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Intelligent Control Theory and Applications in Process Optimization and Smart...
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Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 11095

Special Issue Editors

Prof. Dr. Ming-Jong Tsai

E-Mail Website
Guest Editor
Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Interests: optomechatronics; intelligent theory applications; automated sensing; 3D printing control
Dr. Ricky Min-Fan Lee

E-Mail Website
Guest Editor
Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Interests: intelligent control theory; image-based servo control; deep learning; autonomous robotics

Special Issue Information

Dear Colleagues,

In recent years, with the rapid development of Industry 4.0, smart manufacturing has become an important issue in industrial production. In addition to the industrial Internet of Things, smart control and process optimization are actually key technologies for smart manufacturing. It is important that the system can respond to environmental changes in real time and operate efficiently. Among them, artificial intelligence is a more indispensable core element. Artificial intelligence theory, including neural networks, fuzzy theory, etc., has been widely used in various fields. In addition, machine learning, including deep learning, is an important tool to realize artificial intelligence. Artificial intelligence conducts intelligent analysis and makes optimal decisions based on the obtained data to establish intelligent production resource scheduling and product optimization functions, which can effectively improve production efficiency and promote manufacturing process to smart manufacturing. The demand for artificial intelligence technology development such as neural networks, fuzzy, and deep learning has grown rapidly in various fields. Therefore, this is the right time to pay attention to related research works in different fields that have used AI models and learning methods.

This Special Issue on “Intelligent Control Theory and Its Application in Process Optimization and Smart Manufacturing” welcomes the use of different types of research on intelligent theory to promote the development and application of intelligent control theory for process optimization and smart manufacturing in smart factories. Therefore, this issue aims to gather relevant research covering (but not limited to) the following topics:

  • AI model-based intelligent control;
  • AI model-based process optimization;
  • AI model-based smart manufacturing/material processing;
  • Intelligent control and manufacturing of traditional machining processes;
  • Intelligent control and manufacturing of non-traditional machining processes, such as 3D additive manufacturing, etc.;
  • Monitoring and control of smart factories.

Prof. Dr. Ming-Jong Tsai
Dr. Ricky Min-Fan Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Industry 4.0
  • Artificial Intelligence
  • deep learning
  • intelligent control
  • optimization
  • smart manufacturing
  • smart material processing

Published Papers (7 papers)

