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A Review of Advanced Reactive Materials in Fiber Sensor Technology: Challenges and Requirements of Precise Humidity control in Mechanochemical Treatment process

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sensors AND MATERIALS 2025年第1期37卷 41-61页

作者： Riza, Muhammad Arif Go, Yun ii Zaitizila, Ismail Kok, Soo Ping Heriot Watt Univ Malaysia 1 Sch Engn & Phys Sci Jalan Venna P5-2 Precinct 5 Putrajaya 62200 Malaysia

In this comprehensive review, we explore the intersection of reactive materials in fiber sensor technology and the critical role of humidity control in mechanochemical treatment processes. We examine recent advancements in embedded sensor technologies, focusing on their applications in various treatment processes across industries. We highlight the development of metal oxide nanomaterials as effective optoelectronic humidity sensors, discussing their fabrication, characterization, and operational principles. In this review, we also delve into the emerging field of wearable chemical sensors, emphasizing their potential in healthcare monitoring and environmental applications. Furthermore, we address the integration of embedded sensors in Industry 4.0 frameworks, illustrating their impact on factory automation, process optimization, and sustainable manufacturing. We underscore the importance of humidity control in mechanochemical treatments, linking it to the broader context of sensor technology applications. By unifying current research and identifying future trends, we provide valuable insights into the synergies between reactive materials, fiber sensor technology, and humidity control, offering a foundation for further innovations in treatment process enhancement and efficiency.

关键词： sensitive industry moisture temperature

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A deep mixed-effects modeling approach for real-time monitoring of metal additive manufacturing process

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iiSE TRANSACTIONS 2024年第9期56卷 945-959页

作者： Xu, Ruiyu Huang, Song Song, Zheren Gao, Yuanyuan Wu, Jianguo Peking Univ Dept Ind Engn & Management Beijing Peoples R China

In-situ process monitoring of additive manufacturing (AM) is critically important and has been attracting significant efforts to achieve desirable process reliability and quality consistency. Despite enormous progress in embedding various sensors into the AM system, effectively fusing these sensor data for anomaly detection is very challenging, due to complex relationship among sensing signals, process condition and environment. In this article we propose a deep mixed-effects modeling approach to monitor the melt pool temperature for anomaly detection. It consists of a deep neural network (DNN) capturing the relationship between the temperature and other sensing data, a random-effect term accounting for the random variation of mean temperature, and a residual term modeling potential autocorrelations. To train the model (Phase I), an efficient hard expectation-maximization (EM) algorithm is developed, which iteratively optimizes the DNN parameters and the ones for the random-effect and residual terms. In online monitoring (Phase ii), a T2-based and a generalized likelihood ratio (GLR) test-based control charts are developed to timely detect the process anomalies. The asymptotic behaviors of both the T2 and GLR statistics are further established. The effectiveness of the proposed approach is demonstrated through thorough simulation study and real case study of an AM process.

关键词： Additive manufacturing mixed-effects model deep neural network hard EM profile monitoring

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Application of sensors in Flexible process control of Intelligent manufacturing 39

Application of Sensors in Flexible Process Control of Intell...

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39th Youth Academic Annual conference of Chinese-Association-of-Automation (YAC)

作者： Qiu, Jianxiong He, Zhaoshui Yan, Cheng Zhou, Hailong Guangdong Univ Technol Guangzhou Peoples R China Guangdong Univ Technol Sch Automat Key Lab Informat Proc & Syst Integrat IoT Minist Educ Guangzhou Peoples R China Intelligent Mfg Engn Technol Res Ctr Huizhou Met Cutting Proc Unit Huizhou Peoples R China Tianjin Bonuo Intelligent Robot Technol Co Ltd Tianjin Peoples R China

