Robotic Intelligent Machining System

Topic Information

Dear Colleagues,

Robotic intelligent machining systems have been used for various applications in production systems because they have many advantages, such as large workspaces, a high degree of freedom, flexibility, and cost-effectiveness. Compared to dedicated machine tools, industrial robots have large workspaces that can easily be extended by the addition of stages or mobile platforms. Robotic machining can be highly flexible because robots can be adjusted for a variety of machining processes by changing the end-effectors or tools attached to the manipulator. As robotic machining systems have a higher DOF than machine tools, more complex parts can be machined. Robots can also form work cells with other robots or machine tools. Therefore, the use of robots is increasing as the machining paradigm shifts from mass production to mass customization. Furthermore, the total costs of robotic machining systems are less than those of dedicated machine tools. However, despite the advantages, the implementation of robotic machining systems is still in its infancy due to their low machining accuracy. Although most industrial robots are used for welding and material handling processes, the adoption of robots for other machining processes has increased. Thus, substantial research has been carried out to analyze and reduce robotic machining errors and improve the performance of robotic machining systems.

Robotic intelligent machining systems, which use the industrial robot as the actuator, multi-source sensors (such as vision, force, and vibration) as the perception system, and machine learning and intelligent cloud platform as the decision system, can complete anthropomorphic manufacturing tasks according to incomplete, inaccurate information on the working condition without certainty, prior knowledge, or a prediction model. In addition, the intelligent robot machining system can combine the monitoring data with the theoretical model to achieve higher accuracy and efficiency in manufacturing tasks.

The aim of this topic is to present an overview of the current state of recent research on robotic intelligent machining systems, namely machining process planning and control techniques including the analysis of the robot-workspace, robot trajectory planning, vibration monitoring and control, deformation monitoring and compensation, as well as the principles of these technologies such as robot stiffness characteristics, dynamic characteristics, chatter mechanisms, and deformation mechanisms.

Suggested topics include, but are not limited to:

• Multi-robot collaborative processing of large and complex parts
• Intelligent robot machining of multi-variety and small batch parts
• Robot stiffness and pose planning
• Robot dynamics and trajectory planning
• Robotic milling chatter and suppression
• Improving the machining accuracy of robotic machining systems
• Robot stiffness model and identification method
• Analysis of robot workspace based on stiffness

Dr. Fangchen Yin
Prof. Dr. Changcai Cui
Prof. Dr. Guoqin Huang
Dr. Jingqi Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400
Automation automation	-	-	2020	26.3 Days	CHF 1000
Machines machines	2.6	2.1	2013	15.6 Days	CHF 2400
Materials materials	3.4	5.2	2008	13.9 Days	CHF 2600
Robotics robotics	3.7	5.9	2012	17.3 Days	CHF 1800
Actuators actuators	2.6	3.2	2012	16.7 Days	CHF 2400

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Published Papers (3 papers)

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All Applied Sciences Automation Machines Materials Robotics Actuators

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18 pages, 6089 KiB  

Open AccessArticle

Workpiece Placement Optimization for Robot Machining Based on the Evaluation of Feasible Kinematic Directional Capabilities

by Saša Stradovnik and Aleš Hace

Appl. Sci. 2024, 14(4), 1531; https://doi.org/10.3390/app14041531 - 14 Feb 2024

Viewed by 575

Abstract

Workpiece placement plays a crucial role when performing complex surface machining task robotically. If the feasibility of a robotic task needs to be guaranteed, the maximum available capabilities should be higher than the joint capabilities required for task execution. This can be challenging, [...] Read more.

Workpiece placement plays a crucial role when performing complex surface machining task robotically. If the feasibility of a robotic task needs to be guaranteed, the maximum available capabilities should be higher than the joint capabilities required for task execution. This can be challenging, especially when performing a complex surface machining task with a collaborative robot, which tend to have lower motion capabilities than conventional industrial robots. Therefore, the kinematic and dynamic capabilities within the robot workspace should be evaluated prior to task execution and optimized considering specific task requirements. In order to estimate maximum directional kinematic capabilities considering the requirements of the surface machining task in a physically consistent and accurate way, the Decomposed Twist Feasibility (DTF) method will be used in this paper. Estimation of the total kinematic performance capabilities can be determined accurately and simply using this method, adjusted specifically for robotic surface machining purposes. In this study, we present the numerical results that prove the effectiveness of the DTF method in identifying the optimal placement of predetermined machining tasks within the robot’s workspace that requires lowest possible joint velocities for task execution. These findings highlight the practicality of the DTF method in enhancing the feasibility of complex robotic surface machining operations. Full article

(This article belongs to the Topic Robotic Intelligent Machining System)

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20 pages, 6769 KiB  

Open AccessArticle

A High-Precision Planar NURBS Interpolation System Based on Segmentation Method for Industrial Robot

by Xun Liu, Yan Xu, Jiabin Cao, Jinyu Liu and Yanzheng Zhao

Appl. Sci. 2023, 13(24), 13210; https://doi.org/10.3390/app132413210 - 13 Dec 2023

Viewed by 769

Abstract

NURBS curve parameter interpolation is extensively employed in precision trajectory tasks for industrial robots due to its smoother performance compared to traditional linear or circular interpolation methods. The trajectory planning systems for industrial robots necessitate four essential functional modules: first, the spline curve [...] Read more.

