Thomas E. McLeay

422 total citations
19 papers, 319 citations indexed

About

Thomas E. McLeay is a scholar working on Mechanical Engineering, Industrial and Manufacturing Engineering and Control and Systems Engineering. According to data from OpenAlex, Thomas E. McLeay has authored 19 papers receiving a total of 319 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Mechanical Engineering, 12 papers in Industrial and Manufacturing Engineering and 4 papers in Control and Systems Engineering. Recurrent topics in Thomas E. McLeay's work include Advanced machining processes and optimization (12 papers), Manufacturing Process and Optimization (11 papers) and Industrial Vision Systems and Defect Detection (5 papers). Thomas E. McLeay is often cited by papers focused on Advanced machining processes and optimization (12 papers), Manufacturing Process and Optimization (11 papers) and Industrial Vision Systems and Defect Detection (5 papers). Thomas E. McLeay collaborates with scholars based in United Kingdom, Sweden and United States. Thomas E. McLeay's co-authors include Visakan Kadirkamanathan, Sam Turner, Tony L. Schmitz, Jaydeep Karandikar, Mahdi Mahfouf, Tom Slatter, Kostas Triantafyllopoulos, Eleanor Stillman, Keith Worden and Timothy J. Rogers and has published in prestigious journals such as Mechanical Systems and Signal Processing, Applied Soft Computing and The International Journal of Advanced Manufacturing Technology.

In The Last Decade

Thomas E. McLeay

18 papers receiving 301 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Thomas E. McLeay United Kingdom 10 218 145 102 55 43 19 319
Gengxiang Chen China 10 227 1.0× 109 0.8× 78 0.8× 78 1.4× 46 1.1× 22 336
Joanna Kossakowska Poland 11 311 1.4× 163 1.1× 154 1.5× 109 2.0× 36 0.8× 22 390
Johan Persson Sweden 7 130 0.6× 121 0.8× 26 0.3× 41 0.7× 81 1.9× 28 309
Norfadzlan Yusup Malaysia 4 245 1.1× 116 0.8× 179 1.8× 96 1.7× 22 0.5× 13 370
S. Ravikumar India 8 173 0.8× 110 0.8× 65 0.6× 21 0.4× 96 2.2× 15 298
Jamel Louati Tunisia 10 253 1.2× 95 0.7× 21 0.2× 22 0.4× 78 1.8× 34 336
Yalcin Ertekin United States 7 440 2.0× 150 1.0× 53 0.5× 100 1.8× 26 0.6× 29 496
Giulio Mattera Italy 11 193 0.9× 147 1.0× 24 0.2× 13 0.2× 34 0.8× 27 300
Baobao Qi China 8 247 1.1× 56 0.4× 29 0.3× 28 0.5× 121 2.8× 36 349
Asha Viswanath United Arab Emirates 10 119 0.5× 24 0.2× 46 0.5× 16 0.3× 20 0.5× 17 272

Countries citing papers authored by Thomas E. McLeay

Since Specialization
Citations

This map shows the geographic impact of Thomas E. McLeay's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Thomas E. McLeay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas E. McLeay more than expected).

Fields of papers citing papers by Thomas E. McLeay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas E. McLeay. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Thomas E. McLeay. The network helps show where Thomas E. McLeay may publish in the future.

Co-authorship network of co-authors of Thomas E. McLeay

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas E. McLeay. A scholar is included among the top collaborators of Thomas E. McLeay based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thomas E. McLeay. Thomas E. McLeay is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Rogers, Timothy J., et al.. (2022). Online damage detection of cutting tools using Dirichlet process mixture models. Mechanical Systems and Signal Processing. 180. 109434–109434. 14 indexed citations
2.
McLeay, Thomas E., et al.. (2022). A two-step machining and active learning approach for right-first-time robotic countersinking through in-process error compensation and prediction of depth of cuts. Robotics and Computer-Integrated Manufacturing. 77. 102345–102345. 14 indexed citations
3.
McLeay, Thomas E., et al.. (2022). A Bayesian information fusion approach for end product quality estimation using machine learning and on-machine probing. Journal of Manufacturing Processes. 76. 475–485. 10 indexed citations
4.
McLeay, Thomas E., et al.. (2022). A probabilistic framework for product health monitoring in multistage manufacturing using Unsupervised Artificial Neural Networks and Gaussian Processes. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 237(9). 1295–1310. 6 indexed citations
5.
Mahfouf, Mahdi, et al.. (2021). An interpretable machine learning based approach for process to areal surface metrology informatics. Surface Topography Metrology and Properties. 9(4). 44001–44001. 3 indexed citations
6.
McLeay, Thomas E., et al.. (2020). Development of a New Machine Learning-based Informatics System for Product Health Monitoring. Procedia CIRP. 93. 473–478. 3 indexed citations
7.
McLeay, Thomas E., Michael S. Turner, & Keith Worden. (2020). A novel approach to machining process fault detection using unsupervised learning. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 235(10). 1533–1542. 9 indexed citations
8.
McLeay, Thomas E., et al.. (2020). Inspection by exception: A new machine learning-based approach for multistage manufacturing. Applied Soft Computing. 97. 106787–106787. 22 indexed citations
9.
McLeay, Thomas E., et al.. (2019). An Intelligent Metrology Informatics System based on Neural Networks for Multistage Manufacturing Processes. Procedia CIRP. 82. 444–449. 14 indexed citations
10.
Bull, Lawrence A., Keith Worden, Timothy J. Rogers, et al.. (2019). A probabilistic framework for online structural health monitoring: active learning from machining data streams. Journal of Physics Conference Series. 1264(1). 12028–12028. 4 indexed citations
11.
Ayvar-Soberanis, Sabino, et al.. (2019). Machining Distortion in Asymmetrical Residual Stress Profiles. Procedia CIRP. 82. 395–399. 6 indexed citations
12.
Mahfouf, Mahdi, et al.. (2019). A new Gaussian process-based approach for uncertainty propagation in surface metrology profile parameters estimation. Huddersfield Research Portal (University of Huddersfield). 520–523. 1 indexed citations
13.
Öztürk, Erdem, et al.. (2019). Work holding assessment of an UV adhesive and fixture design method. The International Journal of Advanced Manufacturing Technology. 106(1-2). 741–752. 5 indexed citations
14.
McLeay, Thomas E., et al.. (2019). A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing. Computers in Industry. 105. 35–47. 33 indexed citations
15.
Triantafyllopoulos, Kostas, et al.. (2016). A Multivariate Control Chart for Autocorrelated Tool Wear Processes. Quality and Reliability Engineering International. 32(6). 2093–2106. 20 indexed citations
16.
McLeay, Thomas E., et al.. (2016). Using spindle noise to monitor tool wear in a turning process. The International Journal of Advanced Manufacturing Technology. 86(9-12). 2781–2790. 55 indexed citations
17.
Karandikar, Jaydeep, Thomas E. McLeay, Sam Turner, & Tony L. Schmitz. (2014). Tool wear monitoring using naïve Bayes classifiers. The International Journal of Advanced Manufacturing Technology. 77(9-12). 1613–1626. 95 indexed citations
18.
Karandikar, Jaydeep, Thomas E. McLeay, Sam Turner, & Tony L. Schmitz. (2013). Remaining Useful Tool Life Predictions Using Bayesian Inference. 5 indexed citations
19.
McLeay, Thomas E. & Michael S. Turner. (2011). Failure mode analysis to define process monitoring systems. Journal of Machine Engineering.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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