ISBN: (纸本)9798350390780;9798350379228

In order to carry out the intelligent production of typical chemical products for the application of order-based flexible production system, based on intelligent measurement and control technology, a new generation of information technology such as industrial Internet, intelligent management and control, data visualization, and informatization is integrated. In this paper, we study the establishment of an order-based flexible production process with definable configuration according to the intelligent processing mode of process automation. After selecting the sensor reasonably, Configuration of the industrial intelligent detection system, System signal processing and digitalization, Industrial information network construction and debugging, Programming operation and debugging of intelligent measurement and control systems, Production tasks such as visualization and remote monitoring of the production process. The study found,The sensor plays a vital role in the control system control of the DCS -based controller, Liquid level sensors and flow/weight sensors and other key sensors can monitor various parameters in the production process in real time, provide accurate data support for the control system to ensure the accuracy and stability of the ingredient system. And realize the flexible ingredients controlled by program control, and then make the feeding at the same time to improve production efficiency. This research provides a strong technical guarantee for actual production, and provides a feasible implementation method for our intelligent manufacturing process industrial control.

关键词： Sensor process control Visualization DCS control

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sensors for in-process and on-machine monitoring of machining operations

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CIRP JOURNAL OF manufacturing SCIENCE AND TECHNOLOGY 2024年 51卷 263-292页

作者： Shokrani, Alborz Dogan, Hakan Burian, David Nwabueze, Tobechukwu D. Kolar, Petr Liao, Zhirong Sadek, Ahmad Teti, Roberto Wang, Peng Pavel, Radu Schmitz, Tony Univ Bath Dept Mech Engn Bath England Czech Tech Univ Fac Mech Engn Res Ctr Mfg Technol Dept Prod Machines & Equipment Prague Czech Republic Univ Tennessee Mech Aerosp & Biomed Engn Knoxville TN USA Univ Nottingham Fac Engn Machining & Condit Monitoring Grp Nottingham NG7 2RD England Natl Res Council Canada Aerosp Mfg Technol Ctr Montreal PQ Canada Univ Napoli Feder II Dept Chem Mat & Ind Prod Engn DICMAPI Naples Italy Univ Kentucky Dept Mech Engn Lexington KY USA TechSolve Inc Cincinnati OH 45237 USA

Machining is extensively used for producing functional parts in various industries such as aerospace, automotive, energy, etc. There is a growing demand for improved part quality and performance at lower costs from increasingly difficult-to-machine materials. Whilst modern machine tools are equipped with sensors for closed loop control of their axes' movements and position, they provide minimal information regarding the cutting performance and tool condition. The integration of additional sensors into cutting tools, machine tools and/or their components can provide an insight into the machining performance. It also provides an opportunity to improve the machining process and reduce the need for post-process inspection and rework. This paper presents a comprehensive analysis of various sensors utilised for in-process and on-machine measurement and monitoring of machining performance parameters such as cutting forces, vibrations, tool wear, surface integrity, etc. Data transfer and communication methods, as well as power supply options for sensor-integrated systems are also investigated. Sensor integrated machining systems can potentially improve machining performance and part quality by early detection of errors and damages, maximising tool usage and preventing machining and tool wear induced damages. A combination of sensor data collection and intelligent sensor signal processing can further increase the capabilities of sensor integrated systems from process monitoring to active process control.

关键词： Machining sensors Monitoring

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Employing a multi-resonance microwave sensor for in-line moisture monitoring of fluidized bed agglomeration

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CHEMICAL ENGINEERING JOURNAL 2024年 499卷

作者： Stoeckl, Gero Atxutegi, Aitor Bellinghausen, Stefan Heinrich, Stefan Hamburg Univ Technol TUHH Inst Solids Proc Engn & Particle Technol Denickestr 15 D-21073 Hamburg Germany Siemens Hammersmith Grove 26-28 London W6 7HA England

Amorphous substances are relatively common in food industry and their characteristics depend highly on the moisture content. process parameters are adjusted based on online measurements to control the product quality of the agglomerates. This leads to two major challenges: precise moisture content measurements of amorphous particles at (i) different temperatures and for (ii) a wide moisture content range. In this study two workflows (inline fluidized bed and conditioned fixed bed) calibration of microwave sensors are compared and it is shown that for fluidized beds inline calibration is necessary to achieve accurate moisture measurements. The application of the novel microwave sensor MW 4200 working at resonance frequencies of 2.3 and 5.6 GHz is described. The temperature influence on the measurement is investigated with measurements of a fixed particle bed. Rapid, reliable in-process moisture control at a wide range of moisture contents and temperatures for agglomeration in fluidized beds, with a mean relative accuracy of 82 %, is achieved. This technology improves productivity and precision in industrial operations, minimizes waste and optimizes energy usage.

