Automation and manufacturing of smart materials in additive manufacturing technologies using Internet of Things towards the adoption of industry 4.0

Ashima, Reem; Haleem, Abid; Bahl, Shashi; Javaid, Mohd; Mahla, Sunil Kumar; Singh, Shiv

doi:10.1016/j.matpr.2021.01.583

Cited by 177 publications

(62 citation statements)

References 68 publications

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“…The processes applied by the engineers and researchers were observed to be in line with the findings of several previous studies (Andrei et al, 2015;Ashima et al, 2021;Fatima et al, 2021;Feng et al, 2014;Ul Haq et al, 2020;Huson & Hoskins, 2014;Javaid & Haleem, 2018aKucklick, 2006) that have used RE to meet market demand through fast, precise, and accurate products.…”

Section: Analysis Of Making 3d Cad Milling Settingssupporting

confidence: 83%

Reverse engineering from 3D mesh to ceramic product in the form of miranda kerr tea for one teapot in PT doulton Indonesia

et al. 2021

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Section: Analysis Of Making 3d Cad Milling Settingssupporting

confidence: 83%

Reverse engineering from 3D mesh to ceramic product in the form of miranda kerr tea for one teapot in PT doulton Indonesia

et al. 2021

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“…Anything necessitates a wide range of technology that must be carefully integrated and orchestrated. These advancements in technology allow intelligent machines, machinery, appliances, and integrated automation systems [2,3] to automate routine operations and solve complex problems without human interference. Improvements in the smart workplace, smart data exploration, cognitive automation, and other aspects of business smartness should all be included.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection

Awotunde

Chakraborty

Adeniyi

2021

Wireless Communications and Mobile Computing

127

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The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning-based intrusion detection paradigm for IIoT with hybrid rule-based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule-based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well-known network datasets, NSL-KDD and UNSW-NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL-KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW-NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.

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“…In the future, electrospinning can be integrated into additive manufacturing (AM) technologies Haq et al 2020;Javaid and Haleem 2018a) for fabrication of micro-vessel-incorporated scaffolds and implants. Tissue-engineered scaffolds for soft tissues synthesized using AM technology coupled with advanced technologies in the Industry 4.0 framework and Internet of things (IoT) have minimal wastage, rapid production rates, and can meet patient-specific needs (Ashima et al 2021;Fatima et al 2021;Javaid and Haleem 2020). For hard tissues, various advanced materials, such as polyether ether ketone (PEEK) Javaid 2019a, 2019b), have demonstrated both high biocompatibility and excellent mechanical properties which are similar to the native hard tissues in the human body for load-bearing applications Haleem 2018b, 2019).…”

Section: Discussionmentioning

confidence: 99%

Near-field electrospinning polycaprolactone microfibers to mimic arteriole-capillary–venule structure

Qavi

Tan

2021

Prog Biomater

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The ability to create three-dimensional (3D) cell-incorporated constructs for tissue engineering has progressed tremendously. One of the major challenges that limit the clinical applications of tissue engineering is the inability to form sufficient vascularization of capillary vessels in the 3D constructs. The lack of a functional capillary network for supplying nutrients and oxygen leads to poor cell viability. This paper presents the near-field electrospinning (ES) technique to fabricate a branched microfiber structure that mimics the morphology of capillaries. Polycaprolactone solution was electrospun onto a sloped collector that resulted in morphological and geometric variation of the fibers. With proper control over the solution viscosity and the electrospinning voltage, a single fiber was scattered into a branched fiber network and then converged back to a single fiber on the collector. The obtained fibers have a diameter of less than 100 microns at the two ends with coiled and branched fibers of less than 10 microns that mimics the arteriole-capillary-venule structure. The formation of such a structure in the near-field ES strongly depends on the solution viscosity. Low viscosity solutions form beads and discontinuous lines thus cannot be used to achieve the desired structure. The branching of PCL fiber occurs due to an electrohydrodynamic instability. The transition from the straight large fiber to smaller coiled/branched fibers is not instantaneous and stretches over a horizontal region of 1.5 cm. The current work shows the feasibility of electrospinning the stem-branch-stem fibrous structure by adopting a valley-shaped collector with potentials for tissue engineering applications.

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Automation and manufacturing of smart materials in additive manufacturing technologies using Internet of Things towards the adoption of industry 4.0

Cited by 177 publications

References 68 publications

Reverse engineering from 3D mesh to ceramic product in the form of miranda kerr tea for one teapot in PT doulton Indonesia

Reverse engineering from 3D mesh to ceramic product in the form of miranda kerr tea for one teapot in PT doulton Indonesia

Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection

Near-field electrospinning polycaprolactone microfibers to mimic arteriole-capillary–venule structure

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