A Knowledge-Based Approach for Microwire Casting Plant Control

Zaporojan, Sergiu; Plotnic, C.; Calmicov, Igor; Larin, V.

doi:10.4018/978-1-4666-5125-8.ch069

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Glass-coated microwires can be engineered to possess high ductility [2], electric conductivity, magnetic bistability [3], and can be used in ceramic-matrix composites [4] and biomedical materials [3]. Applications of microwires include improvements in sensor technology, semiconductor packaging, high precision miniaturized electrical motors, aerospace and medical applications, and energy storage for solar and battery cells [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Microwire Casting Process Through Convolutional Neural Networks

Li¹,

Gulak

Zhao

2023

Lecture Notes in Mechanical Engineering

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Even though the Taylor-Ulitovsky process for producing microwire has existed and been widely used in the past century, there are various challenges facing the microwire manufacturing process, such as inconsistent wire diameter, constant breaks of microwire during fabrication, and the difficulty of producing wires with a smaller diameter. These challenges can make the microwire fabrication process inefficient, and this research aims to understand how thermal images from the fabrication process under various parameter settings can be used to assess and classify the quality of the microwire. Thermal videos and other process variables were collected from a microwire manufacturing lab, and the thermal image datasets from the video were trained using a pretrained Convolutional Neural Networks (CNN) in order to better understand how changing certain parameters for the microwire manufacturing process can affect the microwire quality. The features extracted from the thermal images using the proposed CNNbased machine learning algorithm is capable of classifying the microwire fabrication process into four stages, i.e., initialization stage, stable stage 1, stable stage 2, and ending stage. The stage classification accuracy reveals high repeatability and performance from the proposed CNN model. The results are promising since manufacturing process parameter settings can be adjusted and optimized by referring to thermal image characteristics, and therefore the CNN model can improve microwire quality and predict failure of the microwire.

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Section: Introductionmentioning

confidence: 99%

Understanding the Microwire Casting Process Through Convolutional Neural Networks

Li¹,

Gulak

Zhao

2023

Lecture Notes in Mechanical Engineering

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“…Intelligent user interfaces are part of production systems and socio-technical production systems, which would successfully unite people with the cyber and physical world for mutual understanding [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A Brief Overview of Intelligent Interfaces in Production Systems

Istrati¹

2022

Proceedings of the 12th International Conference on “Electronics, Communications and Computing"

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This paper presents a state of the art in field of intelligent user interfaces, which are the main elements that make the interaction between humans and machines in the most efficient, natural, effective way. As an interdisciplinary product, the design of user interfaces represents the combination of models from disciplines like software engineering, artificial intelligence, human-computer interaction and other disciplines (sociology, psychology, etc.). In the process of design and operation of production systems, an important factor is its responsiveness and the ability to have integrated the capacity to change over time and functionally. To achieve a high level of structural configuration and feasibility, a production system must have a compatible interface.

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Understanding the Microwire Casting Process through Convolutional Neural Networks

Li¹,

Gulak²,

Zhao³

2022

Preprint

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<p>Even though the Taylor-Ulitovsky process for producing microwire has existed and been widely used in the past century, there are various challenges facing the microwire manufacturing process, such as inconsistent wire diameter, constant breaks of microwire during fabrication, and the difficulty of producing wires with a smaller diameter. These challenges can make the microwire fabri- cation process inefficient, and this research aims to understand how thermal im- ages from the fabrication process under various parameter settings can be used to assess and classify the quality of the microwire. Thermal videos and other process variables were collected from a microwire manufacturing lab, and the thermal image datasets from the video were trained using a pretrained Convolutional Neural Networks (CNN) in order to better understand how changing certain pa- rameters for the microwire manufacturing process can affect the microwire qual- ity. The features extracted from the thermal images using the proposed CNN- based machine learning algorithm is capable of classifying the microwire fabri- cation process into four stages, i.e., initialization stage, stable stage 1, stable stage 2, and ending stage. The stage classification accuracy reveals high repeatability and performance from the proposed CNN model. The results are promising since manufacturing process parameter settings can be adjusted and optimized by re- ferring to thermal image characteristics, and therefore the CNN model can im- prove microwire quality and predict failure of the microwire. </p>

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A Knowledge-Based Approach for Microwire Casting Plant Control

Cited by 4 publications

References 6 publications

Understanding the Microwire Casting Process Through Convolutional Neural Networks

Understanding the Microwire Casting Process Through Convolutional Neural Networks

A Brief Overview of Intelligent Interfaces in Production Systems

Understanding the Microwire Casting Process through Convolutional Neural Networks

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