关键词： process monitoring Fluidized bed Agglomeration process optimization Sustainable manufacturing

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Data quality evaluation for smart multi-sensor process monitoring using data fusion and machine learning algorithms

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PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT 2023年第2期17卷 197-210页

作者： Segreto, Tiziana Teti, Roberto Univ Naples Federico II Dept Chem Mat & Ind Prod Engn Ple Tecchio 80 I-80125 Naples Italy Fraunhofer Joint Lab Excellence Adv Prod Technol Naples Italy

Condition monitoring and control of manufacturing processes are among the key issues for the development of smart factories. The employment of multiple sensor systems for manufacturing process monitoring typically involves the detection and collection of a huge volume of heterogeneous data acquired by sensors of different nature. Smart monitoring implementation requires valuable sensorial data from multiple sensors to achieve high prediction performance in the decision making phase. Data fusion techniques can be employed to combine multiple data sources in order to generate more accurate and reliable information for decision making aimed at achieving the automatic identification of machine, tool, and part failures. This paper focuses on sensorial data quality evaluation using data fusion techniques and machine learning algorithms for intelligent multiple sensor monitoring of manufacturing processes. For this purpose, a sensorial data set derived from an experimental campaign of Inconel 718 machining was subjected to different fusion techniques in order to find the best data combination for accurate classification and recognition purposes.

关键词： Smart factory manufacturing processes Sensor monitoring Data fusion Machine learning

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Transformer-enabled generative adversarial imputation network with selective generation (SGT-GAIN) for missing region imputation

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iiSE TRANSACTIONS 2024年第9期56卷 975-987页

作者： Li, Yuxuan Shi, Zhangyue Liu, Chenang Oklahoma State Univ Sch Ind Engn & Management Stillwater OK 74078 USA

Although data have been extensively leveraged for process monitoring and control in advanced manufacturing, it still suffers from the connection issues among sensors, machines, and computers, which may lead to significant data loss, i.e., missing region in the collected data, in the application of data-driven monitoring. To address the missing region issues, one popular way is to perform missing data imputation. With the advances of machine learning, many approaches have been developed for the missing data imputation, such as the popular Generative Adversarial Imputation Network (GAIN), which is based on the Generative Adversarial Network (GAN). However, the inherent shortcomings of generative adversarial architecture may still lead to unstable training. More importantly, the collected online sensor data in manufacturing are in sequential order whereas GAIN considered the input data independently. Hence, to address these two limitations, this work proposes a novel approach termed transformer-enabled GAIN with selective generation (SGT-GAIN). The contributions of the proposed SGT-GAIN consist of three aspects: (i) the architecture for transformer-enabled generation is developed to capture the sequential information among the data;(ii) a selective multi-generation framework is proposed to further reduce the imputation bias;and (iii) an ensemble learning framework is applied to enhance the imputation robustness. Both the numerical simulation study and a real-world case study in additive manufacturing demonstrated the effectiveness of the proposed SGT-GAIN.

关键词： Advanced manufacturing generative adversarial imputation network (GAIN) missing region imputation selective generation transformer

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Photonic sensors-assisted ML pipeline for Precise control of Curing process in Advanced Thermosetting Composite manufacturing 8

Photonic Sensors-assisted ML pipeline for Precise Control of...

