Microstructure and Properties of Ti-Zr-Mo Alloys Fabricated by Laser Directed Energy Deposition

Zhang, Jingtao; Wang, Cunshan; Shareef, Nisha

doi:10.3390/ma16031054

Cited by 6 publications

(2 citation statements)

References 49 publications

(51 reference statements)

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“…The design considerations for Ti-Mo-Zr alloys were based on several factors influenced by fabrication method [21] or another alloying element, such as chromium [22]. Titanium is considered to have extremely low toxicity for the human body and exhibits excellent interaction with bone due to its corrosion resistance and lack of rejection by the body.…”

Section: Introductionmentioning

confidence: 99%

Effect of Si Contents on the Properties of Ti15Mo7ZrxSi Alloys

et al. 2023

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The main purpose of this research is to evaluate the mechanical characteristics and biocompatibility of two novel titanium alloys, Ti15Mo7ZrxSi (x = 0, 0.5, 0.75, 1). These samples had already undergone grinding, polishing, cutting, and chipping. Electrochemical, metallographic, three-point bending, and microhardness studies were conducted on the studied materials to determine their corrosion behavior, microstructure, Young’s modulus, and hardness. The first investigations revealed that both samples had biphasic and dendritic structures, elastic moduli that were between the highest and minimum values achieved by around 20 GPa, and favorable behavior when in contact with physiological fluids at ambient temperature. Ti15Mo7Zr0.5Si and Ti15Mo7Zr0.75Si, the research samples, had greater corrosion potentials, reduced corrosion rates, and therefore higher corrosion resistance, as well as modulus of elasticity values that were comparable to and closer to those of human bone. The results of this investigation indicate that both alloys exhibit favorable corrosion behavior, great biocompatibility, Young’s modulus results lower than those of conventional alloys used in biomedical implants, and hardness values higher than commercially pure titanium.

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Section: Introductionmentioning

confidence: 99%

Effect of Si Contents on the Properties of Ti15Mo7ZrxSi Alloys

et al. 2023

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“…Real-time prediction of bead width in L-DED with Semi-Supervised TL 3 Due to its own characteristics and constant thermal activity cycles during the process, DED encompass different ranges of precision within the metal-based AM technologies and part quality, directly affecting the final part finishing and the build-up time -not being exempt from drawbacks [9]. One of the most critical defect that occurs in L-DED is porosity, which significantly affects the mechanical properties and the overall quality of the manufactured part.…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction of deposited bead width in L-DED using Semi-Supervised Transfer Learning

Mochi,

Núñez,

Ribeiro

et al. 2023

Preprint

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Laser Directed Energy Deposition (L-DED) is an additive manufacturing (AM) technology that offers unique advantages for creating and repairing metallic parts. The quality of the final print is highly dependent on the consistency of the printing parameters, and as a result, the deposited bead geometry along the printing pathway. Inconsistencies, particularly in the bead width, can produce defects between adjacent beads, increasing the material susceptibility to failure. Therefore, it is crucial to monitor and predict the printing parameters in real-time during the process. The objective of this study is to develop a machine learning model that accurately predicts the width of the deposited track in real-time. To achieve this, a dataset with approximately 12,379 melt pool images was used to train the algorithm and predict the width of the deposited track. Each one of the deposited track was measured by a profilometer at 10 points along the pathway, and intermediate labels were predicted using a spline curve, making the model semi-supervised. To implement the machine learning model, pre-trained frozen networks based on Convolutional Neural Networks (CNN) architectures VGG, ResNet, and DenseNet were employed, using transfer learning principles. These networks were integrated into a dense, fully connected network containing trainable parameters. The results demonstrate a good model fit, with a mean absolute error of 4.5% and a mean absolute error of 0.0358 mm. Moreover, the processing frequency of the best model, at 55 Hz, enables real-time control of the L-DED manufacturing process. It is important to highlight, however, that the VGG based model shows a frequency of 250 Hz and similar results. Thus, accurately predicting the width of the deposited bead has the potential to significantly improve the quality of the final part by reducing overall defects, such as lack of fusion and porosity, whenever this data is used as input to a feed-back control system. This is particularly valuable in industries where mechanical properties and fatigue life are critical, such as automotive, aerospace, and biomedical sectors. The models developed in this study offer a promising approach to effectively provide data to control the L-DED manufacturing process in real-time, enabling the reliable production of high-quality components associated with reduced manufacturing costs.

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