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Research

25 pages, 10137 KiB  
Article
Active Steering Controller for Driven Independently Rotating Wheelset Vehicles Based on Deep Reinforcement Learning
by Zhenggang Lu, Juyao Wei and Zehan Wang
Processes 2023, 11(9), 2677; https://0-doi-org.brum.beds.ac.uk/10.3390/pr11092677 - 06 Sep 2023
Cited by 1 | Viewed by 759
Abstract
This paper proposes an active steering controller for Driven Independently Rotating Wheelset (DIRW) vehicles based on deep reinforcement learning (DRL). For the two-axle railway vehicles equipped with Independently Rotating Wheelsets (IRWs), each wheel connected to a wheel-side motor, the Ape-X DDPG controller, an [...] Read more.
This paper proposes an active steering controller for Driven Independently Rotating Wheelset (DIRW) vehicles based on deep reinforcement learning (DRL). For the two-axle railway vehicles equipped with Independently Rotating Wheelsets (IRWs), each wheel connected to a wheel-side motor, the Ape-X DDPG controller, an enhanced version of the Deep Deterministic Policy Gradient (DDPG) algorithm, is adopted. Incorporating Distributed Prioritized Experience Replay (DPER), Ape-X DDPG trains neural network function approximators to obtain a data-driven DIRW active steering controller. This controller is utilized to control the input torque of each wheel, aiming to improve the steering capability of IRWs. Simulation results indicate that compared to the existing model-based H∞ control algorithm and data-driven DDPG control algorithm, the Ape-X DDPG active steering controller demonstrates better curving steering performance and centering ability in straight tracks across different running conditions and significantly reduces wheel–rail wear. To validate the proposed algorithm’s efficacy in real vehicles, a 1:5 scale model of the DIRW vehicle and its digital twin dynamic model were designed and manufactured. The proposed control algorithm was deployed on the scale vehicle and subjected to active steering control experiments on a scaled track. The experimental results reveal that under the active steering control of the Ape-X DDPG controller, the steering performance of the DIRW scale model on both straight and curved tracks is significantly enhanced. Full article
(This article belongs to the Special Issue Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing)
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27 pages, 3994 KiB  
Article
Efficient Multi-Objective Optimization on Dynamic Flexible Job Shop Scheduling Using Deep Reinforcement Learning Approach
by Zufa Wu, Hongbo Fan, Yimeng Sun and Manyu Peng
Processes 2023, 11(7), 2018; https://0-doi-org.brum.beds.ac.uk/10.3390/pr11072018 - 06 Jul 2023
Cited by 2 | Viewed by 1596
Abstract
Previous research focuses on approaches of deep reinforcement learning (DRL) to optimize diverse types of the single-objective dynamic flexible job shop scheduling problem (DFJSP), e.g., energy consumption, earliness and tardiness penalty and machine utilization rate, which gain many improvements in terms of objective [...] Read more.
Previous research focuses on approaches of deep reinforcement learning (DRL) to optimize diverse types of the single-objective dynamic flexible job shop scheduling problem (DFJSP), e.g., energy consumption, earliness and tardiness penalty and machine utilization rate, which gain many improvements in terms of objective metrics in comparison with metaheuristic algorithms such as GA (genetic algorithm) and dispatching rules such as MRT (most remaining time first). However, single-objective optimization in the job shop floor cannot satisfy the requirements of modern smart manufacturing systems, and the multiple-objective DFJSP has become mainstream and the core of intelligent workshops. A complex production environment in a real-world factory causes scheduling entities to have sophisticated characteristics, e.g., a job’s non-uniform processing time, uncertainty of the operation number and restraint of the due time, avoidance of the single machine’s prolonged slack time as well as overweight load, which make a method of the combination of dispatching rules in DRL brought up to adapt to the manufacturing environment at different rescheduling points and accumulate maximum rewards for a global optimum. In our work, we apply the structure of a dual layer DDQN (DLDDQN) to solve the DFJSP in real time with new job arrivals, and two objectives are optimized simultaneously, i.e., the minimization of the delay time sum and makespan. The framework includes two layers (agents): the higher one is named as a goal selector, which utilizes DDQN as a function approximator for selecting one reward form from six proposed ones that embody the two optimization objectives, while the lower one, called an actuator, utilizes DDQN to decide on an optimal rule that has a maximum Q value. The generated benchmark instances trained in our framework converged perfectly, and the comparative experiments validated the superiority and generality of the proposed DLDDQN. Full article
(This article belongs to the Special Issue Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing)
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21 pages, 6560 KiB  
Article
Development and Numerical Optimization of a System of Integrated Agents for Serial Production Lines
by Hisham Alkhalefah, Usama Umer, Mustufa Haider Abidi and Ahmed Elkaseer
Processes 2023, 11(5), 1578; https://0-doi-org.brum.beds.ac.uk/10.3390/pr11051578 - 22 May 2023
