AI Cloud-Based Smart Manufacturing and 3D Printing Techniques for Future In-House Production

Jawad, Mohammed Saeed; Bezbradica, Marija; Crane, Martin; Alijel, Mohammed Khalil

doi:10.1109/aiam48774.2019.00154

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…Cloud-computing elasticity and highly tenability combined with blockchain and smart contract capabilities are the main enablers technology towards industry 5.0. This can be push forwards innovative mass individualized production and rapid prototyping services for circuit-boards production as an example [ 15 , 16 ].…”

Section: Digital-twin As Integrated Added-values Manufacturing In The...mentioning

confidence: 99%

Adoption of digital twin for sustainable manufacturing and achievements of production strategic-planned goals

Jawad¹,

Chandran²,

Ramli³

et al. 2022

MethodsX

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Section: Digital-twin As Integrated Added-values Manufacturing In The...mentioning

confidence: 99%

Adoption of digital twin for sustainable manufacturing and achievements of production strategic-planned goals

Jawad¹,

Chandran²,

Ramli³

et al. 2022

MethodsX

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“…Industrial IoT: The fourth industrial revolution (Industry 4.0) is triggered by the advancements in automation and manufacturing industries, coupled with the emergence of IIoT devices. In particular, the introduction of AI in the manufacturing industry has enabled various attractive applications, including smart factories, predictive maintenance, and 3D printing, to name a few [130], [131]. Albeit the promising features of FL that can be beneficial for IIoT networks, the upsurge number of IoT devices that may participate in the training process, might produces a colossal traffic that burdens the network.…”

Section: ) Iot Networkmentioning

confidence: 99%

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Al-Quraan¹,

Mohjazi²,

Bariah³

et al. 2021

Preprint

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The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost due to increased data communication, (ii) threatened data privacy by allowing untrusted parties to utilise this information. Recently, in light of these limitations, a new emerging technique, coined as federated learning (FL), arose to bring ML to the edge of wireless networks. FL can extract the benefits of data silos by training a global model in a distributed manner, orchestrated by the FL server. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of FL. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth generation (B5G) and sixth generation (6G) communication systems. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

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“…The next step is a cyclic process in which the global optimal value of the problem is approached continuously. For each particle, the following operations are performed: update the particle velocity and position, calculate the equipment utilization rate and order task execution time corresponding to the new decision variable, and update the historical optimal position of each particle and the historical optimal position of the population according to the least principle of (8). When the set number of cycles is reached, a relatively optimal decision variable can be obtained to minimize the average execution time of all the order tasks and maximize equipment utilization.…”

Section: C1 C2mentioning

confidence: 99%

“…Recently, new information technologies, such as artificial intelligence [1][2][3], cloud computing [4][5][6] and Internet of Things (IoT) [7][8][9], have emerged one after another, which have enlightened upgrades to the manufacturing industry. Nowadays, customer-needs show a trend of diversity and individualization, and the personalized customization production mode has gradually become mainstream.…”

Section: Introductionmentioning

confidence: 99%

A Thing-Edge-Cloud Collaborative Computing Decision-Making Method for Personalized Customization Production

Jiang

2021

IEEE Access

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With the development of Industry 4.0 and cloud computing technology, personalized customization as a new production mode is showing a trend of rapid development. Personalized customization has the characteristics of order-driven production, strict processing times, high dynamic external conditions, and large flexibility in the production process, all of which bring more uncertainty to the production system and great challenges to the edge computing processing of related tasks in personalized customization production. Aiming at the above problems, a thing-edge-cloud collaborative computing decision-making (TCCD) method in customized production is proposed. First, the architecture of a personalized customized production system used for implementing the TCCD method is presented. Then, according to the number and type of products in the customer order received from the private cloud platform, the customer's personalized customized order is dynamically divided. Subsequently, a task priority sorting algorithm is proposed to optimize the waiting time of all tasks involved in the order. Furthermore, a discrete particle swarm algorithm is proposed to optimize the average execution time of all tasks and equipment utilization decision-making options (thing-edge collaborative computing, edge-edge collaborative computing, or edge-cloud collaborative computing). Finally, the effectiveness of the proposed TCCD method is verified by using the prototype platform of personalized product packaging intelligent production line with the same process flow.INDEX TERMS Edge collaborative computing, decision-making, Industry 4.0, customized production.

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AI Cloud-Based Smart Manufacturing and 3D Printing Techniques for Future In-House Production

Cited by 12 publications

References 8 publications

Adoption of digital twin for sustainable manufacturing and achievements of production strategic-planned goals

Adoption of digital twin for sustainable manufacturing and achievements of production strategic-planned goals

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

A Thing-Edge-Cloud Collaborative Computing Decision-Making Method for Personalized Customization Production

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