ADVANCED MATERIALS & PROCESSES | APRIL 2024 40 iTSSe TSS iTSSe TSS 11 FEATURE the deposited shape. The final additively manufactured art piece using the CGDS process is shown in Fig. 4. To avoid the use of masks, which reduce spray resolution due to a greater standoff distance and increase the process time, development of a direct 3D CGDS printing setup is in the planning stage. This future setup will include machine learning algorithms, which will be used to proceed from a 2D photograph to a 3D automatic deposition process. CONCLUSION A novel use of cold gas dynamic spray enabled the creation of a complex artwork demonstrating the technology’s current capabilities and helping to determine the needs for improvement. The next step for this artistic endeavor is development of a maskless deposition CGDS process, which will also be beneficial in fields such as aerospace, automotive, and energy. ~iTSSe For more information: Bertrand Jodoin, FASM, professor, mechanical engineering, University of Ottawa, 770 King Edward Ave., Ottawa, Ontario K1N6N5, Canada, 613.562.5800 ext. 6280, bertrand.jodoin@uottawa.ca. References 1. R. Darolia, Thermal Barrier Coatings Technology: Critical Review, Progress Update, Remaining Challenges www.masterbond.com Hackensack, NJ 07601, USA ∙ +1.201.343.8983 ∙ main masterbond.com High Strength Epoxies for Testing Cohesive Strength of FLAME-SPRAYED COATINGS EP15ND-2 for metallic substrates EP15 for ceramic substrates DESIGNED FOR TESTING UNDER ASTM specification C633 HIGH TENSILE STRENGTH > 12,000 psi UNLIMITED WORKING LIFE at room temperature ONE PART, NO MIX SYSTEMS and Prospects, Taylor & Francis, 58(6), p 315-348, 2013, doi: 10.1179/1743280413Y.0000000019. 2. Y. Qing, et al., Enhanced Dielectric and Electromagnetic Interference Shielding Properties of FeSiAl/Al2O3 Ceramics by Plasma Spraying, J Alloys Compd, Elsevier, 651, p 259-265, 2015. 3. M. Jeandin, et al., Approche Artistique de La Projection Thermique, Traitements & Matériaux, 436, p 25-32, 2015, https://minesparis-psl.hal.science/hal-01251474. Accessed 9 Sept 2023. 4. J. Sauvola and M. Pietikäinen, Adaptive Document Image Binarization, Pattern Recognit, Pergamon, 33(2), p 225-236, 2000. 5. N. Otsu, Threshold Selection Method from Gray Level Histograms, IEEE Trans Syst Man Cybern, SMC-9(1), p 62-66, 1979. 6. M. Sezgin and B. Sankur, Survey over Image Thres- holding Techniques and Quantitative Performance Evaluation, SPIE, 13(1), p 146-165, 2004, doi:10.1117/1.1631315. 7. N. Vatani, et al., Gray Level Image Edge Detection using a Hybrid Model of Cellular Learning Automata and Stochastic Cellular Automata, Open Access Library Journal, Scientific Research Publishing, 2(1), p 1-8, 2015, doi:10.4236/ OALIB.1101203. ThermalSprayDirectory.com Simplify Your Search for Vendors Find the right solutions for your business. Search for products, research companies, connect with suppliers, and make confident purchasing decisions all in one place.
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