Viewed by 939
Abstract
In modern high-volume industries, the serial production line (SPL) is of growing importance due to the inexorable increase in the complexity of manufacturing systems and the associated production costs. Optimal decisions regarding buffer size and the selection of components when designing and implementing [...] Read more.
In modern high-volume industries, the serial production line (SPL) is of growing importance due to the inexorable increase in the complexity of manufacturing systems and the associated production costs. Optimal decisions regarding buffer size and the selection of components when designing and implementing an SPL can be difficult, often requiring complex analytical models, which can be difficult to conceive and construct. Here, we propose a model to evaluate and optimize the design of an SPL, integrating numerical simulation with artificial intelligence (AI). Numerous studies relating to the design of SPL systems have been published, but few have considered the simultaneous consideration of a number of decision variables. Indeed, the authors have been unable to locate in the published literature even one work that integrated the selection of components with the optimization of buffer sizes into a single framework. In this research, a System of Integrated Agents Numerical Optimization (SIGN) is developed by which the SPL design can be optimized. A SIGN consists of a components selection system and a decision support system. A SIGN aids the selection of machine tools, buffer sizes, and robots via the integration of AI and simulations. Using a purpose-developed interface, a user inputs the appropriate SPL parameters and settings, selects the decision-making and optimization techniques to use, and then displays output results. It will be implemented in open-source software to broaden the impact of the SIGN and extend its influence in industry and academia. It is expected that the results of this research project will significantly influence open-source manufacturing system design and, consequently, industrial and economic development. Full article
(This article belongs to the Special Issue Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing)
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27 pages, 7216 KiB  
Article
Continuous Reactor Temperature Control with Optimized PID Parameters Based on Improved Sparrow Algorithm
by Mingsan Ouyang, Yipeng Wang, Fan Wu and Yi Lin
Processes 2023, 11(5), 1302; https://0-doi-org.brum.beds.ac.uk/10.3390/pr11051302 - 22 Apr 2023
Cited by 3 | Viewed by 1346
Abstract
To address the problems of strong coupling and large hysteresis in the temperature control of a continuously stirred tank reactor (CSTR) process, an improved sparrow search algorithm (ISSA) is proposed to optimize the PID parameters. The improvement aims to solve the problems of [...] Read more.
To address the problems of strong coupling and large hysteresis in the temperature control of a continuously stirred tank reactor (CSTR) process, an improved sparrow search algorithm (ISSA) is proposed to optimize the PID parameters. The improvement aims to solve the problems of population diversity reduction and easy-to-fall-into local optimal solutions when the traditional sparrow algorithm is close to the global optimum. This differs from other improved algorithms by adding a new Gauss Cauchy mutation strategy at the end of each iteration without increasing the time complexity of the algorithm. By introducing tent mapping in the sparrow algorithm to initialize the population, the population diversity and global search ability are improved; the golden partition coefficient is introduced in the explorer position update process to expand the search space and balance the relationship between search and exploitation; the Gauss Cauchy mutation strategy is used to enhance the ability of local minimum value search and jumping out of local optimum. Compared with the four existing classical algorithms, ISSA has improved the convergence speed, global search ability and the ability to jump out of local optimum. The proposed algorithm is combined with PID control to design an ISSA-PID temperature controller, which is simulated on a continuous reactor temperature model identified by modeling. The results show that the proposed method improves the transient and steady-state performance of the reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a semi-physical experimental platform to verify its feasibility. Full article
(This article belongs to the Special Issue Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing)
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18 pages, 6119 KiB  
Article
A Hybrid Fault Diagnosis Approach Using FEM Optimized Sensor Positioning and Machine Learning
by Sang Jin Jung, Tanvir Alam Shifat and Jang-Wook Hur
Processes 2022, 10(10), 1919; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10101919 - 22 Sep 2022
Viewed by 1255
Abstract
Sensor acquired signal has been a fundamental measure in rotary machinery condition monitoring (CM) to enhance system reliability and stability. Inappropriate sensor mounting can lead to loss of fault-related information and generate false alarms in industrial systems. To ensure reliable system operation, in [...] Read more.
