Laser-Assisted Machining of Ti-6Al-4V Fabricated by DED Additive Manufacturing

Woo, Wan-Sik; Kim, Eun-Jung; Jeong, Ho-In; Lee, Choon-Man

doi:10.1007/s40684-020-00221-7

Cited by 51 publications

(18 citation statements)

References 20 publications

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“…Each layer was deposited with five hatches, and the overlapping efficiency of the hatch was 40%. If the efficiency was lower than 30%, internal pores may have been generated, and the mechanical properties may have been reduced [ 20 , 34 ]. If the overlapping efficiency was too high, deformation and internal stress would occur, owing to the reduced productivity and thermal overlap.…”

Section: Methodsmentioning

confidence: 99%

“…The AM process’s research and development has been actively conducted because it has the advantages of easy design and manufacturing of complex or special shapes, and it is used in various fields such as aerospace, automobiles, medicine, and machinery. AM technology can be classified by processes into two types: the powder bed fusion (PBF) process and directed energy deposition (DED) process [ 20 ]. PBF is an AM process in which thermal energy (a laser or electron beam) selectively fuses regions of a powder bed layer by layer [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Manufacturing of Ti-Nb-Cr-V-Ni-Al Refractory High-Entropy Alloys Using Direct Energy Deposition

2022

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High-entropy alloys (HEAs) are composed of 5–35 at% of five or more elements, have high configurational entropy, do not form intermetallic compounds, and have a single-phase face-centered cubic structure or body-centered cubic structure. In particular, refractory HEAs (RHEAs), based on refractory materials with excellent mechanical properties at high temperatures, have high strength and hardness at room temperature and excellent mechanical properties at low and high temperatures. In this study, the Ti-Nb-Cr-V-Ni-Al RHEAs were deposited using direct energy deposition (DED). In the microstructure of Ti-Nb-Cr-V-Ni-Al, the sigma, BCC A2, and Ti2Ni phases appeared to be different from the BCC A2, BCC B2, and Laves phases predicted in the phase diagram. This microstructure was similar to that of the casted Ti-Nb-Cr-V-Ni-Al and had a constructed fine grain size. It was found that the growth of these microstructures was due to the DED process, which has a fast solidification rate. The fine grain size caused high hardness, and the microhardness of the Ti-Nb-Cr-V-Ni-Al was measured to be about 900 HV. In addition, in order to analyze the thermal properties of Ti-Nb-Cr-V-Ni-Al composed of the refractory material, the heat-affected zone (HAZ) was analyzed through a preheating test. The HAZ was decreased, owing to the high thermal diffusivity of Ti-Nb-Cr-V-Ni-Al.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Manufacturing of Ti-Nb-Cr-V-Ni-Al Refractory High-Entropy Alloys Using Direct Energy Deposition

2022

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“…The purpose of this systematic review is to analyze recently published literature on how artificial Intelligence of Things-based cognitive manufacturing networks (Cug et al, 2022;Kovacova et al, 2022aKovacova et al, , 2022bLyons, 2022a;Robinson, 2022) having an increased level of automation (Dawson, 2022;Kliestik et al, 2022a;Poliak et al, 2022;Rice, 2022) integrate massive machine-sensed multimodal data (Sharma et al, 2021;Woo et al, 2020), neural network-based embedding and cognitive manufacturing control algorithms (Altaf et al, 2021;Chang et al, 2021;Chung et al, 2019;Perzylo et al, 2019), and enhanced operational adjustability and efficiency (Maier et al, 2010;Zeba et al, 2021;Zheng et al, 2021) in the direction of mass personalization and smart adaptive systems. We want to elucidate whether the integration of artificial intelligence data-driven Internet of Things systems and real-time advanced analytics (Kumar & Jaiswal, 2021;Li et al, 2021a;Zhao & Xu, 2010;Krüger et al, 2016) has furthered the swift advancement of Internet of Things-based real-time production logistics.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Lăzăroiu

Androniceanu

Grecu

et al. 2022

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Research background: With increasing evidence of cognitive technologies progressively integrating themselves at all levels of the manufacturing enterprises, there is an instrumental need for comprehending how cognitive manufacturing systems can provide increased value and precision in complex operational processes. Purpose of the article: In this research, prior findings were cumulated proving that cognitive manufacturing integrates artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production. Methods: Throughout April and June 2022, by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms including ?cognitive Industrial Internet of Things?, ?cognitive automation?, ?cognitive manufacturing systems?, ?cognitively-enhanced machine?, ?cognitive technology-driven automation?, ?cognitive computing technologies,? and ?cognitive technologies.? The Systematic Review Data Repository (SRDR) was leveraged, a software program for the collecting, processing, and analysis of data for our research. The quality of the selected scholarly sources was evaluated by harnessing the Mixed Method Appraisal Tool (MMAT). AMSTAR (Assessing the Methodological Quality of Systematic Reviews) deployed artificial intelligence and intelligent workflows, and Dedoose was used for mixed methods research. VOSviewer layout algorithms and Dimensions bibliometric mapping served as data visualization tools. Findings & value added: Cognitive manufacturing systems is developed on sustainable product lifecycle management, Internet of Things-based real-time production logistics, and deep learning-assisted smart process planning, optimizing value creation capabilities and artificial intelligence-based decision-making algorithms. Subsequent interest should be oriented to how predictive maintenance can assist in cognitive manufacturing by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.

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“…The rapid development of the Chinese economy has brought about conflicts between the natural environment and the nation's need for resources, including the waste of resources caused by edge materials, debris, and decommissioned products, which occur within the manufacturing process, while the natural growth of available energy no longer meets the needs of the nation's economic development. Simultaneously, a large number of waste liquids, residues, and greenhouse gas emissions are also generated in the manufacturing process [1][2][3]. Therefore, green design and manufacturing concepts are rapidly gaining popularity in manufacturer development [4].…”

Section: Introductionmentioning

confidence: 99%

Application of Green Design and Manufacturing in Mechanical Engineering: Education, Scientific Research, and Practice

Gao

Wang

et al. 2021

Sustainability

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Green design and manufacturing strategies are necessary to cope with the current resource, energy, and environmental problems of the manufacturing industry. To meet various enterprises’ needs for green design and manufacturing, colleges and universities should integrate these concepts into their curricula. This study discusses the application of green strategies in the mechanical engineering field from the viewpoints of teaching, scientific research, and practical education. Based on its development and a basic connotation analysis, this study highlights the challenges and urgency of incorporating green concepts into teaching and research modules of mechanical engineering, as well as methods and implementation strategies to incorporate them in professional curricula using teaching method reform and the teaching and research integration method. An experimental training course of advanced manufacturing processes at the authors’ institution was amended to emphasize the integrated application of green design and manufacturing. This integration not only enriches the field of mechanical engineering but also provides scientific research directions and methods to educators, along with new ideas to imbibe students with mechanical talents for their technical development. These efforts lay the foundation for the sustainable development of China’s manufacturing industry.

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Laser-Assisted Machining of Ti-6Al-4V Fabricated by DED Additive Manufacturing

Cited by 51 publications

References 20 publications

Manufacturing of Ti-Nb-Cr-V-Ni-Al Refractory High-Entropy Alloys Using Direct Energy Deposition

Manufacturing of Ti-Nb-Cr-V-Ni-Al Refractory High-Entropy Alloys Using Direct Energy Deposition

Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Application of Green Design and Manufacturing in Mechanical Engineering: Education, Scientific Research, and Practice

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