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Applied Sciences
Special Issues
Manufacturing IoT and Manufacturing Big Data
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Manufacturing IoT and Manufacturing Big Data

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2856

Special Issue Editors

Prof. Dr. Haisong Huang

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Guest Editor
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
Interests: intelligent manufacturing; manufacturing big data and manufacturing information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Sai Hong Tang

E-Mail Website
Guest Editor
Associate Professor, Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia
Interests: mechanical and system engineering; operation research and robotics
Dr. Wei Qin

E-Mail Website
Guest Editor
Associate Professor, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: complex system: modeling, control and optimization; machine intelligence: AGI, SI, EA, MAS, etc.; smart manufacturing
Dr. Jianan Wei

E-Mail Website
Guest Editor
Key Laboratory of Modern Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
Interests: manufacturing big data and manufacturing information systems; intelligent manufacturing; machine learning; deep transfer learning; fault diagnosis; imbalanced data processing and predictive maintenance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on “Manufacturing IoT and Manufacturing Big Data”. The Special Issue aims to provide a high-level international publishing platform for experts and scholars to report the newest developments and latest practice in manufacturing IoT, manufacturing big data, smart manufacturing, and industrial engineering and logistics. The Special Issue will focus on the state-of-the-art theories and technologies of manufacturing IoT, big data for smart manufacturing, digital-twin-driving intelligent manufacturing, industrial engineering and intelligent logistics (exploring how the emerging technologies utilize the concept of smart production), and innovative design and service in manufacturing, including but not limited to the following topics:

  • Intelligent manufacturing method systems for industrial big data, manufacturing big data analysis models and algorithms (e.g., imbalanced learning, few-shot learning, positive-unlabeled (PU) learning, zero-shot learning, and modeling methods under various operating conditions), new generation of artificial intelligence methods, etc.;
  • Manufacturing big data perception, manufacturing big data integration, manufacturing big data modeling, CPS technology, manufacturing data platforms, edge computing, etc.;
  • Intelligent product design, intelligent workshop analysis and optimization, intelligent product operation and maintenance, PHM and predictive maintenance, digital-twin-driving intelligent manufacturing, intelligent logistics, etc.;
  • Industrial digitalization and digital industrialization: models, algorithms, and scenarios of big data and real economy, key technologies for marketization of data elements, etc.

Prof. Dr. Haisong Huang
Dr. Sai Hong Tang
Dr. Wei Qin
Dr. Jianan Wei
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. Applied Sciences 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 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.

Published Papers (2 papers)

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Research

28 pages, 14441 KiB  
Article
IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
by Manuel José Rodríguez Aguilar, Ismael Abad Cardiel and José Antonio Cerrada Somolinos
Appl. Sci. 2024, 14(1), 323; https://doi.org/10.3390/app14010323 - 29 Dec 2023
Viewed by 909
Abstract
Production lines face numerous challenges to meet market demands, including constant changes in products that require continuous adjustments. Efficient and rapid reconfiguration and adaptation of production processes are crucial. In cases of inadequate adaptation, bottlenecks can arise due to human errors or incorrect [...] Read more.
Production lines face numerous challenges to meet market demands, including constant changes in products that require continuous adjustments. Efficient and rapid reconfiguration and adaptation of production processes are crucial. In cases of inadequate adaptation, bottlenecks can arise due to human errors or incorrect configurations, often introducing complexity in pinpointing the root cause and resulting in financial losses. Furthermore, improper machine maintenance contributes to this situation as well. This article seeks to establish a framework grounded in the contemporary smart factory, the IIoT, the Industry 4.0 paradigm, and Big Data. The proposed system places emphasis on leveraging real-time data analysis for predicting risks, while concurrently conducting a thorough analysis of historical data to monitor trends and enhance bottleneck identification. The defined architecture operates across multiple levels, acquiring real-time information and generating historical data for training and continuous optimization. Predictive results contribute to decision-making and assist in mitigating bottlenecks in manufacturing lines. Full article
(This article belongs to the Special Issue Manufacturing IoT and Manufacturing Big Data)
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15 pages, 759 KiB  
Article
Identification of Product Innovation Path Incorporating the FOS and BERTopic Model from the Perspective of Invalid Patents
by Dingtang Zhang, Xuan Wu, Peng Liu, Hao Qin and Wei Zhou
Appl. Sci. 2023, 13(13), 7987; https://doi.org/10.3390/app13137987 - 07 Jul 2023
Cited by 1 | Viewed by 1307
Abstract
Under the premise of resource constraint, it is crucial to identify the product innovation opportunities contained in failed patents through external search in order to compensate for the shortcomings of enterprises’ own technology. Due to the cost of patent research and development and [...] Read more.
Under the premise of resource constraint, it is crucial to identify the product innovation opportunities contained in failed patents through external search in order to compensate for the shortcomings of enterprises’ own technology. Due to the cost of patent research and development and the risk of infringement, this paper constructs a product innovation identification path that integrates the FOS and BERTopic model from the perspective of invalid patents. The path consists of three stages, including the identification of the problem to be solved by the product based on functional analysis, the extraction of the subject matter elements based on the core failed patent technology, and the generation and evaluation of innovative solutions based on TRIZ theory and the best- worst method (BWM). Finally, the feasibility of the path constructed in this paper is verified by taking a slurry pump as an example. The application results show that the product innovation identification path constructed in this paper can provide theoretical support for enterprises to carry out technological innovation activities efficiently. Full article
(This article belongs to the Special Issue Manufacturing IoT and Manufacturing Big Data)
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