A. George, N. Papakostas, H. Lhachemi, A. Malik, R. Shorten, R., On design for additive manufacturing: review of challenges and opportunities utilising visualisation technologies. 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1-7, 2019.
• A. George, A. Newell, N. Papakostas, Intellectual Property Protection and Security in Additive Manufacturing, Advances in Manufacturing Technology XXXIII, vol. 9, 2019,
• A. Newell, A. George, N. Papakostas, Product Lifecycle Management Strategies Focusing on Additive Manufacturing Workflow, Advances in Manufacturing Technology XXXIII, 9, 2019.
• N. Papakostas, V. Hargaden, S. Schukraft, M. Freitag, An Approach to Designing Supply Chain Networks Considering the Occurrence of Disruptive Events, 9th IFAC Conference MIM 2019, Procedia IFAC-PapersOnLine, December 1, 2019.
• H. Yuanzhi, E. Ahearne, S. Baron, A. Parnell, An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning Process, International Journal of Advanced Manufacturing Technology, 2019.
• P. Quinn, S. O’Halloran, C. Ryan, A. Parnell, J. Lawlor, R. Raghavendra, Development of a Standalone In-situ Monitoring System for Defect Detection in the Direct Metal Laser Sintering Process, 2019 Solid Freeform Fabrication Symposium Proceedings, Texas August 2019.
• H. Sohrabpoor, R.T. Mousavian, M. Obeidi, I. Ul Ahad, D. Brabazon, Improving precision in the prediction of laser texturing and surface interference of 316L assessed by neural network and adaptive neuro-fuzzy inference models, Int. J. of Advanced Manufacturing Technology, 104, 2019.

Book and Book Chapter

• N. Papakostas, C. Constantinescu, D. Mourtzis, Novel Industry 4.0 Technologies and Applications, (Switzerland: MDPI, 2020)
• R. Shorten, J. Oliver, D. Clayton, A. Malik, H. Lhachemi, Industry 4.0 and The Sharing Economy, in Analytics for the Sharing Economy: Mathematics, Engineering and Business Perspectives, eds. Crisostomi et al. , (Switzerland: Springer, 2020).