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: 31 October 2025 | Viewed by 1147

Special Issue Editors


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Guest Editor
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Interests: intelligent product design; intelligent product manufacturing; multi-objective optimization; artificial intelligence

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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

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

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

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Published Papers (1 paper)

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Research

28 pages, 15623 KiB  
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 3 | Viewed by 724
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
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