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conference on Optical Sensing and Detection ViiI

作者： Kyriazi, Evrydiki Petsinis, Georgios Poulopoulos, Ioannis Syriopoulos, Georgios Prousalidi, Thenia Neale, Geoffrey Asareh, Mehdi Toumasis, Panagiotis Zervos, Charalampos Skordos, Alexandros A. Avramopoulos, Hercules Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece Talos Analyt IKE Athens Greece Cranfield Univ Sch Aerosp Transport & Mfg Cranfield MK43 0AL Beds England

ISBN: (纸本)9781510673175;9781510673168

This study introduces an innovative approach to enhance the utilization of carbon fiber thermosetting composites in advanced structural engineering by addressing the challenges of high manufacturing costs and limited production rates. We develop, deploy and test an ML pipeline utilizing PIC-based sensors (SOI technology, 220 nm thick, fabricated at IMEC's MPW). They are based on a Bragg structure, packaged using ball lenses and suitable for operating at 180 degrees Celsius and 5 bar pressure. The focus is on accurately predicting two crucial parameters: Cure time and Temperature Overshoot, vital for determining the process duration and part quality. Using advanced tools and sensors, this study achieves a high prediction accuracy of 98% in millisecond scale while effectively handling the outliers. The ML pipeline allows the real-time process optimization of manufacturing process, minimizing the cost, and providing insights into the quality of the composite part through the in-depth monitoring of the process.

关键词： Machine learning algorithms PIC-based sensors Bragg grating structure optimization of manufacturing process predictive algorithms ML pipeline process monitoring XGBoost regressor

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Enhancing Aluminum Casting Efficiency through Real-Time Optical Fiber Sensor Monitoring at the Metal-Mold Interface 3

Enhancing Aluminum Casting Efficiency through Real-Time Opti...

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conference on Optical Waveguide and Laser sensors iiI

作者： Zhang, Bohong Hungund, Abhishek Prakash O'Malley, Ronald J. Bartlett, Laura Mumtaz, Farhan Gerald, Rex E., ii Huang, Jie Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Mat Sci & Engn Rolla MO 65409 USA

ISBN: (纸本)9781510674073;9781510674066

This study focuses on the critical aspect of interfacial heat transfer during the solidification process in metal casting, aiming to optimize these manufacturing processes. Fiber-optic sensors were employed to provide continuous real-time monitoring of mold gaps and temperature profiles during the solidification of A356 aluminum in a permanent mold-casting environment. A specially designed mold system, constructed from unheated, uncoated tool steel, facilitated the seamless integration of these advanced fiber-optic sensors. One key technique used was the Extrinsic Fabry-Perot interferometric (EFPI) sensor, which uniquely utilized molten metal as the second reflection interface for measuring mold gaps. This method yielded impressively accurate results, with a maximum error of just 2 mu m compared to physical measurements. Additionally, using the Rayleigh backscattering (RBS) technique, a stainless steel-encased fiber provided real-time temperature measurements with an impressive spatial resolution of 0.65 mm. The study demonstrates that combining high-resolution temperature profiles with gap evolution measurements significantly enhances our understanding of heat transfer dynamics at the mold-metal interface, proving particularly beneficial for optimizing complex-shaped castings and continuous casting processes. Furthermore, the capability to monitor the shape of the casting in real-time as it exits a continuous casting mold introduces a novel tool for quality control and process safety improvement by early detection of conditions that might lead to slab cracking and breakouts, ultimately enhancing overall process efficiency and reliability.

关键词： Extrinsic Fabry-Perot Interferometer Rayleigh Backscattering Optical Frequency-Domain Reflectometry Optical Fiber sensors Gap Measurement Distributed Temperature Measurement Aluminum Casting Molten Metal

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Design and Research of PDC Bit Temperature control System 2

Design and Research of PDC Bit Temperature Control System

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2nd IEEE International conference on sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Wei, Lihong Honra, Jaime Manufacturing and Energy Engineering School of Mechanical Mapúa University Manila1002 Philippines Leshan Vocational and Technical College School of Intelligent Manufacturing Institute Leshan614000 China

ISBN: (纸本)9798350373646

At present, PDC bits are still brazed by artificial flame, and the welding quality is not stable enough. In order to solve this problem, a new type of intelligent temperature control device is developed by combining the brazing technology of double loop PID controller and PDC bit. A multifunctional integrated control method based on embedded real-time operating system RT-Thread is proposed. PID control and layered control are used to detect, control and display the welding process. Experiments show that the device can solve the welding temperature problem of PDC bit. © 2024 IEEE.

关键词： Brazing

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