Sensor acquired signal has been a fundamental measure in rotary machinery condition monitoring (CM) to enhance system reliability and stability. Inappropriate sensor mounting can lead to loss of fault-related information and generate false alarms in industrial systems. To ensure reliable system operation, in this paper we investigate a system’s multiple degrees-of-freedom (DOF) using the finite element method (FEM) to find the optimum sensor mounting position. An appropriate sensor position is obtained by the highest degree of deformation in FEM modal analysis. The effectiveness of the proper sensor mounting position was compared with two other sensor mounting points, which were selected arbitrarily. To validate the effectiveness of this method we considered a gear-actuator test bench, where the sensors were mounted in the same place as the FEM simulation. Vibration data were acquired through these sensors for different health states of the system and failure patterns were recognized using an artificial neural network (ANN) model. An ANN model shows that the optimum sensor mounting point found in FEM has the highest accuracy, compared to other mounting points. A hybrid CM framework, combining the physics-based and data-driven approaches, provides robust fault detection and identification analysis of the gear-actuator system. Full article
(This article belongs to the Special Issue Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing)
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9 pages, 2509 KiB  
Article
Traffic Flow Speed Prediction in Overhead Transport Systems for Semiconductor Fabrication Using Dense-UNet
by Young Ha Joo, Hoonseok Park, Haejoong Kim, Ri Choe, Younkook Kang and Jae-Yoon Jung
Processes 2022, 10(8), 1580; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10081580 - 11 Aug 2022
Viewed by 1402
Abstract
To improve semiconductor productivity, efficient operation of the overhead hoist transport (OHT) system, which is an automatic wafer transfer device in a semiconductor fabrication plant (“fab”), is very important. A large amount of data is being generated in real time on the production [...] Read more.
To improve semiconductor productivity, efficient operation of the overhead hoist transport (OHT) system, which is an automatic wafer transfer device in a semiconductor fabrication plant (“fab”), is very important. A large amount of data is being generated in real time on the production line through the recent production plan of a smart factory. This data can be used to increase productivity, which in turn enables companies to increase their production efficiency. In this study, for the efficient operation of the OHT, the problem of OHT congestion prediction in the fab is addressed. In particular, the prediction of the OHT transport time was performed by training the deep convolutional neural network (CNN) using the layout image. The data obtained from the simulation of the fab and the actual logistics schedule data of a Korean semiconductor factory were used. The data obtained for each time unit included statistics on volume and speed. In the experiment, a layout image was created and used based on the statistics. The experiment was conducted using only the layout image without any other feature extraction, and it was shown that congestion prediction in the fab is effective. Full article
(This article belongs to the Special Issue Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing)
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16 pages, 910 KiB  
Article
Exploring Key Decisive Factors in Manufacturing Strategies in the Adoption of Industry 4.0 by Using the Fuzzy DEMATEL Method
by Fawaz M. Abdullah, Abdulrahman M. Al-Ahmari and Saqib Anwar
Processes 2022, 10(5), 987; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10050987 - 16 May 2022
Cited by 10 | Viewed by 2466
Abstract
Globalization has created a highly competitive and diverse market, an uncertain and risky business environment, and changing customer expectations. An effective manufacturing strategy reduces complexity and provides organizations with a well-organized manufacturing structure. However, existing research on manufacturing strategies appears scattered, lacking systematic [...] Read more.
Globalization has created a highly competitive and diverse market, an uncertain and risky business environment, and changing customer expectations. An effective manufacturing strategy reduces complexity and provides organizations with a well-organized manufacturing structure. However, existing research on manufacturing strategies appears scattered, lacking systematic understanding and finding no causal relationship between manufacturing strategies’ outputs (MSOs) and their importance. Therefore, this study is a pioneer in identifying the influential factors of MSOs in the adoption of Industry 4.0 (I4.0) technologies utilizing the decision-making trial and evaluation laboratory (DEMATEL) approach. This method is considered an effective method for identifying the cause-effect relationship of complex problems. It evaluates interdependent relationships among MSO factors from the perspective of academic and industry experts. Identifying cause and effect factors leads to increasing the market’s competitiveness and prioritizing them. To deal with the vagueness of human beings’ perceptions, this study utilizes fuzzy set theory and the DEMATEL method to form a structural model. Results show that customer satisfaction, cost per unit produced, and the number of advanced features are the main factors influencing MSOs. Full article
(This article belongs to the Special Issue Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing)
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