Mechanical strength and analysis of fracture of titanium joining submitted to laser and tig welding

Piveta, Ana Cláudia Gabrielli; Montandon, Andréia Affonso Barretto; Ricci, Weber Adad; Nagle, Maurício Meirelles

doi:10.1590/s1516-14392012005000127

Cited by 7 publications

(10 citation statements)

References 24 publications

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“…Laser welding is a long-standing competitor to the traditional arc welding process due to a deeper welding penetration depth that improves the mechanical properties of the welded joints [1], [2]. This method has several additional advantages, such as lower power consumption, narrower heat affected zone (HAZ) and higher welding velocity [2]. Unfortunately, the laser welding is not exempt from defects, in particular pores and cracks.…”

Section: Introductionmentioning

confidence: 99%

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Laser Welding Quality Monitoring via Graph Support Vector Machine With Data Adaptive Kernel

et al. 2019

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Laser welding is a rapidly developing technology that is of utmost importance in a number of industrial processes. The physics of the process has been investigated over the past 50 years and is mostly well understood. Nevertheless, online laser-quality monitoring remains an open issue until today due to its dynamic complexity. This paper is a supplement to existing approaches in the field of in situ and real-time laser-quality monitoring that presents a novel combination of state-of-the-art sensors and machine learning for data processing. The investigations were carried out using laser welding of titanium workpieces. The quality was estimated a posteriori by the visual inspection of cross-sections of the welded joints. Four quality categories were defined to cover the two main laser welding regimes: conduction and keyhole. The signals from the laser back reflection and optical and acoustic emissions were recorded during the laser welding process and were decomposed with the M-band wavelets. The relative energies of narrow frequency bands were taken as descriptive features. The correlation of the extracted features with the laser welding quality was carried out using the Laplacian graph support vector machine classifier. Also, an adaptive kernel for the classifier was developed to improve the analysis of the distributions of the complex features and was constructed from Gaussian mixtures. The presented laser welding setup and the developed adaptive kernel algorithm were able to classify the quality for every 2 µm of the welded joint with an accuracy ranged between 85.9% and 99.9%. Finally, the results of the developed adaptive kernel were compared with stateof-the-art machine learning methods.

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Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the laser welding is not exempt from defects, in particular pores and cracks. Both types of defects may take place during the two major regimes of laser welding; conduction and keyhole [2]- [4]. The latter regime is characterized by the presence of a narrow capillary of overheated vapour in the liquid phase.…”

Section: Introductionmentioning

confidence: 99%

Laser Welding Quality Monitoring via Graph Support Vector Machine With Data Adaptive Kernel

et al. 2019

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“…De acordo com estudos prévios 27 , comparada ao metal base, a solda a laser pode reduzir a resistência, mas aumentar a dureza do metal após a soldagem. Entretanto, Sjogren et al 21 (1988), Berg et al 24 (1996) e Piveta et al 18 (2010) não encontraram diferença estatisticamente significante para resistência mecânica entre estruturas soldadas a laser e sem solda.…”

Section: Resultadounclassified

“…O aumento do calor gerado na área de soldagem proporcionou um aumento definido do tamanho de grãos. Entretanto, segundo estudos prévios 2,18,23 , esse aumento na microestrutura Ticp após soldagem TIG não resultou em diminuição na resistência mecânica do mesmo (Figura 8). Em relação ao metal base, esses autores verificaram que a solda TIG aumentou a resistência à tração.…”

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Análise metalográfica do titânio puro submetido à soldagem laser Nd: YAG e TIG

