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2nd IEEE International Conference on Measurement, Instrumentation, control and automation, ICMICA 2023

作者： Babu, G. Dora Chandramohan Goud, Ediga Peravali, Swapna Engineering SGPR Government Polytechnic Applied Electronics and Instrumentation Kurnool India Cvr College of Engineering Department of Electronics and Instrumentation Hyderabad India AU College of Engineering Instrument Technology Visakhapatnam India

ISBN: (纸本)9798350355048

The paper proposes a controller for higher order SISO delayed time processes which employs an identical open loop transfer function that is required for an integrating plus time delay transfer function model. The proposed technique's controller settings depends on the tuning factor and process model parameters. The closed-loop responses of numerous simulated case studies are studied. When compared to existing strategies, the proposed method provides better closed-loop performance for both set point and regulatory situations. Evaluation metrics such as integral error, stability margins, and sensitivity peak are compared, and it is discovered that improved responses are obtained using the proposed simple method, which has straightforward mathematical relations of process parameters for controller design. The proposed method has been verified experimentally on a higher order ratio process and demonstrates better performance. © 2024 IEEE.

关键词： Time delay

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Optimizing UPVC profile production using adaptive neuro-fuzzy inference system

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International Journal of Information technology (Singapore) 2024年 1-18页

作者： Naghipour, Avaz Salehpour, Arash Iranag, Behnam Safiri Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

This paper explores the implementation of an Adaptive Neuro-Fuzzy Inference System to optimize Unplasticized Polyvinyl Chloride profile production. Given the intrinsic complexities of polymer extrusion, such as maintaining consistent quality amidst varying raw material properties and environmental conditions, traditional control methods like Proportional-Integral-Derivative (PID) controllers often need to catch up in adapting to non-linear production environments. This study proposes an ANFIS-based control system that leverages the adaptability of neural networks and the human-like reasoning of fuzzy logic to address these challenges effectively. The system uses real-world data from expert operators to refine its control parameters, ensuring accurate regulation of critical variables such as extrusion temperature, feed material composition, and ambient conditions. By testing the system against traditional control methods, the research demonstrates significant improvements in quality control, efficiency, and waste reduction. The ANFIS system’s adaptability allows it to minimize errors and adjust dynamically to variations in production conditions, outperforming traditional PID systems in maintaining consistent quality. Furthermore, the study outlines the implications of this research for intelligent manufacturing within Industry 4.0 frameworks, highlighting how ANFIS can integrate seamlessly with existing manufacturing systems to optimize production. This paper ultimately contributes to the growing literature advocating for intelligent control systems in polymer extrusion. It underscores their potential to transform manufacturing efficiency and quality in Unplasticized Polyvinyl Chloride production. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Adaptive neuro-fuzzy inference system (ANFIS) Fuzzy logic controller Manufacturing optimization Neural networks Polymer extrusion process UPVC profile production

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Influence of engagement parameters on estimation of tangential cutting force using high-frequency process data of the machine tool 16

Influence of engagement parameters on estimation of tangenti...

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16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022

作者： Rytir, Michal Rönnecke, Jonas Hänel, Albrecht Kolar, Petr Ihlenfeldt, Steffen Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Department of Industrial Production and Automation Jugoslávských partyzánu 1580/3 Prague160 00 Czech Republic Fraunhofer Institute for Machine Tools and Forming Technology IWU Department of Production Digital Twin Nöthnitzer Straße 44 Dresden Germany Technische Universität Dresden Institute of Mechatronic Engineering Chair of Machine Tools Development and Adaptive Controls Dresden01062 Germany

The machining of high-value products needs advanced quality control to avoid the risk of expensive follow-up operations performed on parts with unacceptable quality. This situation requires the in-process or close to in-process control of the machining operation. The tangential cutting force is a parameter that characterizes the force interaction between the specific workpiece material and the specific tool geometry. This paper presents a method for a tangential cutting force calculation along the given tool path based on processing of the high-frequency data acquired from the machine tool control system with support of the geometric material removal model. The calculated data are compared with the reference dynamometer results. The influence of the variable engagement parameters is demonstrated and discussed from the perspective of the in-process workpiece quality control. © 2023 Elsevier B.V.. All rights reserved.

关键词： Machine tools

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Static Characteristics of the Anaerobic Digestion of Organic Wastes with production of Hydrogen and Methane, Including Substrate Inhibition Influence

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International Journal Bioautomation 2024年第4期28卷 197-204页

作者： Chorukova, Elena Kabaivanova, Lyudmila Department of Biotechnology The Stephan Angeloff Institute of Microbiology Bulgarian Academy of Sciences Acad. G. Bonchev Str. Bl. 26 Sofia1113 Bulgaria

In this paper, static characteristics of a simple mathematical model of a two-stage anaerobic digestion (AD) process for sequential production of hydrogen (Н2) and methane (СН4) are derived. The influence of substrate inhibition in the first and second bioreactors is considered. Different process variables are described in the considered mathematical model. The concentration of the influent organic matter is assumed to be the main external disturbance reflected in the model. The obtained input-output static characteristics for the energy carriers (H2 and CH4) could be used to control and optimize the subjected process. © 2024 by the authors.

