Process control based on pattern recognition for routing carbon fiber reinforced polymer

Shaban, Yasser; Meshreki, Mouhab; Yacout, Soumaya; Balazinski, Marek; Attia, Helmi

doi:10.1007/s10845-014-0968-6

Cited by 18 publications

(4 citation statements)

References 30 publications

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“…In our work, we use the binarization technique proposed in Boros et al (2000). The proposed binarization technique is briefly explained in Shaban et al (2017). The pattern generation is the second step in the proposed technique; in this step, the key building block in LAD extraction is generated.…”

Section: Logical Analysis Of Data (Lad)mentioning

confidence: 99%

Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0

et al. 2020

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New online fault monitoring and alarm systems, with the aid of Cyber-Physical Systems (CPS) and Cloud Technology (CT), are examined in this article within the context of Industry 4.0. The data collected from machines is used to implement maintenance strategies based on the diagnosis and prognosis of the machines' performance. As such, the purpose of this paper is to propose a Cloud Computing Platform containing three layers of technologies forming a Cyber-Physical System which receives unlabelled data to generate an interpreted online decision for the local team, as well as collecting historical data to improve the analyzer. The proposed troubleshooter is tested using unlabelled experimental data sets of rolling element bearing. Finally, the current and future Fault Diagnosis Systems and Cloud Technologies applications in the maintenance field are discussed.

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Section: Logical Analysis Of Data (Lad)mentioning

confidence: 99%

Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0

et al. 2020

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“…Shaban et al proposed a process control technique applicable to the routing process for carbon fiber reinforced polymer (CFRP), which is a composite material used in the aerospace industry [103]. LAD was used to evaluate and to control the quality of the machined parts by monitoring some machining features and parameters.…”

Section: Fault Detection and Diagnosis With Ladmentioning

confidence: 99%

Recent advances in the theory and practice of Logical Analysis of Data

Lejeune

Lozina

Ragab

et al. 2019

European Journal of Operational Research

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“…Carbon fiber-reinforced plastic (CFRP) composites have remarkable mechanical attributes, including a high stiffnessto-weight ratio, exceptional strength-to-weight ratio, fracture toughness, corrosion resistance, unique structural functionalities, and enhanced dimensional stability. As a result, CFRP composites have garnered increasing appreciation across various manufacturing sectors like aerospace, medical services, aeronautics, and the automotive industry [1][2][3]. CFRP composites can be classified into two primary components: the resin matrix and the reinforcing fibers.…”

Section: Introductionmentioning

confidence: 99%

Multi-condition tool wear prediction for milling CFRP base on a novel hybrid monitoring method

Li,

Huang,

et al. 2023

Meas. Sci. Technol.

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In the carbon fiber-reinforced plastic milling process, the high abrasive property of carbon fiber will lead to the rapid growth of tool wear, resulting in poor surface quality of parts. However, due to the signal data distribution discrepancy under different working conditions, addressing the problem of local degradation and low prediction accuracy in tool wear monitoring model is a significant challenge. This paper proposes an entropy criterion deep conditional domain adaptation network, which effectively exploits domain invariant features of the signals and enhances the stability of model training. Furthermore, a novel unsupervised optimization method based on tool wear distribution is proposed, which refines the monitoring results of data-driven models. This approach reduces misclassification of tool wear conditions resulting from defects in data-driven models and interference from the manufacturing process, thereby enhancing the accuracy of the monitoring model. The experimental results show that the hybrid method provides assurance for the accurate construction of tool wear monitoring model under different working conditions.

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Process control based on pattern recognition for routing carbon fiber reinforced polymer

Cited by 18 publications

References 30 publications

Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0

Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0

Recent advances in the theory and practice of Logical Analysis of Data

Multi-condition tool wear prediction for milling CFRP base on a novel hybrid monitoring method

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