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Titlebook: Selected Topics in Manufacturing; Emerging Trends from Luigi Carrino,Luigi Maria Galantucci,Luca Settiner Book 2024 The Editor(s) (if appli

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樓主: mountebank
11#
發(fā)表于 2025-3-23 12:49:39 | 只看該作者
12#
發(fā)表于 2025-3-23 16:07:06 | 只看該作者
Surface Functionalization of Metallic Biomaterials: Present Trend and Future Perspectiveslenging research area. The paper reports the most used surface functionalization for metallic biomaterials highlighting limitations and advantages of such technologies on the actual industrial panorama.
13#
發(fā)表于 2025-3-23 19:41:31 | 只看該作者
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發(fā)表于 2025-3-24 01:12:27 | 只看該作者
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發(fā)表于 2025-3-24 04:38:08 | 只看該作者
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發(fā)表于 2025-3-24 07:58:19 | 只看該作者
Aerosol Jet Printing of 3D Biocompatible Gold Nanoparticle-Based Micro-Structuresploited for 2D printed electronics, nevertheless, is gaining attention in the bioelectronic field. Few emerging studies have also established AJ.P as a micro-AM 3D printing technique. In this context, the 3D AJ.P process has not been deeply analysed yet. This work proposes an unique study of novel 3
17#
發(fā)表于 2025-3-24 13:59:10 | 只看該作者
Surface Quality Improvement Techniques for 3D Printed Metal Samples the benefits of AM processes, these techniques are generally characterized by a low-quality surface finish, one of the most important requirements in several industrial fields. Considering this aspect, it is important to define solutions able to improve the surface finish to benefit the low lead ti
18#
發(fā)表于 2025-3-24 17:47:29 | 只看該作者
Arc Oscillation for Microstructural and Geometric Control of Solids Produced by WAAMre metallic materials, a very low incidence of porosity and excellent mechanical properties. However, there are challenges in WAAM, like the uncontrollable grain growth (due to the prolonged exposure to high temperatures) and the accumulation of impurities or decrease in toughness (due to preferred
19#
發(fā)表于 2025-3-24 19:36:54 | 只看該作者
20#
發(fā)表于 2025-3-25 00:53:41 | 只看該作者
Unlocking New In-Situ Defect Detection Capabilities in Additive Manufacturing with Machine Learning ring the quality and reliability of AM parts remains a challenge, as defects can occur during the building process. In-situ monitoring is a promising approach for detecting and classifying these defects for in-process part qualification. In this paper, we present a novel approach for in-situ monitor
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