NURBS curve parameter interpolation is extensively employed in precision trajectory tasks for industrial robots due to its smoother performance compared to traditional linear or circular interpolation methods. The trajectory planning systems for industrial robots necessitate four essential functional modules: first, the spline curve discretization technique ensuring chord error compliance; second, the contour scanning technique for determining the maximum feasible feed rate for multi-constraint and multi-segment paths; third, the technique for achieving a smooth feed rate profile; and fourth, the continuous curve parameter interpolation technique. Therefore, this paper proposes a high-precision planar NURBS interpolation system for industrial robots. Firstly, a segmentation method for NURBS curves based on a closed-loop chord error constraint is proposed, which segments the original global NURBS curve into a collection of Bezier curves that strictly meet the chord error constraint. Secondly, a bidirectional scanning technique is presented to meet the joint space constraint, establishing an analytical mapping between the tool tip kinematic constraint and the joint kinematic constraint. Then, based on the traditional S-shaped feed rate profile, an adaptive algorithm with a displacement constraint is introduced, considering the real-time speed adjustment requirements of robots. Finally, a compensation interpolation strategy based on arc length parameterization is adopted to solve the accumulated error problem in parameter interpolation. The effectiveness of and potential for enhancing the quality of planar machining of the proposed planar NURBS interpolation system for industrial robots are validated through simulations and experiments. The results demonstrate the system’s applicability and accuracy, and its ability to improve planar machining quality. Full article

(This article belongs to the Topic Robotic Intelligent Machining System)

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21 pages, 21604 KiB  

Open AccessArticle

Research on a Method of Robot Grinding Force Tracking and Compensation Based on Deep Genetic Algorithm

by Minghui Meng, Chuande Zhou, Zhongliang Lv, Lingbo Zheng, Wei Feng, Ting Wu and Xuewei Zhang

Machines 2023, 11(12), 1075; https://doi.org/10.3390/machines11121075 - 08 Dec 2023

Viewed by 1027

Abstract

In the grinding process of complex-shaped cast workpieces, discrepancies between the workpiece’s contours and their corresponding three-dimensional models frequently lead to deviations in the machining trajectory, resulting in instances of under-grinding or over-grinding. Addressing this challenge, this study introduces an advanced robotic grinding [...] Read more.

In the grinding process of complex-shaped cast workpieces, discrepancies between the workpiece’s contours and their corresponding three-dimensional models frequently lead to deviations in the machining trajectory, resulting in instances of under-grinding or over-grinding. Addressing this challenge, this study introduces an advanced robotic grinding force automatic tracking technique, leveraging a combination of deep neural networks and genetic algorithms. Harnessing the capability of force sensing, our method dynamically recalibrates the grinding path, epitomizing truly flexible grinding. Initially, in line with the prerequisites for force and pose tracking, an impedance control strategy was developed, integrating pose deviations with force dynamics. Subsequently, to enhance steady-state force tracking, we employed a genetic algorithm to compensate for force discrepancies caused by positional errors. This was built upon the foundational concepts of the three-dimensional model, impedance control, and environmental parameter estimation, leading to an optimized grinding trajectory. Following tracking tests, it was observed that the grinding’s normal force was consistently controlled within the bracket of 20 ± 2.5 N. To further substantiate our methodology, a specialized experimental platform was established for grinding complex-shaped castings. Optimized strategies were employed under anticipated forces of 5 N, 10 N, and 15 N for the grinding tests. The results indicated that the contact forces during the grinding process remained stable at 5 ± 1 N, 10 ± 1.5 N, and 15 ± 2 N. When juxtaposed with conventional teaching grinding methods, our approach manifested a reduction in grinding forces by 71.4%, 70%, and 75%, respectively. Post-grinding, the workpieces presented a pronounced enhancement in surface texture, exhibiting a marked increase in surface uniformity. Surface roughness metrics, originally recorded at 17.5 μm, 17.1 μm, and 18.7 μm, saw significant reductions to 1.5 μm, 1.6 μm, and 1.4 μm, respectively, indicating reductions by 76%, 73%, and 78%. Such outcomes not only meet the surface finishing standards for complex-shaped castings but also offer an efficacious strategy for robot-assisted flexible grinding. Full article

(This article belongs to the Topic Robotic Intelligent Machining System)

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