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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
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.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Jadayel, M., & Khameneifar, F. (2025). Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations. Machines, 13(5), 382-382. External link
Jadayel, M. (2024). Improving Geometric Accuracy of Fused Filament Fabrication (FFF) 3D Printed Parts by Computational Analysis of Digitized Sacrificial Parts [Ph.D. thesis, Polytechnique Montréal]. Available
Jadayel, M., & Khameneifar, F. (2023, May). A novel setup for in-process geometric inspection of 3D printed parts via structured-light 3D scanning [Paper]. International Congress of the Canadian Society of Mechanical Engineering (CSME 2023), Sherbrooke, QC, Canada. External link
Jadayel, M., & Khameneifar, F. (2023, July). Structured-light 3D scanning performance in offline and in-process measurement of 3D printed parts [Paper]. 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME 2023), Gulf of Naples, Italy. Published in Procedia CIRP, 126. External link
Jadayel, M., & Khameneifar, F. (2022, June). 3D scan data-based shape compensation using graph U-Nets to correct the systematic geometric errors of additive manufacturing parts [Poster]. 5th Holistic Innovation in Additive Manufacturing Conference (HI-AM 2022), Montréal, Québec. External link
Jadayel, M., & Khameneifar, F. (2021, June). A shape Compensation Approach for Enhancing the Geometric Accuracy of Additive Manufacturing Parts [Abstract]. 4th Holistic Innovation in Additive Manufacturing Conference (HI-AM 2021). External link
Jadayel, M., & Khameneifar, F. (2020). Improving geometric accuracy of 3D printed parts using 3D metrology feedback and mesh morphing. Journal of Manufacturing and Materials Processing, 4(4), 112 (15 pages). Available
Jadayel, M., & Khameneifar, F. (2020, June). Improving the geometric accuracy of additive manufactured parts via 3D metrology feedback and CAD morphing [Poster]. 3rd Holistic Innovation in Additive Manufacturing Conference (HI-AM 2020). Unavailable
