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Processes
Special Issues
Intelligent Decision-Making in Product Design and Industrial Manufacturing
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Intelligent Decision-Making in Product Design and Industrial Manufacturing

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

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 4120

Special Issue Editors

Dr. Yu Zhang

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Guest Editor
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Interests: Intelligent design; smart manufacturing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lihui Wang

E-Mail Website
Guest Editor
Department of Production Engineering, KTH Royal Institute of Technology, 10044 Stockholm, Sweden
Interests: intelligent product design; intelligent product manufacturing; multi-objective optimization; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiewu Leng

E-Mail Website
Guest Editor
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: intelligent product design; intelligent product manufacturing; multi-objective optimization; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Pai Zheng

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: intelligent product design; intelligent product manufacturing; multi-objective optimization; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's market environment with intense competition, enterprises need to constantly innovate and optimize their products throughout all stages of the product life cycle to meet customers' demands and enhance their market competitiveness. As an emerging technology, intelligent decision-making ‌is used to combine artificial intelligence technology with product decision-making processes and provide decision-makers with scientific, accurate, and efficient decision support using data analysis and model operation, which improves product quality and production efficiency, reduces production costs and optimizes resource allocation, and further provides a strong technical support for enterprises to gain advantages in highly competitive markets. Therefore, intelligent decision-making has become one of the most effective methods of product innovation and optimization, and is key to determining the competitiveness of enterprises.

This Special Issue on “Intelligent Decision-Making in Product Design and Industrial Manufacturing” seeks high-quality works focusing on the latest novel advances in intelligent decision-making technology for both product design and manufacturing. Topics include, but are not limited to:

  • Data-driven intelligent product design/manufacturing;
  • AI-based intelligent product design/manufacturing;
  • Cloud/edge/fog computing-enabled intelligent product design/manufacturing;
  • Human–machine interaction-based intelligent product design/manufacturing;
  • Multi-objective optimization in product design/manufacturing;
  • The development of intelligent decision-making systems in product design/manufacturing.

Thank you, and we hope you consider participating in this Special Issue.

Sincerely,

Dr. Yu Zhang
Prof. Dr. Lihui Wang
Prof. Dr. Jiewu Leng
Dr. Pai Zheng
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 250 words) can be sent to the Editorial Office for assessment.

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 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.

Keywords

  • intelligent decision-making
  • artificial intelligence
  • multi-objective optimization
  • intelligent product design
  • intelligent product manufacturing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