关键词： Anaerobic digestion

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An MDP-based Method for Dynamic Workforce Allocation in Bernoulli Serial production Lines 19

An MDP-based Method for Dynamic Workforce Allocation in Bern...

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19th IEEE International Conference on automation Science and Engineering, CASE 2023

作者： Tu, Jiachen Zhang, Liang University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States

ISBN: (纸本)9798350320695

With the development of Industry 4.0 in manufacturing, new technologies such as big data analytics, artificial intelligence, and cloud computing, have been widely deployed to production practice for performance evaluation and analysis. These technologies enabled fast and accurate production decision-making on the factory floor. To further support these activities, it's essential to develop real-time automated decision-making mechanisms backed by rigorous control and optimization analytics. The focus of this paper is on enhancing the throughput of production processes through the control and optimization of the floating workforce resources on the factory floor. Specifically, we consider serial production lines with finite buffers and machines characterized by the Bernoulli reliability model. Additionally, we assume that multiple shared workforce units can be allocated dynamically during production via real-time production bottleneck identification and mitigation in order to improve steady-state throughput. This paper addresses this problem for three and four-machine line cases, where two shared workforce units are dynamically allocated among the machines. An optimal control policy is derived based on the Markov decision process (MDP) approach. Numerical experiments are carried out to demonstrate the efficacy of the proposed method. © 2023 IEEE.

关键词： Data Analytics

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A Method for Predicting the Temperature of Steel Billet Coming Out of Soaking Furnace Based on Deep Belief Neural Network 2

A Method for Predicting the Temperature of Steel Billet Comi...

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2nd IEEE International Conference on control, Electronics and Computer technology, ICCECT 2024

作者： Yan, Su Xia, Xuhui Hunan Valin Lianyuan Steel Co. Ltd. Production Management Department Hunan China Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Wuhan China

ISBN: (纸本)9798350380958

Accurately predicting the billet discharge temperature through the process parameters of the even-heat furnace is the key to improving the billet rolling quality and reducing the energy consumption of the even-heat furnace, and it is also a major problem faced by the academia and the industry at present. Aiming at this problem, combined with the powerful feature learning and representation capability of deep belief neural network, a deep belief neural network-based billet temperature prediction method for even-heat furnace is proposed to realize the complex nonlinear mapping between the process parameters of even-heat furnace and the billet temperature, and the accuracy and superiority of the algorithms proposed in this paper are verified through the comparison and analysis of the actual production data and existing prediction methods. © 2024 IEEE.

关键词： Forecasting

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Stochastic Event-Triggered Estimation With Smart Sensors Over Packet-Dropping Links

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IEEE Transactions on control of Network Systems 2025年第2期12卷 1756-1768页

作者： Deng, Yahan Yang, Nachuan Li, Yuzhe Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay SAR Hong Kong999077 Hong Kong

The event-triggered scheme (ETS) has been widely used for sensor data scheduling in cyber-physical systems (CPS). Existing literature on the design of ETSs for packet drops deals with the issue of non-Gaussianity of the a posteriori distribution in the system state. On the one hand, the Gaussian assumption only derives an approximate result, while on the other hand, exact results can be obtained by numerical integration but with excessive computational complexity. To this end, in this paper, we propose a stochastic ETS based on acknowledgment (ACK) information for remote state estimation with smart sensors and packet drops. The transmission decision is jointly driven by the holding time at the remote end and the accumulated innovative information. Then, we inductively derive the exact probability density function of the augmented innovative information vector by the Bayesian rule, which is used to obtain the explicit form of the estimation error covariance. These exact theoretical results mean that the design of scheduling parameter sequences no longer relies on experience. Finally, numerical simulations are provided to demonstrate that the empirical results agree with the theoretical results. © 2014 IEEE.

