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Mathematics
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Mathematical Techniques and New ITs for Smart Manufacturing Systems
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Mathematical Techniques and New ITs for Smart Manufacturing Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 2613

Special Issue Editors

Dr. Kai Ding

E-Mail Website
Guest Editor
Institute of Smart Manufacturing Systems, Chang'an University, Xi'an, China
Interests: smart manufacturing; cyber-physical production system; Industrial Internet of Things (IIoT); digital twin
Prof. Dr. Felix T. S. Chan

E-Mail Website
Guest Editor
Department of Decision Sciences, School of Business, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao
Interests: engineering management; logistics; supply chain management; production management systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart manufacturing, as the theme of Industry 4.0, has drawn much attention from both academics and practitioners. Mathematical techniques (e.g., algebra, probability and statistics, programming, matrix operations, queueing theory) and new information technologies (e.g., digital twin, cloud-edge computing, deep learning, blockchain) have empowered manufacturing systems into smart systems. Considering a manufacturing system covers design, simulation, production planning, monitoring, scheduling, optimization, quality assurance, lean logistics, human–robot collaboration, and many other domains, there is still much work to discuss on the mathematical techniques and new IT-driven configuration and operations management of smart manufacturing systems. This Special Issue welcomes cutting-edge technologies and multi-disciplinary research with respect to the above domains, in the form of technical papers or review papers. 

Topics include but are not limited to the following:

  • Mathematical modeling of smart manufacturing systems.
  • Mathematical optimization for smart manufacturing management
  • Digital twin-based simulation and optimization of smart manufacturing systems.
  • Computational intelligence for production operations management.
  • Mathematical optimization approaches for production control.
  • AI-based decision support systems for smart manufacturing systems.
  • Energy-efficient additive manufacturing prediction.
  • Cloud-edge interplay-based IIoT platform for smart manufacturing.
  • Blockchain applications in collaborative manufacturing networks and supply chains.
  • Industrial knowledge graph applications in smart manufacturing systems.

Dr. Kai Ding
Prof. Dr. Felix T. S. Chan
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • smart manufacturing system
  • cyber-physical production system
  • smart production management
  • 3D printing and additive manufacturing
  • low carbon manufacturing
  • mathematical planning
  • statistical learning
  • industrial internet of things
  • cloud-edge interplay
  • digital twins
  • deep learning
  • blockchain

Published Papers (3 papers)

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Research

31 pages, 6086 KiB  
Article
A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process
by Longlong He, Ruiyu Pan, Yafei Wang, Jiani Gao, Tianze Xu, Naqi Zhang, Yue Wu and Xuhui Zhang
Mathematics 2024, 12(7), 1109; https://doi.org/10.3390/math12071109 - 07 Apr 2024
Viewed by 423
Abstract
In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks [...] Read more.
In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks (BN) and preliminary hazard analysis (PHA). Firstly, the fault tree model of CMTS was transformed into a risk Bayesian network, and the inference results of the fault tree and Bayesian network were integrated to identify the key risk factors in the transportation system. Subsequently, based on the preliminary hazard analysis of these key risk factors, corresponding rectification measures and a risk control system construction plan are proposed. Finally, a case study was carried out on the X coal mine as a pilot mine to verify the feasibility of the method. The application of this method effectively identifies and evaluates potential risk factors in CMTS, providing a scientific basis for accident prevention. This research holds significant importance for the safety management and decision making of coal mine enterprises during the process of intelligent transformation and is expected to provide strong support for enhancing the safety and reliability of CMTS. Full article
(This article belongs to the Special Issue Mathematical Techniques and New ITs for Smart Manufacturing Systems)
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23 pages, 6082 KiB  
Article
Dynamic Optimization Method of Knowledge Graph Entity Relations for Smart Maintenance of Cantilever Roadheaders
by Yan Wang, Yuepan Liu, Kai Ding, Shirui Wei, Xuhui Zhang and Youjun Zhao
Mathematics 2023, 11(23), 4833; https://doi.org/10.3390/math11234833 - 30 Nov 2023
Cited by 1 | Viewed by 668
Abstract
The fault maintenance scenario in coal-mine equipment intelligence is composed of videos, images, signals, and repair process records. Text data are not the primary data that reflect the fault phenomenon, but rather the secondary processing based on operation experience. Focusing on the difficulty [...] Read more.
The fault maintenance scenario in coal-mine equipment intelligence is composed of videos, images, signals, and repair process records. Text data are not the primary data that reflect the fault phenomenon, but rather the secondary processing based on operation experience. Focusing on the difficulty of extracting fault knowledge from the limited textual maintenance process records, a forward static full-connected topology network modeling method based on domain knowledge from four dimensions of physical structure, internal association, condition monitoring, and fault maintenance, is proposed to increase the efficiency of constructing a fault-maintenance knowledge graph. Accurately identifying the intrinsic correlation between the equipment anomalies and the faults’ causes through only domain knowledge and loosely coupled data is difficult. Based on the static full-connected knowledge graph of the cantilever roadheader, the information entropy and density-based DBSCAN clustering algorithm is used to process and analyze many condition-monitoring historical datasets to optimize the entity relationships between the fault phenomena and causes. The improved DBSCAN algorithm consists of three stages: firstly, extracting entity data related to fault information from the static fully connected graph; secondly, calculating the information entropy based on the real dataset describing the fault information and the historical operating condition, respectively; and thirdly, comparing the entropy values of the entities and analyzing the intrinsic relationship between the fault phenomenon, the operating condition data, and the fault causes. Based on the static full-connected topology storage in the Neo4j database, the information entropy and density-based DBSCAN algorithm is computed by using Python to identify the relationship weights and dynamically display optimized knowledge graph topology. Finally, an example of EBZ200-type cantilever roadheader for smart maintenance is studied to analyze and evaluate the forward and four-mainlines knowledge graph modeling method and the dynamic entity relations optimization method for static full-connected knowledge graph. Full article
(This article belongs to the Special Issue Mathematical Techniques and New ITs for Smart Manufacturing Systems)
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19 pages, 9149 KiB  
Article
A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies
by Fengtian Chang, Guanghui Zhou, Kai Ding, Jintao Li, Yanzhen Jing, Jizhuang Hui and Chao Zhang
Mathematics 2023, 11(22), 4570; https://doi.org/10.3390/math11224570 - 07 Nov 2023
Viewed by 947
Abstract
Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these [...] Read more.
Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these challenges. However, poor detection results or process interpretations are still unaddressed key issues. To bridge the gap, this paper takes the automotive bodies as objects, and proposes a resistance spot welding quality online detection method with dynamic current and resistance data based on a combined convolutional neural network (CNN), long short-term memory network (LSTM), and an attention mechanism. First, an overall online detection framework using an edge–cloud collaboration was proposed. Second, an online quality detection model was established. In it, the combined CNN and LSTM network were used to extract local detail features and temporal correlation features of the data. The attention mechanism was introduced to improve the interpretability of the model. Moreover, the imbalanced data problem was also solved with a multiclass imbalance algorithm and weighted cross-entropy loss function. Finally, an experimental verification and analysis were conducted. The results show that the quality detection accuracy was 98.5%. The proposed method has good detection performance and real-time detection abilities for the in-site welding processes of automobile bodies. Full article
(This article belongs to the Special Issue Mathematical Techniques and New ITs for Smart Manufacturing Systems)
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