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Processes
Special Issues
Monitoring, Optimization, Control and Artificial Intelligence in Manufacturing System
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Monitoring, Optimization, Control and Artificial Intelligence in Manufacturing System

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

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 5115

Special Issue Editors

Dr. Jong-Ho Shin

E-Mail Website
Guest Editor
Department of Industrial Engineering, Chosun University, Gwangju, 61452, Republic of Korea
Interests: artificial intelligence in manufacturing systems; PHM; smart factory
Special Issues, Collections and Topics in MDPI journals
Dr. Duck Young Kim

E-Mail Website
Guest Editor
Department of Industrial & Management Engineering, POSTECH, Pohang 37673, Korea
Interests: factory control; factory analytics; factory design

Special Issue Information

Dear Colleagues,

The new era of the 4th industrial revolution is approaching swiftly thanks to the rapid development of information technology (IT), big data, artificial intelligence (AI), and numerous other related advances. The core concept of the 4th industrial revolution can be represented as cyberphysical systems (CPS) such that, in this regard, manufacturing systems are also evolving as a form of digital transformation. Many sensors and data communication networks have helped the transformation of manufacturing systems into CPS. More data is collected in this environment, and the collected data provides new opportunities to improve conventional manufacturing processes by making them more efficient and intelligent. This change in the amount of collectable data requires new methodologies for monitoring, optimizing, controlling manufacturing systems. In addition, the application of big data and AI in manufacturing systems is resulting in new breakthroughs for the future of factories. However, the revolution is still ongoing and under development, necessitating further research. Hence, this Special Issue will focus on new developments in manufacturing systems in terms of monitoring, optimization, control, and artificial intelligence.

Dr. Jong-Ho Shin
Dr. Duck Young Kim
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

  • process monitoring
  • process optimization
  • process control
  • artificial intelligence
  • manufacturing system

Published Papers (2 papers)

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Research

21 pages, 3351 KiB  
Article
Speed Control of Wheeled Mobile Robot by Nature-Inspired Social Spider Algorithm-Based PID Controller
by Huma Khan, Shahida Khatoon, Prerna Gaur, Mohamed Abbas, Chanduveetil Ahamed Saleel and Sher Afghan Khan
Processes 2023, 11(4), 1202; https://0-doi-org.brum.beds.ac.uk/10.3390/pr11041202 - 13 Apr 2023
Cited by 4 | Viewed by 2694
Abstract
Mobile robot is an automatic vehicle with wheels that can be moved automatically from one place to another. A motor is built in its wheels for mobility purposes, which is controlled using a controller. DC motor speed is controlled by the proportional integral [...] Read more.
Mobile robot is an automatic vehicle with wheels that can be moved automatically from one place to another. A motor is built in its wheels for mobility purposes, which is controlled using a controller. DC motor speed is controlled by the proportional integral derivative (PID) controller. Kinematic modeling is used in our work to understand the mechanical behavior of robots for designing the appropriate mobile robots. Right and left wheel velocity and direction are calculated by using the kinematic modeling, and the kinematic modeling is given to the PID controller to gain the output. Motor speed is controlled by the PID low-level controller for the robot mobility; the speed controlling is done using the constant values Kd, Kp, and Ki which depend on the past, future, and present errors. For better control performance, the integral gain, differential gain, and proportional gain are adjusted by the PID controller. Robot speed may vary by changing the direction of the vehicle, so to avoid this the Social Spider Optimization (SSO) algorithm is used in PID controllers. PID controller parameter tuning is hard by using separate algorithms, so the parameters are tuned by the SSO algorithm which is a novel nature-inspired algorithm. The main goal of this paper is to demonstrate the effectiveness of the proposed approach in achieving precise speed control of the robot, particularly in the presence of disturbances and uncertainties. Full article
(This article belongs to the Special Issue Monitoring, Optimization, Control and Artificial Intelligence in Manufacturing System)
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17 pages, 2294 KiB  
Article
Computational Experience with Piecewise Linear Relaxations for Petroleum Refinery Planning
by Zaid Ashraf Rana, Cheng Seong Khor and Haslinda Zabiri
Processes 2021, 9(9), 1624; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9091624 - 09 Sep 2021
Cited by 2 | Viewed by 1846
Abstract
Refinery planning optimization is a challenging problem as regards handling the nonconvex bilinearity, mainly due to pooling operations in processes such as crude oil distillation and product blending. This work investigated the performance of several representative piecewise linear (or piecewise affine) relaxation schemes [...] Read more.
Refinery planning optimization is a challenging problem as regards handling the nonconvex bilinearity, mainly due to pooling operations in processes such as crude oil distillation and product blending. This work investigated the performance of several representative piecewise linear (or piecewise affine) relaxation schemes (referred to as McCormick, bm, nf5, and nf6t) and de (which is a new approach proposed based on eigenvector decomposition) that mainly give rise to mixed-integer optimization programs to convexify a bilinear term using predetermined univariate partitioning for instances of uniform and non-uniform partition sizes. The computational results showed that applying these schemes improves the relaxation tightness compared to only applying convex and concave envelopes as estimators. Uniform partition sizes typically perform better in terms of relaxation solution quality and convergence behavior. It was also seen that there is a limit on the number of partitions that contribute to relaxation tightness, which does not necessarily correspond to a larger number of partitions, while a direct relationship between relaxation size and tightness does not always hold for non-uniform partition sizes. Full article
(This article belongs to the Special Issue Monitoring, Optimization, Control and Artificial Intelligence in Manufacturing System)
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