Smart Product Design and Manufacturing on Industrial Internet

Topic Information

Dear Colleagues,

In recent years, artificial intelligence and big data technologies, powered with Industrial Internet and web environment, have had huge impacts on research and development activities concerning product design and manufacturing fields. It is obvious that Industrial-Internet-based paradigms, systems, key enabling technologies, models, and algorithms for smart product design and manufacturing are playing an important role in making Industry 4.0/5.0 a reality. Topics associated with keywords like smart product design, smart manufacturing, product maintenance and services, robots and actuators, big data and intelligent algorithms, etc., in the context of Industrial Internet and web environment, often constitute the focus of both academic and industrial sectors. It is necessary to collect key contributions from the above fields, sum up the current progress on both academic research and industrial practices and present them to the public.

On the basis of the reasons mentioned above, this Special Topic Collection aims to explore a wide range of issues related to the product design and manufacturing with artificial intelligence and big data on Industrial Internet and web environment. Potential authors can feel free to involve any group journal as the host of their manuscripts. We welcome original research articles, reviews, short communication, and technical notes. Research areas include (but are not limited to) the following topics:

Smart Product Design:

• Fundamentals, such as Industrial-Internet-based paradigms, architectures, and systems; computational geometry and graphics issues in product design; datasets and deep learning for 3D shapes and product semantics, representation learning for 3D shapes and product semantics, and intelligent generative and retrieval models for product design including text2shape, voice2shape, sketch2shape, image2shape, and graph2shape; topology optimization, finite element analysis and optimization, bond graphs, and Modelica for dynamic issues in design; etc.
• Methods and practices, such as key enabling technologies, case study, and industrial scenarios and applications on Industrial Internet and web environment; data-driven and intelligent conceptual design; design for X, intelligent parameterized CAD, product service system design, product mass customization design, product platform and modular design; intelligent computational design, generative design especially for additive manufacturing; crowdsourcing design; Kansei engineering and emotional computing in product design; smart electromechanical system design, materials design; next-generation CAD software models on web; etc.

Smart Manufacturing:

• Fundamentals, such as Industrial-Internet-based paradigms, architectures, and systems related to service-oriented manufacturing, social manufacturing, cloud manufacturing, networked collaborative manufacturing, digital manufacturing, mass customization, on-demand manufacturing, and shared manufacturing; issues with manufacturing interactions and collaborations; intelligent and interconnected equipment, protocols, and sensor networks; human factors within the context of Industry 4.0/5.0; blockchain technologies; low carbon and social impacts in manufacturing; meta verse and VR/AR in manufacturing; etc.
• Methods and practices, such as key enabling technologies, case study, and industrial scenarios and applications on Industrial Internet and web environment; intelligent factory and production lines; intelligent assembly; smart equipment modeling and control; smart process technologies and experience representations; IOT and sensing/measuring networks, devices on web and manufacturing data sampling, CPS/CPSS, and digital twins; production planning and scheduling; APS/MES/DCS; intelligent process monitoring and quality control; materials processing and logistics, intelligent inventory; manufacturing performance analysis and optimization; sustainable production supply chain; new CAPP/CAM/APS/MES/DCS software models on the Internet or web; etc.

Smart Product Maintenance and Services:

• Fundamentals, such as Industrial-Internet-based paradigms, architectures, and systems related to maintenance service principles, MRO lifecycle, and product service systems; maintenance service flow modeling and scheduling; quality of services evaluation; etc.
• Methods and practices, such as key enabling technologies, case study, and industrial scenarios and applications on Industrial Internet and web environment; IoT and sensing networks, product CPS/CPSS, and digital twins; smart remote monitoring and performance prediction of product usages; product fault diagnosis and executive reliability, product predictive maintenance, and MRO modeling; smart configuration and running for product service systems, service workflow control and management, etc.

Smart Robots and Actuators:

• Fundamentals, such as Industrial-Internet-based paradigms, architectures, and systems related to robot-driven manufacturing and maintenance, robotic actuators; actuators in smart manufacturing and control systems; robot and actuator thinking; human–robot/actuator interactions; etc.
• Methods and practices, such as key enabling technologies, case study, and industrial scenarios and applications on Industrial Internet and web environment; robots and actuators on web; smart robot grasping, cognitive robots for manufacturing and maintenance, and robot path planning and scheduling; smart control for robots and actuators; etc.

