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In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
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Azzi, M., Bitar-Nehme, E., & Sapieha, J.-E. (2014, November). Design of new multifunctional galling-corrosion testing apparatus [Paper]. ASME International Mechanical Engineering Congress & Exposition (IMECE 2014), Montréal, Québec (7 pages). External link
Bitar-Nehme, E., & Mayer, J. R. R. (2018). Modelling and compensation of dominant thermally induced geometric errors using rotary axes power consumption. CIRP Annals, 67(1), 547-550. External link
Bitar-Nehme, E. (2017). Modélisation des erreurs thermiques des machines-outils numériques à cinq-axes [Ph.D. thesis, École Polytechnique de Montréal]. Available
Bitar-Nehme, E., & Mayer, J. R. R. (2016). Thermal volumetric effects under axes cycling using an invar R-test device and reference length. International Journal of Machine Tools and Manufacture, 105, 14-22. External link
Desgagnes, L., Tangestani, R., Miao, H., Natarajan, A., Rudloff, R., Pendurti, S., Bitar-Nehme, E., & Martin, É. (2025, March). A Layer-By-Layer FEM Curing Model for Binder Jetting of 316L [Paper]. 154th Annual Meeting & Exhibition of The Minerals, Metals & Materials Society (TMS 2025), Las Vegas, NV, USA. Published in The minerals, metals & materials series. External link
Khazaee, S., Bitar-Nehme, E., Boukhili, R., Kostenov, J., Regnaud, W., & Martin, É. (2025). A Low-Viscosity, Recyclable Polymer-Based Binder Strategy for Metal FDM: Toward High Powder Loading, Sustainable Processing, and Comprehensive Characterization of 17-4PH Stainless Steel Parts. Polymers, 17(19), 2575. External link
Khazaee, S., Bitar-Nehme, E., Boukhili, R., Kostenov, J., Regnaud, W., & Martin, É. (2025, March). Development of an Environmentally Friendly and Low-Cost Binder for 17-4PH Metal Part Printing via Fused Deposition Modeling [Paper]. 154th Annual Meeting & Exhibition of The Minerals, Metals & Materials Society (TMS 2025), Las Vegas, NV, USA. Published in The minerals, metals & materials series. External link
Moradi, M., Arunkumar, S., Azzi, M., Bitar-Nehme, E., Natarajan, A., Khazaee, S., & Martin, É. (2026). Binder Jetting of Co-Cr-Mo Alloys for Biomedical Applications: A Tribocorrosion Perspective. Key Engineering Materials, 1038, 31-36. External link
Moradi, M., Karimialavijeh, H., Bitar-Nehme, E., & Martin, É. (2025, March). Printability and Green Mechanical Properties of Binder Jet Additive Manufactured Co–Cr–Mo Parts [Paper]. 154th Annual Meeting & Exhibition of The Minerals, Metals & Materials Society (TMS 2025), Las Vegas, NV, USA. Published in The minerals, metals & materials series. External link
Ngoc, H. V., Mayer, J. R. R., & Bitar-Nehme, E. (2023). Deep Learning to Directly Predict Compensation Values of Thermally Induced Volumetric Errors. Machines, 11(4), 16 pages. External link
Ngoc, H. V., Mayer, J. R. R., & Bitar-Nehme, E. (2022). Deep learning LSTM for predicting thermally induced geometric errors using rotary axes powers as input parameters. CIRP Journal of Manufacturing Science and Technology, 37, 70-80. External link
Rimpault, X., Bitar-Nehme, E., Balazinski, M., & Mayer, J. R. R. (2018). Online monitoring and failure detection of capacitive displacement sensor in a Capball device using fractal analysis. Measurement: Journal of the International Measurement Confederation, 118, 23-28. External link
Sadallah, A., Miao, H., Changeux, B., Bitar-Nehme, E., Chakraborty, A., Turenne, S., & Martin, É. (2024, March). Effect of Shot Peening and High-Temperature Shot Peening on the High Cycle Fatigue of 7010-T7452 Aluminum Alloy [Paper]. Light Metals 2024 (TMS 2024), Orlando, FL, USA. External link
Zeng, M., Feng, M., Mayer, J. R. R., Bitar-Nehme, E., & Duong, X. T. (2025). Machine learning models for predicting volumetric errors based on scale and master balls artefact probing data. CIRP Journal of Manufacturing Science and Technology, 59, 135-157. Available
Zeng, M., Mayer, J. R. R., Feng, M., Bitar-Nehme, E., & Truong Duong, X. (2025). Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints. Journal of Machine Engineering, 25, 5-19. External link