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Processes
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Process Automation and Smart Manufacturing in Industry 4.0/5.0
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Process Automation and Smart Manufacturing in Industry 4.0/5.0

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

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 2276

Special Issue Editor

Dr. Sergey Y. Yurish

E-Mail Website
Guest Editor
International Frequency Sensor Association (IFSA), 08860 Castelldefels, Spain
Interests: smart sensors; optical sensors; frequency measurements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global Industry 4.0 market was valued at USD 102.94 billion in the year 2022 and is projected to reach a value of USD 433.84 billion by the year 2030. The global market is expected to exhibit a compound annual growth rate (CAGR) of 19.70 % over the forecast period.

Industry 4.0/5.0 is an integrated system, which consists of an automation tool, robotic control, communications and big data analytics. The increased adoption of industrial robots is one of the main driving factors of this market, while the data risks associated with integration of advanced technologies are the restraining factors.

This Special Issue, entitled “Process Automation and Smart Manufacturing in Industry 4.0/5.0”, contains extended papers selected from the 4th IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0/5.0 (ARCI 2024), 7–9 February 2024, Innsbruck, Austria.

Topics include (but are not limited to):

  • Process automation;
  • Process control and monitoring;
  • Design principles in Industry 4.0/5.0;
  • Smart manufacturing and technologies;
  • Smart factories;
  • Machine learning and artificial intelligence in manufacturing;
  • Chemical process control;
  • Industrial big data and analytics;
  • Digital production and virtual engineering.

Dr. Sergey Y. Yurish
Guest Editor

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 control
  • automation
  • smart factory
  • smart manufacturing
  • virtual engineering

Published Papers (2 papers)

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Research

23 pages, 2459 KiB  
Article
An Algorithm for Optimizing the Process Parameters of the Spindle Process of Universal CNC Machine Tools Based on the Most Probable Explanation of Bayesian Networks
by Liyue Zhang, Haoran Liu, Niantai Wang, Yuhua Qin and Enping Chen
Processes 2023, 11(11), 3099; https://doi.org/10.3390/pr11113099 - 28 Oct 2023
Viewed by 716
Abstract
As an essential component of a universal CNC machine tool, the spindle plays a critical role in determining the accuracy of machining parts. The three cutting process parameters (cutting speed, feed speed, and cutting depth) are the most important optimization input parameters for [...] Read more.
As an essential component of a universal CNC machine tool, the spindle plays a critical role in determining the accuracy of machining parts. The three cutting process parameters (cutting speed, feed speed, and cutting depth) are the most important optimization input parameters for studying process optimization. Better processing quality is often achieved through their optimization. Therefore, it is necessary to study the three cutting process parameters of the CNC machine tool spindle. In this paper, we proposed an improved algorithm incorporated with the beetle antennae search algorithm for the most probable explanation in Bayesian networks to achieve optimization calculation of process parameters. This work focuses on building adaptive dynamic step parameters to improve detection behavior. The chaotic strategy is discretized and used to establish the dominant initial population during the population initialization. This article uses four standard network data sets to compare the time and fitness values based on the improved algorithm. The experimental results show that the proposed algorithm is superior in time and accuracy compared to similar algorithms. At the same time, an optimization example for the actual machining of a universal CNC machine tool spindle was provided. Through the optimization of this algorithm, the true machining quality was improved. Full article
(This article belongs to the Special Issue Process Automation and Smart Manufacturing in Industry 4.0/5.0)
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15 pages, 2916 KiB  
Article
Research on Optimization Algorithm of AGV Scheduling for Intelligent Manufacturing Company: Taking the Machining Shop as an Example
by Chao Wu, Yongmao Xiao and Xiaoyong Zhu
Processes 2023, 11(9), 2606; https://doi.org/10.3390/pr11092606 - 31 Aug 2023
Cited by 1 | Viewed by 1177
Abstract
Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation [...] Read more.
Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration; secondly, the Laplace distribution is introduced to regulate the volatilization of the pheromone in the pheromone updating of the ant colony algorithm, which speeds up the speed of convergence; the path obtained by the ant colony algorithm is subjected to the deletion of the bi-directional redundant nodes, which enhances the path smoothing degree; and finally, the improved ant colony algorithm is fused with the improved dynamic window algorithm, so as to enable the robots to arrive at the end point safely. Simulation shows that in the same map environment, the ant colony algorithm compared with the basic ant colony algorithm reduces the path length by 40% to 67% compared to the basic ant colony algorithm and reduces the path inflection points by 34% to 60%, which is more suitable for complex environments. It also verifies the feasibility and superiority of the conflict-free path optimization strategy in solving the production scheduling problem of the flexible machining operation shop. Full article
(This article belongs to the Special Issue Process Automation and Smart Manufacturing in Industry 4.0/5.0)
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