A big data-driven framework for sustainable and smart additive manufacturing

Majeed, Arfan; Zhang, Yingfeng; Ren, Shengnan; Lv, Jingxiang; Peng, Tao; Waqar, Saad; Yin, Enhuai

doi:10.1016/j.rcim.2020.102026

Cited by 206 publications

(109 citation statements)

References 66 publications

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“… Maroufkhani et al, (2020) proposed a data-driven analysis to obtain the technological-organizational-environmental paradigm to implement the lessening resource utilization and emission reduction solutions in SSCM systems. Majeed et al (2021) developed a modeling structure by uniting big data analytics to additive manufacturing, and sustainable smart manufacturing technologies which is advantageous to the additive manufacturing initiatives.…”

Section: Data Collection and Methodologymentioning

confidence: 99%

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Bui

Tsai

Tseng

et al. 2021

Sustainable Production and Consumption

182

119

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Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation.

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Section: Data Collection and Methodologymentioning

confidence: 99%

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Bui

Tsai

Tseng

et al. 2021

Sustainable Production and Consumption

182

119

View full text Add to dashboard Cite

show abstract

“…In Figure 3, for the same screw speed and barrel temperature, the main compression and metering section variations for the micro-extruder (Table 1, Column 2, blue full line) and the standard extruder (Table 1, Column 3, red dashed line) are compared. The conventional extruder presents a bigger barrel diameter that leads to higher velocities (V b , V j , and V sz ; Equations (3), (6) and 7), as visible by the red lines in Figure 3a. Specifically, the higher relative velocities are leading to stronger viscous heating dissipations during the melting stage, leading to larger Br numbers (red dashed line in Figure 3d), at least if the shear thinning effect is not dominant.…”

Section: Melting Efficiency For Micro-vs Conventional Extrudermentioning

confidence: 99%

“…The application of additive manufacturing (AM) for polymeric final products, which is popularly known as 3D printing or rapid prototyping, has increased significantly in the last years [1][2][3][4][5][6][7][8][9]. A frequently considered methodology for the manufacturing of polymer parts is extrusion-based AM (EAM) [2,10].…”

Section: Introductionmentioning

confidence: 99%

Theoretical Evaluation of the Melting Efficiency for the Single-Screw Micro-Extrusion Process: The Case of 3D Printing of ABS

et al. 2020

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One of the challenges for single-screw micro-extrusion or additive manufacturing (AM), thus 3D printing, of polymers is controlling the melting efficiency so that energy and equipment costs can be minimized. Here, a numerical model is presented for AM process design, selecting acrylonitrile–butadiene–styrene (ABS) as viscoelastic reference polymer. It is demonstrated that AM melting is different compared to conventional melting due to variation in extrusion dimensions, leading to a different balance in heating by conduction and viscous heat dissipation as caused by the shearing between the melt layers in the associated film layer near the barrel. The thickness of this melt film layer is variable along the screw length, and it is shown that simplified models assuming an overall average value are too approximate. It is highlighted that the screw frequency, pitch angle and compression ratio are important process parameters to control the point of melt finalization. In addition, the power-law index reflecting the shear thinning nature of the polymer melt is showcased as a key parameter. Moreover, AM process results assuming constant and temperature dependent specific heat capacities and thermal conductivities are compared. The current work opens the door for on-line AM process control, addressing all relevant operating and material parameters.

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“…Smart manufacturing leverage correlates with the advanced data velocity, volume, and variety connected with big data. Applying big data techniques increases the strength of analyses and assists with predictive analysis [ 6 ]. The mentioned capabilities come up in most industries but with various factors and speeds, i.e., their needs, based on suppliers and installation methods.…”

Section: Introductionmentioning

confidence: 99%

Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing

Shahbazi

Byun

2021

Sensors

104

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Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system’s quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system’s quality control approach.

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A big data-driven framework for sustainable and smart additive manufacturing

Cited by 206 publications

References 66 publications

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Theoretical Evaluation of the Melting Efficiency for the Single-Screw Micro-Extrusion Process: The Case of 3D Printing of ABS

Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing

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