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A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing

- Li, Xiaomin, Wan, Jiafu, Dai, Hong-Ning, Imran, Muhammad, Xia, Min, Celesti, Antonio

  • Authors: Li, Xiaomin , Wan, Jiafu , Dai, Hong-Ning , Imran, Muhammad , Xia, Min , Celesti, Antonio
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 7 (2019), p. 4225-4234
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  • Description: At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing. © 2005-2012 IEEE.

Machine learning driven digital twin for industrial control black box system : a novel framework and case study

- Siddiqui, Mustafa, Kahandawa, Gayan, Hewawasam, Hasitha, Rehman Siddiqi, Muftooh

Artificial intelligence enabled digital twin for predictive maintenance in industrial automation system : a novel framework and case study

- Siddiqui, Mustafa, Appuhamillage, Gayan, Hewawasam, Hasitha

  • Authors: Siddiqui, Mustafa , Appuhamillage, Gayan , Hewawasam, Hasitha
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Mechatronics, ICM 2023, Leicestershire UK, 15-17 March 2023, Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023
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  • Description: Industrial automation systems are excessively used in advanced manufacturing environments. These systems are always prone to failure which not only disturbs smooth manufacturing operations but can also cause injuries to operators. Therefore, in this research, a novel predictive maintenance algorithm is proposed that can be used to detect anomalies in the automation system to avoid asset failure. Artificial Intelligence enabled Digital Twin model was used to detect early anomalies to avoid catastrophic effects of equipment failure. Real-time sensor data were used to validate the proposed novel algorithm. The data were recorded via sensors mounted on the physical system. This paper presents the effectiveness of the proposed algorithm to detect anomalies in industrial automation systems under faulty conditions. © 2023 IEEE.

Model compression for IoT applications in industry 4.0 via multiscale knowledge transfer

- Fu, Shipeng, Li, Zhen, Liu, Kai, Din, Sadia, Imran, Muhammad, Yang, Xiaomin

  • Authors: Fu, Shipeng , Li, Zhen , Liu, Kai , Din, Sadia , Imran, Muhammad , Yang, Xiaomin
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6013-6022
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  • Description: Recently, Industry 4.0 has attracted much attention. It has close relations with the Internet of Things (IoT). On the other hand, convolutional neural networks (CNNs) have shown promising performance in many foundational services of the IoT applications. For the IoT applications with high-speed data streams and the requirement of time-sensitive actions, fast processing is demanded on small-scale platforms or even on IoT devices themselves. Therefore, it is inappropriate to employ cumbersome CNNs in IoT applications, making the study of model compression necessary. In knowledge transfer, it is common to employ a deep, well-trained network, called teacher, to guide a shallow, untrained network, called student, to have better performance. Previous works have made many attempts to transfer single-scale knowledge from teacher to student, leading to degradation of generalization ability. In this article, we introduce multiscale representations to knowledge transfer, which facilitates the generalization ability of student. We divide student and teacher into several stages. Student learns from multiscale knowledge provided by teacher at the end of each stage. Extensive experiments demonstrate the effectiveness of our proposed method both on image classification and on single image super-resolution. The huge performance gap between student and teacher is significantly narrowed down by our proposed method, making student suitable for IoT applications. © 2005-2012 IEEE.

Cloud-based smart manufacturing for personalized candy packing application

- Wang, Shiyong, Wan, Jiafu, Imran, Muhammad, Li, Di, Zhang, Chunhua

  • Authors: Wang, Shiyong , Wan, Jiafu , Imran, Muhammad , Li, Di , Zhang, Chunhua
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Journal of Supercomputing Vol. 74, no. 9 (2018), p. 4339-4357
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  • Description: Industry 4.0 has been proposed to address personalized consumption demands by building cyber-physical production systems for smart manufacturing. Although cloud manufacturing and some integrated frameworks for smart factory have been presented in literatures, it still lacks industrial applications. In this paper, we use personalized candy packing application as a demonstration to illustrate our smart factory design. We first describe the component layers of the smart factory, i.e., physical devices, private cloud, client terminals, and network, to enable the smart factory to be integrated with other systems, such as banks and logistical network, to cope with personalized consumption demands. Then, we present a scheme for inter-layered interaction. As for the physical devices, we also design an intra-layered negotiation mechanism to implement dynamic reconfiguration, so that the system can support hybrid production of multi-typed products. Finally, we give experimental results to verify efficiency, self-organized process, and hybrid production paradigm of the proposed system. © 2016, Springer Science+Business Media New York.