22 pages, 1020 KB  
Article
Spherical Fuzzy CRITIC–ARAS Framework for Evaluating Flow Experience in Metaverse Fashion Retail
by Adnan Veysel Ertemel, Nurdan Tümbek Tekeoğlu and Ayşe Karayılan
Processes 2025, 13(11), 3578; https://doi.org/10.3390/pr13113578 - 6 Nov 2025
Viewed by 432
Abstract
The Metaverse—an evolving convergence of virtual and physical realities—has emerged as a transformative platform, particularly within the fashion and retail industries. Its immersive nature aligns closely with the principles of flow theory, which describes the optimal psychological state of deep engagement and enjoyment. [...] Read more.
The Metaverse—an evolving convergence of virtual and physical realities—has emerged as a transformative platform, particularly within the fashion and retail industries. Its immersive nature aligns closely with the principles of flow theory, which describes the optimal psychological state of deep engagement and enjoyment. This study investigates the dynamics of fashion retail shopping in the Metaverse through a novel multi-criteria decision-making (MCDM) framework. Specifically, it integrates the CRITIC and ARAS methods within a spherical fuzzy environment to address decision-making under uncertainty. Flow theory is employed as the theoretical lens, with its dimensions serving as evaluation criteria. By incorporating spherical fuzzy sets, the model accommodates expert uncertainty more effectively. The findings provide empirical insights into the relative importance of flow constructs in shaping immersive consumer experiences in Metaverse-based retail environments. This study offers both theoretical contributions to the literature on digital consumer behavior and practical implications for fashion brands navigating immersive virtual ecosystems. Sensitivity analyses and comparative validation further demonstrate the robustness of the proposed framework. Full article
(This article belongs to the Special Issue Intelligent Decision-Making in Product Design and Industrial Manufacturing)
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24 pages, 2134 KB  
Article
Smart Risk Assessment and Adaptive Control Strategy Selection for Human–Robot Collaboration in Industry 5.0: An Intelligent Multi-Criteria Decision-Making Approach
by Ertugrul Ayyildiz, Tolga Kudret Karaca, Melike Cari, Bahar Yalcin Kavus and Nezir Aydin
Processes 2025, 13(10), 3206; https://doi.org/10.3390/pr13103206 - 9 Oct 2025
Viewed by 1156
Abstract
The emergence of Industry 5.0 brings a paradigm shift towards collaborative environments where humans and intelligent robots work side-by-side, enabling personalized, flexible, and resilient manufacturing. However, integrating humans and robots introduces new operational and safety risks that require proactive and adaptive control strategies. [...] Read more.
The emergence of Industry 5.0 brings a paradigm shift towards collaborative environments where humans and intelligent robots work side-by-side, enabling personalized, flexible, and resilient manufacturing. However, integrating humans and robots introduces new operational and safety risks that require proactive and adaptive control strategies. This study proposes an intelligent multi-criteria decision-making framework for smart risk assessment and the selection of optimal adaptive control strategies in human–robot collaborative manufacturing settings. The proposed framework integrates advanced risk analytics, real-time data processing, and expert knowledge to evaluate alternative control strategies, such as real-time wearable sensor integration, vision-based dynamic safety zones, AI-driven behavior prediction models, haptic feedback, and self-learning adaptive robot algorithms. A cross-disciplinary panel of ten experts structures six main and eighteen sub-criteria spanning safety, adaptability, ergonomics, reliability, performance, and cost, with response time and implementation/maintenance costs modeled as cost types. Safety receives the most significant weight; the most influential sub-criteria are collision avoidance efficiency, return on investment (ROI), and emergency response capability. The framework preserves linguistic semantics from elicitation to aggregation and provides a transparent, uncertainty-aware tool for selecting and phasing adaptive control strategies in Industry 5.0 collaborative cells. Full article
(This article belongs to the Special Issue Intelligent Decision-Making in Product Design and Industrial Manufacturing)
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28 pages, 15623 KB  
Article
Application Research of Vision-Guided Grinding Robot for Wheel Hub Castings
by Chunlei Li, Rui Nan, Yingying Wei, Liang Li, Jiaxing Liang and Nan Li
Processes 2025, 13(1), 238; https://doi.org/10.3390/pr13010238 - 15 Jan 2025
Cited by 4 | Viewed by 1762
Abstract
The combination of vision and robotic grinding technology provides robots with “visual perception” capabilities that enable them to accurately locate the area to be ground and perform the grinding tasks efficiently. Based on the rough grinding requirements for wheel hub burrs proposed by [...] Read more.
The combination of vision and robotic grinding technology provides robots with “visual perception” capabilities that enable them to accurately locate the area to be ground and perform the grinding tasks efficiently. Based on the rough grinding requirements for wheel hub burrs proposed by a casting company, this paper investigates the application of a vision-guided grinding robot in treating burrs on wheel hub castings. First, through vision system calibration, the conversion from pixel coordinate system to robot base coordinate system is implemented, thus ensuring that the subsequently extracted burr point coordinates can be correctly mapped to the robot’s operational coordinate system. Next, the images of the burrs on wheel hub castings are collected and processed. All the burr points are extracted by applying image algorithms. In order to improve grinding accuracy, a height error compensation model is established to adjust the coordinates of the 2D-pixel points; the coordinate error after compensation was reduced by 58.33%. Subsequently, the compensated burr point trajectories are optimized by utilizing an intelligent optimization algorithm to generate the shortest grinding path. Through experimental analysis of the relationship between spindle speed and surface roughness, a grinding trajectory simulation model is constructed, and the simulation results are integrated into the robot system. Finally, actual wheel hub burr grinding experiments are performed to validate the effectiveness and practicality of the proposed solution. Full article
(This article belongs to the Special Issue Intelligent Decision-Making in Product Design and Industrial Manufacturing)
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