Piveta

Ricci

Montandon

et al. 2013

Rev. odontol. UNESP

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ResumoIntrodução: Os métodos de soldagem mais utilizados em Odontologia não podem ser aplicados ao titânio puro e às suas ligas em função da alta reatividade do titânio com elementos atmosféricos; dessa forma, o mesmo não deve ser soldado por processo comum. Objetivo: O objetivo deste trabalho foi avaliar a característica metalúrgica do titânio comercialmente puro sem solda e submetido aos processos de soldagem a laser e TIG. Material e método: Foram confeccionados 15 corpos de prova em titânio comercialmente puro, cinco para cada condição, na forma de hastes cilíndricas, obtidas por fundição odontológica, sob atmosfera de gás argônio e vácuo, com calor produzido por um arco voltaico, com a injeção do titânio sob vácuo-pressão. Três grupos foram formados I: soldagem a laser; II: soldagem TIG, e III: sem solda. Os corpos de prova do grupo I e II foram seccionados ao meio e soldados por TIG e por laser, respectivamente; o grupo III foi mantido sem corte e sem solda, como controle. A análise metalográfica foi realizada sob aumentos de 50×, 100× e 200×, em microscópio. Resultado: Pelos resultados obtidos nas micrografias, o titânio comercialmente puro apresentou uma morfologia de grãos equiaxiais da fase α, o cordão de solda a laser apresentou estrutura martensítica e, na TIG, microestrutura Widmanstätten. Conclusão: A microestrutura martensítica é condizente com a alta taxa de resfriamento proveniente do processo de soldagem a laser. As estruturas martensítica e Widmansttäten são mais refinadas quando comparadas à microestrutura do metal base.Descritores: Soldagem em odontologia; titânio; prótese dentária; implante dentário. AbstractIntroduction: The welding method most commonly used in dentistry can not be applied to pure titanium and its alloys and its alloys due to high reactivity of titanium with the atmospheric elements must not be welded by common process. Objective: This study surveyed the characteristics of commercially pure titanium metal intact samples and those laser weld and tungsten inert gas weld. Material and method: Fifteen cylindrical rods were developments by using brass rods are patterns. The samples were invested in casings, subjected to thermal cycles, and positioned in a plasma arc-welding machine under argon atmosphere and vacuum, and titanium was injected under vacuum/ pressure. Three groups were formed I: laser welding, II, III and TIG welding, without welding. The bodies of the test piece in group I and II were cut in half and welded by TIG and laser, respectively, and group III was maintained intact as control. The metallographic analysis was performed under magnification 50×, 100× and 200× microscope. Result:The results obtained in the micrographs, the commercially pure titanium showed an equiaxed grain morphology of α phase, the laser weld bead showed martensitic structure and TIG Widmanstätten microstructure. Conclusion: Martensitic microstructure is in agreement with the high cooling rate from the laser welding process. Martensitic and Widmansttäten structures are more refined when co...

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Artificial Intelligence for Monitoring and Control of Metal Additive Manufacturing

Masinelli

Shevchik

Pandiyan

et al. 2020

Industrializing Additive Manufacturing

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Quality monitoring in Additive Manufacturing (AM) is currently mostly based on temperature measurements of the process zone or on layer/piecewise high-resolution surface imaging. To this aim, various sensors, such as pyrometers, photodiodes, and matrix CCD detectors, have been tested. These standard temperature measurements, however, do not provide a comprehensive description of the process dynamics, as they are just limited to surface observations. Furthermore, they are often used for post-factum inspection, i.e., after the piece is partially or even completely produced. No robust and low-cost methods are so far known to monitor the quality of laser processes in real-time. To close this gap, we propose an innovative approach for online quality monitoring of additive manufacturing employing acoustic emissions (AE). In fact, AE signals can provide in-depth information about the process, e.g., melting, resolidification, delamination, and cracking of the workpiece. Moreover, the sintering or melting of the metal powder has several unique acoustic signatures that can be detected and interpreted in terms of quality. In our approach, the correlation of the acoustic signals to the quality of the produced pieces is made by Artificial Intelligence (AI) methods. Specifically, AI in the form of Machine Learning is used to perform a data-driven extraction and recognition of the unique acoustic signatures from different sintering or melting events. In this contribution, we present a summary of our results in the fields of selective laser melting and laser welding, which have similar underlying mechanisms. At first, we discuss how, by using AE, we can classify different types of defects and porosity content in both processes. Afterward, with the aid of high-speed X-ray imaging, we demonstrate the real-time performance of our approach in the classification of transient events/regimes that are critical for the final quality -in particular conduction, stable keyhole, unstable keyhole, pore formation, and blowout. Finally, we present the future possibilities in terms of control of AM processes based on AI.

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Mechanical strength and analysis of fracture of titanium joining submitted to laser and tig welding

Cited by 7 publications

References 24 publications

Laser Welding Quality Monitoring via Graph Support Vector Machine With Data Adaptive Kernel

Laser Welding Quality Monitoring via Graph Support Vector Machine With Data Adaptive Kernel

Análise metalográfica do titânio puro submetido à soldagem laser Nd: YAG e TIG

Artificial Intelligence for Monitoring and Control of Metal Additive Manufacturing

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