关键词： Packet loss

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File Formats and Digital Data Transfer for Dental Needs 8th

File Formats and Digital Data Transfer for Dental Needs

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8th International Congress on Information and Communication technology, ICICT 2023

作者： Pavlova, Diana Dovramadjiev, Tihomir Daskalov, Dimo Dimova, Rozalina Mrugalska, Beata Dentaprime Dental Clinic Varna Bulgaria Department of Industrial Design Faculty of Shipbuilding Technical University of Varna Varna Bulgaria Department of Communication Engineering and Technologies Faculty of Computer Sciences and Automation Technical University of Varna Varna Bulgaria Faculty of Engineering Management Poznan University of Technology Poznan60-965 Poland

ISBN: (纸本)9789819932351

The use of CAD/CAM in dentistry makes it possible to optimize the treatment process in case of a violation of parts of the orofacial complex. It contributes to the rationalization of the production process of the dental prosthetic structure by transferring file formats between different software programs for design, design visualization, simulation, and post-production through an additive or subtractive process. This electronic process has the ability to store larger amounts of medical data with different formats and transfer them between different software. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Dentistry

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Dynamic Productivity Prediction and New production Feature Selection Methods for Advanced Planning Scheduling

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Journal of Information Science and Engineering 2024年第2期40卷 341-357页

作者： Tsai, Ming-Fong Li, Wei-Tse Chen, Lien-Wu Department of Electronic Engineering National United University Miaoli360302 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

Smart manufacturing is an important research field that is associated with production planning and scheduling, the Internet of Things and artificial intelligence technologies. production lines use advanced planning and scheduling systems for production operations, time forecasting and planning;integrated manufacturing execution systems are used to collect real-time production information via the Internet of Things to strengthen scheduling control;and artificial intelligence machine learning technology is used to perform predictive maintenance to achieve high-accuracy planning and scheduling. Advanced planning and scheduling systems use genetic algorithms for planning with the aim of increasing speed and accuracy, and the integration of real-time production information from manufacturing execution systems and dynamic adjustments to shift planning are important issues in smart manufacturing. A traditional cyber-physical system integrates historical and real-time production information and carries out a machine learning analysis to improve the production scheduling efficiency, but the prediction of production times for new product orders is a topic that needs further research. This paper proposes new methods of dynamic productivity prediction and new production feature selection, with the aim of improving the performance of advanced planning and scheduling systems. A genetic ant colony algorithm is used to predict dynamic productivity based on real-time production information, to reduce the error between production time plans and actual operations. Historical production information is analysed, and the best correlation coefficient is used in new production feature selection, in order to reduce the discrepancy between production productivity forecasts and actual results. Our proposed dynamic productivity prediction method can reduce the error by at least 1.5% compared with other schemes in the literature, while the proposed production feature selection method can reduce

关键词： Forecasting

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Design and Analysis of Individually Demagnetized Power Converter for Switched Reluctance Motors

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8439-8449页

作者： Liu, Hemiao Chen, Hao Wang, Xing Gorbounov, Yassen Wheeler, Patrick Anuchin, Alecksey Demidova, Galina Korovkin, Nikolay Liudmila, Sakhno Shenzhen Research Institute China University of Mining and Technology Shenzhen515100 China China University of Mining and Technology School of Electrical Engineering Xuzhou221116 China Xinjiang Institute of Engineering Ürümqi830000 China International Joint Research Center of Central and Eastern European Countries on New Energy Electric Vehicle Technology and Equipment Xuzhou221008 China International Cooperation Joint Laboratory of New Energy Power Generation and Electric Vehicles of Jiangsu Province Colleges and Universities Xuzhou221008 China Xuzhou Key Laboratory of New Energy Electric Vehicle Technology and Equipment Xuzhou221008 China Mining Production University of Mining and Geology "St. Ivan Rilski " Department of Automation Sofia1700 Bulgaria The University of Nottingham Power Electronics Machines and Control Group NottinghamNG7 2RD United Kingdom Electric Drives Department Moscow111250 Russia Saint-Petersburg National Research University of Information Technologies Mechanics and Optics Faculty of Control Systems and Robotics Engineer Saint Petersburg197101 Russia Peter the Great St. Petersburg Polytechnic University Institute of Energy Saint Petersburg195251 Russia

Switched reluctance motors (SRMs) are capable of operating at high speeds and high loads. The large back EMF results in the excitation winding current being demagnetized more slowly, which, in turn, reduces the efficiency and output of the motor system. This study proposes a novel individually demagnetization power converter (IDPC) specifically designed for SRMs to effectively use the demagnetization energy through an independent demagnetization circuit. Compared with traditional converters, the IDPC can accept direct current (dc) and high-frequency ac inputs while achieving fast demagnetization and energy recovery. The topology and working principle of the IDPC are analyzed in detail in this article using a three-phase 12/8 SRM as the research object. In addition, the influence of the winding demagnetization voltage on the negative torque during the demagnetization phase was studied, as well as the functional implementation and logic control strategy of the IDPC. Simulation and experimental results confirm the validity of the proposed topology and high-frequency ac drive characteristics, as well as their advantages in terms of efficiency, output capacity, and dynamic performance. © 2015 IEEE.

关键词： Reluctance motors

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