Neural Network Signal Processing in Spark Assisted Chemical Engraving (SACE) Micromachining

Sahebari, Seyed Mahmoud Seyedi; Barari, Ahmad; Ziki, Jana D. Abou

doi:10.1109/induscon51756.2021.9529814

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…To overcome the reliance, our previous research [26] introduced an innovative algorithm that utilizes three distinct thresholds for each dataset. These thresholds were determined through regression using Recurrent Neural Networks (RNN), building upon the same underlying concept.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Characterization of Spark-Assisted Chemical Engraving (SACE) Process Using Time Series Classification

Sahebari,

Bassyouni,

Barari

et al. 2023

Preprint

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Spark-Assisted Chemical Engraving (SACE) requires precise control over key factors to overcome gas film instability and achieve reproducible optimal resolution and machining speed. This paper presents a substantial advancement in the SACE micromanufacturing technique by introducing a composite algorithm. This algorithm leverages deep learning and time series classification, employing a Temporal Convolutional Network (TCN) and a Long Short-Term Memory (LSTM) architecture for sequence-to-sequence intelligent classification. These classifiers are trained and optimized using Bayesian optimization, achieving impressive accuracies of 97.12% for TCN and 96.64% for LSTM. The algorithm utilizes TCN's superior performance to calculate derived parameters like gas film formation time, lifetime, mean discharge current and energy, and discharging frequency. Its versatility is demonstrated across various experimental conditions, showcasing its potential for rapid and accurate systematic studies. By highlighting the algorithm's applicability in real-time process control for SACE, this study establishes a foundation for future advancements in the field of glass micro manufacturing.

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Section: Related Workmentioning

confidence: 99%

Intelligent Characterization of Spark-Assisted Chemical Engraving (SACE) Process Using Time Series Classification

Sahebari,

Bassyouni,

Barari

et al. 2023

Preprint

Self Cite

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“…Electrochemical discharge machining, or spark-assisted chemical engraving (SACE), is a machining technology that hybridizes electrochemical machining and electrical discharge machining [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. The ECDM method can achieve the parallel processing of large-area vias arrays by customizing the working electrode, inheriting the high-cost effect and process flexibility of the EDM method.…”

Section: Through Glass Vias’ Formation and Metallization Techniquesmentioning

confidence: 99%

“…Vias with a diameter of 20 µm and a spacing of 60 µm can be fabricated on thin glass, while vias with a top diameter of 60 µm and bottom diameter of 40 µm can be produced on thick glass. Electrochemical discharge machining, or spark-assisted chemical engraving (SACE), is a machining technology that hybridizes electrochemical machining and electrical discharge machining [29][30][31][32][33][34][35][36][37][38][39]. The ECDM method can achieve the parallel processing of large-area vias arrays by customizing the working electrode, inheriting the high-cost effect and process flexibility of the EDM method.…”

Section: Electrochemical Discharge Machining (Ecdm)mentioning

confidence: 99%

Application of Through Glass Via (TGV) Technology for Sensors Manufacturing and Packaging

Yu,

Wu,

Zhong

et al. 2023

Sensors

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Glass has emerged as a highly versatile substrate for various sensor and MEMS packaging applications, including electromechanical, thermal, optical, biomedical, and RF devices, due to its exceptional properties such as high geometrical tolerances, outstanding heat and chemical resistance, excellent high-frequency electrical properties, and the ability to be hermetically sealed. In these applications, Through Glass Via (TGV) technology plays a vital role in manufacturing and packaging by creating electrical interconnections through glass substrates. This paper provides a comprehensive summary of the research progress in TGV fabrication along with its integrations, including through via formation and metallization. This paper also reviews the significant qualification and reliability achievements obtained by the scientific community for TGV technology. Additionally, this paper summarizes the application of TGV technology in various sensors such as MEMS sensors and discusses the potential applications and future development directions of TGV technology.

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A Multitasking Environment for Real-Time Monitoring of Discharging Activity During SACE Process Using LSTM

Seyedi Sahebari,

Barari,

Abou Ziki

2023

2023 15th IEEE International Conference on Industry Applications (INDUSCON)

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Neural Network Signal Processing in Spark Assisted Chemical Engraving (SACE) Micromachining

Cited by 4 publications

References 26 publications

Intelligent Characterization of Spark-Assisted Chemical Engraving (SACE) Process Using Time Series Classification

Intelligent Characterization of Spark-Assisted Chemical Engraving (SACE) Process Using Time Series Classification

Application of Through Glass Via (TGV) Technology for Sensors Manufacturing and Packaging

A Multitasking Environment for Real-Time Monitoring of Discharging Activity During SACE Process Using LSTM

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