Big Data and Intelligent Algorithms:

• AI-related datasets, models and algorithms, computing powers for product design, manufacturing, maintenance and services, and robots and actuators, such as feature engineering and prompt engineering with special industrial fields; symbol-based AI including production rules, frames, ontology, knowledge graphs, CBR, and decision-making; computing-driven AI including neural networks, machine learning, deep learning, and causal inference; NLP, large language models (like chatGPT), and multi-modal models; swarm intelligence, multi-agents, and collective intelligence; learning algorithms including federated learning, transfer learning, reinforcement learning, representation learning, and few-shot learning; etc.
• Big data for product design, manufacturing, maintenance and services, and robots and actuators, such as big data analytics, data flow processing, data cleaning, big data visualization, etc.

Please note that authors can submit their papers to this Special Topic Collection at any time. Papers will be published online immediately after their acceptance and without delays caused by whether all paper collections are ready.

We look forward to hearing from you.

Prof. Dr. Pingyu Jiang
Prof. Dr. Jihong Liu
Prof. Dr. Ying Liu
Prof. Dr. Jihong Yan
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Actuators actuators	2.2	3.9	2012	16.5 Days	CHF 2400	Submit
Algorithms algorithms	1.8	4.1	2008	15 Days	CHF 1600	Submit
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	18 Days	CHF 1800	Submit
Future Internet futureinternet	2.8	7.1	2009	13.1 Days	CHF 1600	Submit
Journal of Manufacturing and Materials Processing jmmp	3.3	5.1	2017	14.7 Days	CHF 1800	Submit
Machines machines	2.1	3.0	2013	15.6 Days	CHF 2400	Submit
Robotics robotics	2.9	6.7	2012	17.7 Days	CHF 1800	Submit
Systems systems	2.3	2.8	2013	17.3 Days	CHF 2400	Submit

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Published Papers (4 papers)

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All Actuators Algorithms BDCC Future Internet JMMP Machines Robotics Systems

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18 pages, 20092 KiB  

Open AccessArticle

Multi-Source Data Fusion for Vehicle Maintenance Project Prediction

by Fanghua Chen, Deguang Shang, Gang Zhou, Ke Ye and Guofang Wu

Future Internet 2024, 16(10), 371; https://doi.org/10.3390/fi16100371 - 14 Oct 2024

Viewed by 528

Abstract

Ensuring road safety is heavily reliant on the effective maintenance of vehicles. Accurate predictions of maintenance requirements can substantially reduce ownership costs for vehicle owners. Consequently, this field has attracted increasing attention from researchers in recent years. However, existing studies primarily focus on [...] Read more.

Ensuring road safety is heavily reliant on the effective maintenance of vehicles. Accurate predictions of maintenance requirements can substantially reduce ownership costs for vehicle owners. Consequently, this field has attracted increasing attention from researchers in recent years. However, existing studies primarily focus on predicting a limited number of maintenance needs, predominantly based solely on vehicle mileage and driving time. This approach often falls short, as it does not comprehensively monitor the overall health condition of vehicles, thus posing potential safety risks. To address this issue, we propose a deep fusion network model that utilizes multi-source data, including vehicle maintenance record data and vehicle base information data, to provide comprehensive predictions for vehicle maintenance projects. To capture the relationships among various maintenance projects, we create a correlation representation using the maintenance project co-occurrence matrix. Furthermore, building on the correlation representation, we propose a deep fusion network that employs the attention mechanism to efficiently merge vehicle mileage and vehicle base information. Experiments conducted on real data demonstrate the superior performance of our proposed model relative to competitive baseline models in predicting vehicle maintenance projects. Full article

(This article belongs to the Topic Smart Product Design and Manufacturing on Industrial Internet)

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17 pages, 4612 KiB  

Open AccessArticle

A Novel Kind of Knowledge Graph Construction Method for Intelligent Machine as a Service Modeling

by Yuhao Liu, Jiayuan Han, Peng Yan, Biyao Li, Maolin Yang and Pingyu Jiang

Machines 2024, 12(10), 723; https://doi.org/10.3390/machines12100723 - 12 Oct 2024

Viewed by 608

Abstract

With the development of Intelligent Machine as a Service (IMaaS), devices increasingly require personalization, intelligence, and service orientation, making resource modeling a key challenge. Knowledge graph (KG) technology, known for unifying heterogeneous data, has become an essential tool for modeling and analyzing manufacturing [...] Read more.