Rapid cross-linking of epoxy thermosets induced by solvate ionic liquids

- Hameed, Nishar, Eyckens, Daniel, Long, Benjamin, Salim, Nisa, Capricho, Jaworski, Servinis, Linden, De Souza, Mandy, Perus, Magenta, Varley, Russell, Henderson, Luke

  • Authors: Hameed, Nishar , Eyckens, Daniel , Long, Benjamin , Salim, Nisa , Capricho, Jaworski , Servinis, Linden , De Souza, Mandy , Perus, Magenta , Varley, Russell , Henderson, Luke
  • Date: 2020
  • Type: Text , Journal article
  • Relation: ACS Applied Polymer Materials Vol. 2, no. 7 (2020), p. 2651-2657
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  • Description: The high-volume manufacture of fiber-reinforced composites faces a huge challenge because long resin curing times put a low ceiling on the total output of parts produced per year. To translate the benefits from using epoxy in large-volume production platforms, cure cycle times of less than 1 min must be achieved. In this work, we report solvate ionic liquids (SILs) as simple and efficient rapid curing catalytic additives in epoxy systems. Ultrafast curing was observed at low levels of 1-5% of SIL in epoxy resin, and the cure rate is enhanced up to 26-fold without compromising the mechanical and thermal properties. Further investigations revealed that enhancement in the cure rate is dependent on the type of SILs employed, influenced by the metal center, the ligands around the metal, and the identity of the counter anion. The relative Lewis acidity of each of the active complexes was calculated, and the rapid cure effect was attributed to the activation of the epoxide moietyviathe Lewis acidic nature of the SIL. Making epoxy thermosets rapidly processable enables enormous benefits, finding applications in a whole variety of transformation methods that exist for traditional glass and metals. Copyright © 2020 American Chemical Society

Digital twin for risk and uncertainty analysis in complex industrial control and automation systems using artificial intelligence and machine learning

- Siddiqui, Muhammad

  • Authors: Siddiqui, Muhammad
  • Date: 2023
  • Type: Text , Thesis , Masters
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  • Description: Industrial control systems play a crucial role in enabling advanced manufacturing operations. However, these systems are inherently susceptible to failure. Detecting faults at an early stage is of paramount importance, as it can prevent the occurrence of fatal and catastrophic consequences resulting from equipment failures. Moreover, timely detection and resolution of faults can save significant costs and time for organizations. The failure of these systems not only poses risks to operators but can also lead to substantial delays in the advanced manufacturing process, imposing substantial financial burdens on organizations. Therefore, a methodology is needed that can be used to avoid the adverse effects of equipment failure of industrial control systems to achieve smooth advanced manufacturing operations. To achieve this, the methodology should be able to detect the abnormal behaviour of the system at very early stages for predictive maintenance. This methodology can be designed using an extremely popular concept known as the Digital Twin, which has gained significant importance in the era of Industry 4.0. In this research, artificial intelligence techniques will be employed to develop a highly accurate and detailed digital twin model. This model will serve as a valuable tool for predictive maintenance in complex industrial control systems, facilitating the achievement of smooth and uninterrupted advanced manufacturing processes. Also, the performance of the proposed Digital Twin model will be compared with state-of-the-art anomaly detection approaches. The digital twin, utilizing the proposed algorithms, will not only be able to detect anomalies but also quantify their severity, classifying them into different levels such as minor, severe, and faulty operations. Furthermore, the research addresses the generalization challenges faced by state-of-the-art approaches, showcasing the digital twin's ability to effectively classify unseen data as healthy or anomalous. The results obtained from the analysis and comparison of state-of-the-art approaches with the proposed algorithms clearly demonstrate the methodology's capability to detect anomalies, quantify their level, and classify them accurately and effectively in real-world data. This validation underscores the robustness and reliability of the developed methodology, further solidifying its potential as a valuable tool for predictive maintenance in complex industrial control systems.
  • Description: Masters by Research

Role of Lean Six Sigma in manufacturing setting: a systematic literature review and agenda for future research

- Kumar, Sandeep, Swarnakar, Vikas, Phanden, Rakesh, Khanduja, Dinesh, Chakraborty, Ayon

  • Authors: Kumar, Sandeep , Swarnakar, Vikas , Phanden, Rakesh , Khanduja, Dinesh , Chakraborty, Ayon
  • Date: 2024
  • Type: Text , Journal article , Review
  • Relation: TQM Journal Vol. 36, no. 7 (2024), p. 1996-2047
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  • Description: Purpose: The purpose of this study is to present the systematic literature review (SLR) on Lean Six Sigma (LSS) by exploring the state of the art on growth of literature on LSS within the manufacturing sector, critical factors to implement LSS, the role of LSS in the manufacturing sector from an implementation and sustainability viewpoint and Industry 4.0 viewpoints while highlighting the research gaps. Design/methodology/approach: An SLR of 2,876 published articles extracted from Scopus, WoS, Emerald Insight, IEEE Xplore, Taylor & Francis, Springer and Inderscience databases was carried out following the protocol of systematic review. In total, 154 articles published in different journals over the past 10 years were selected for quantitative and qualitative analysis which revealed a number of research gaps. Findings: The findings of the SLR revealed the growth of literature on LSS within the manufacturing sector. The review also highlighted the most cited critical success factors, critical failure factors, performance indicators and associated tools and techniques applied during LSS implementation. The review also focused on studies related to LSS and sustainability viewpoint and LSS and Industry 4.0 viewpoints. Practical implications: The findings of this SLR can help senior managers, practitioners and researchers to understand the current developments and future requirements to adopt LSS in manufacturing sectors from sustainability and Industry 4.0 viewpoints. Originality/value: Academic publications in the context of the role of LSS in various research streams are sparse, and to the best of the authors’ knowledge, this paper is one of the first SLRs which explore current developments and future requirements to implement LSS from sustainability and Industry 4.0 perspective. © 2023, Emerald Publishing Limited.
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