Recent Progress in Robotic Machining

Special Issue Editors


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Guest Editor
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: robotic machining; 3D optical measurement; multi-robot cooperative system

E-Mail Website
Guest Editor
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: the application of vision measurement; optimization method of robotic machining; compensation machining
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Special Issue Information

Dear Colleagues,

Robotic machining, utilizing robots as the core of intelligent manufacturing equipment, is emerging as a transformative approach for processing large and intricate components in recent advancements. In contrast to CNC machine tools, robotic intelligent manufacturing equipment offers technical advantages, including heightened motion flexibility, expansive working space, variable topology structure, and the capability for multi-machine parallel and cooperative operations. Integrating 3D measurement, multimodal perception, artificial intelligence, and feedback control technology, robots are poised to revolutionize traditional manufacturing systems. This transformation encompasses enhanced intelligence, precision, flexibility, self-discipline, and the ability to engage in collaborative human–machine interaction. Such changes will significantly impact manufacturing methodologies, elevating system dexterity and augmenting human–computer interaction capabilities across various sectors, such as aerospace, wind power equipment, rail transit, and other critical areas involving large, complex components in high-performance manufacturing.

For this Special Issue, we are looking for recent findings focusing on robotic machining technologies, encompassing their applications and related research fields. Papers are expected to highlight capability enhancement in solving problems in robotic machining technology related to 3D measurement, multi-robot collaborative systems, artificial intelligence, and feedback control technology in complex component processing and manufacturing with robots. The content will feature interdisciplinary perspectives, fostering a dialogue on the latest advancements in robotic machining.

We are interested in contributions that focus on topics related to recent progress in robotic machining, such as:

  1. Robotics, mechatronics, and manufacturing automation with multimodal information, e.g., 3D measurement, vibration measurement, processing force measurement, etc.
  2. Computer-integrated manufacturing related to robotic drilling, milling, and grinding in large-size components.
  3. Application of automatic 3D measurement technology with robots.
  4. Application of multi-robot cooperative system in high-precision machining of large-size components, e.g., dual or multi-robot control strategies.
  5. Optimization method of robotic machining.
  6. Compensation technology for the robotic machining system.

Prof. Dr. Wenlong Li
Dr. Wei Xu
Guest Editors

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Keywords

  • robotic machining
  • robotic intelligent manufacturing equipment
  • multi-robot collaborative systems
  • complex component processing and manufacturing with robots

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Published Papers (1 paper)

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Research

26 pages, 7455 KiB  
Article
Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance
by Minh Trinh, Michael Königs, Lukas Gründel, Marcel Beier, Oliver Petrovic and Christian Brecher
J. Manuf. Mater. Process. 2025, 9(4), 126; https://doi.org/10.3390/jmmp9040126 - 11 Apr 2025
Viewed by 292
Abstract
The aim of this paper is to develop an approach to increase the accuracy of industrial robots for machining processes. During machining tasks, process forces displace the end effector of the robot. A simulation of the various process influences is therefore necessary to [...] Read more.
The aim of this paper is to develop an approach to increase the accuracy of industrial robots for machining processes. During machining tasks, process forces displace the end effector of the robot. A simulation of the various process influences is therefore necessary to ensure stable machining during production planning in optimizing the process parameters. Realistic simulations require precise dynamics and stiffness models of the robot. Regarding the dynamics, the frictional component is highly complex and difficult to model. Therefore, this paper follows a grey-box approach to combine the advantages of the state-of-the-art Lund–Grenoble model (white-box) with those of a data-driven one (black-box) in the first part. The resulting grey-box LuGre model proves to be superior to the white- and black-box models. In the second part, a model-based simulation planning assistance tool is developed, which makes use of the grey-box LuGre model. The simulation assistance provides the manufacturing planner with process knowledge using the identified robot and cutting force models. Furthermore, it provides optimization methods such as a switching point analysis. Finally, the assistance tool gives predictions about the machining result and a process evaluation. The third part of the paper shows the evaluation of the simulation assistance on a real machining process and workpiece, showing an increase in accuracy using the tool. Full article
(This article belongs to the Special Issue Recent Progress in Robotic Machining)
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