With the development of Intelligent Machine as a Service (IMaaS), devices increasingly require personalization, intelligence, and service orientation, making resource modeling a key challenge. Knowledge graph (KG) technology, known for unifying heterogeneous data, has become an essential tool for modeling and analyzing manufacturing resources. On this basis, this study proposes a novel resource KG construction method for IMaaS. First, an E-R diagram is used to divide the constant and variable entities and set the constant attributes and the constant relationships. Then, the triplets are named, the value space is set, and the schema layer is constructed. Finally, the related information about devices is used to fill the data layer, and then, the knowledge graph is generated. Meanwhile, this study utilizes desktop FDM 3D printing devices as a case example for validation. The method proposed in this study can enhance the accuracy and maintainability of equipment resource management in the manufacturing sector, effectively promoting subsequent activities such as management, analysis, and decision-making. Full article

(This article belongs to the Topic Smart Product Design and Manufacturing on Industrial Internet)

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18 pages, 1408 KiB  

Open AccessArticle

A Flow Shop Scheduling Method Based on Dual BP Neural Networks with Multi-Layer Topology Feature Parameters

by Hui Mu, Zinuo Wang, Jiaqi Chen, Guoqiang Zhang, Shaocun Wang and Fuqiang Zhang

Systems 2024, 12(9), 339; https://doi.org/10.3390/systems12090339 - 1 Sep 2024

Viewed by 659

Abstract

Nowadays, the focus of flow shops is the adoption of customized demand in the context of service-oriented manufacturing. Since production tasks are often characterized by multi-variety, low volume, and a short lead time, it becomes an indispensable factor to include supporting logistics in [...] Read more.

Nowadays, the focus of flow shops is the adoption of customized demand in the context of service-oriented manufacturing. Since production tasks are often characterized by multi-variety, low volume, and a short lead time, it becomes an indispensable factor to include supporting logistics in practical scheduling decisions to reflect the frequent transport of jobs between resources. Motivated by the above background, a hybrid method based on dual back propagation (BP) neural networks is proposed to meet the real-time scheduling requirements with the aim of integrating production and transport activities. First, according to different resource attributes, the hierarchical structure of a flow shop is divided into three layers, respectively: the operation task layer, the job logistics layer, and the production resource layer. Based on the process logic relationships between intra-layer and inter-layer elements, an operation task–logistics–resource supernetwork model is established. Secondly, a dual BP neural network scheduling algorithm is designed for determining an operations sequence involving the transport time. The neural network 1 is used for the initial classification of operation tasks’ priority; and the neural network 2 is used for the sorting of conflicting tasks in the same priority, which can effectively reduce the amount of computational time and dramatically accelerate the solution speed. Finally, the effectiveness of the proposed method is verified by comparing the completion time and computational time for different examples. The numerical simulation results show that with the increase in problem scale, the solution ability of the traditional method gradually deteriorates, while the dual BP neural network has a stable performance and fast computational time. Full article

(This article belongs to the Topic Smart Product Design and Manufacturing on Industrial Internet)

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25 pages, 619 KiB  

Open AccessArticle

A Study of the Impact of Manufacturing Servitization on Firms’ Cost Stickiness

by Ming Bai, Hao Guan, Ye Hong and Haoyi Sun

Systems 2024, 12(7), 266; https://doi.org/10.3390/systems12070266 - 22 Jul 2024

Cited by 1 | Viewed by 1050

Abstract

Since 2014, China has been actively promoting the transformation of manufacturing servitization, clarifying the importance of manufacturing servitization. This paper investigates the correlation between manufacturing servitization and cost stickiness, supplementing the research on the economic consequences of manufacturing servitization and the influencing factors [...] Read more.

Since 2014, China has been actively promoting the transformation of manufacturing servitization, clarifying the importance of manufacturing servitization. This paper investigates the correlation between manufacturing servitization and cost stickiness, supplementing the research on the economic consequences of manufacturing servitization and the influencing factors of cost stickiness. This paper launches an empirical study with a sample of A-share manufacturing companies from 2014 to 2022. The research results show that, first, manufacturing servitization can inhibit enterprise cost stickiness; second, manufacturing servitization affects enterprise cost stickiness through the path of reducing enterprise adjustment costs, reducing managers’ optimistic expectations and reducing enterprise agency costs; third, the negative relationship between manufacturing servitization and cost stickiness is stronger among firms with a low level of internal control, a strong degree of financing constraints, a good quality internal information environment, a strong degree of competition in the market, and firms that are in capital-intensive manufacturing industries; fourth, the role of embedded servitization on enterprise cost stickiness is not significant, while hybrid servitization can have a significant negative effect on enterprise cost stickiness; and fifth, the impact of manufacturing servitization on enterprise cost stickiness mainly lies in the cost of material resources rather than the cost of human resources. Full article

(This article belongs to the Topic Smart Product Design and Manufacturing on Industrial Internet)

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