In industrial robots, the end-effector, the tool that directly interacts with the workpiece, is crucial for determining the quality of manufacturing tasks such as grinding, polishing, and deburring. However, traditional industrial robot systems mostly focus on the motion control of the robot body, even though the control performance of the end-effector often becomes the key bottleneck restricting processing quality. Therefore, talent in Intelligent Manufacturing Engineering programs must be cultivated to master high-precision motion control and constant force control methods for industrial robot end-effectors. To bridge this gap and considerably enhance the practical skills of students, this paper designs and develops an experimental platform and a corresponding teaching method centered on the motion control system of an industrial robot end-effector. The core objective is to enable students to systematically understand and implement motion control and constant force control techniques, with particular emphasis on force–position hybrid control in realistic grinding scenarios.

A dedicated experimental platform was developed, comprising a six-degree-of-freedom serial industrial robot, a 2R1T end-effector driven by voice coil motors, a Beckhoff industrial PC, ELMO servo drives, incremental encoders, and a six-axis force sensor. TwinCAT3 software was used to implement the control system, which comprised four functional modules: motion control, logic operation control, adaptive constant force variable admittance control, and a human–machine interface. A hierarchical experimental teaching method incorporating three progressively integrated experiments—single-axis motion control, multi-axis coordinated motion control, and constant force grinding control, the core experiment—was proposed. In the constant force experiment, students implemented force–position hybrid control and compared the performance of a PID-optimized admittance controller with that of a neural network adaptive constant force variable admittance controller based on an RBF network. Students performed the grinding task with a target contact force of 15 N, guided through the complete engineering cycle of parameter initialization, system activation, experimental execution, data acquisition, and performance evaluation.

Through the single-axis motion control experiment, students mastered three positioning modes and developed precision positioning skills. The multi-axis coordinated motion control experiment enabled students to achieve smooth trajectory tracking and synchronous multi-axis movement. In the constant force grinding control experiment, students implemented force–position hybrid control and compared two control strategies. The neural network adaptive controller achieved steady-state force fluctuation within ±1.5 N and an average force error of 0.16 N, considerably outperforming the PID-optimized admittance controller, whose fluctuation range and average error were ±3.2 N and 0.31 N, respectively, under identical conditions. These results clearly illustrate the superior adaptability and disturbance rejection capability of the neural network adaptive constant force variable admittance controller. Throughout the process, students gained hands-on experience in parameter tuning, real-time data monitoring, system debugging, and quantitative performance evaluation.

The developed experimental platform and proposed teaching method effectively guide students from foundational motion control skills to a comprehensive understanding of force–position hybrid control. By completing the full engineering cycle of parameter setting, experimentation, data analysis, and iterative optimization, students develop practical abilities in system debugging, performance evaluation, and controller comparison. The teaching method provides a replicable and effective approach for cultivating high-quality talent in intelligent manufacturing.

As the intelligent manufacturing sector expands its industrialization process, industrial robot technology continues to mature. Traditional teaching methods for this technology no longer suffice to adequately meet educational requirements. This paper aims to significantly enhance the practical robot operation skills of students majoring in intelligent manufacturing engineering and to enrich teaching content in industrial education by exploring simulation and experimental teaching design for robot deburring path planning.

First, we designed a deburring system for wheel hub windows that integrates industrial robot technology with machine vision. This system automates the positioning, recognition, and deburring path planning for wheel hub workpieces. Using Bezier curves, we designed the deburring path while considering robot kinematics and the 3D model of the wheel hub, optimizing the path length by accounting for the rotational freedom of the tool. By assessing the local curvature of the wheel hub window curve and its three-dimensional structure, adjustments are made to the tool position, contact point, and rotational and orientational angles to develop and execute an optimal tool angle strategy. The vision system is calibrated, deburring process parameters are configured, and the wheel hub workpiece is precisely configured. These allow the tool path to be adapted and the robot path to be crafted, resulting in comprehensive robotic deburring path planning. Subsequently, a robotic deburring experimental teaching approach was introduced, allowing teachers to clearly illustrate the fundamental principles and procedures of robotic deburring technology while offering personalized instructions and ongoing feedback based on student performance. Robot path planning simulation and actual robot deburring experiments were conducted using RobotStudio software for offline programming and simulation, and an experimental platform was established for conducting deburring experiments. The visual system gathered wheel hub pose information, enabling effective robot path planning and deburring operations.

The simulation results revealed that the robot maintained good contact with the wheel hub window edge, with the tool center point movement path being a smooth set of curves without obvious abrupt changes or bends. The joint angle change curve demonstrated no significant peaks, indicating smooth progression and gradual posture changes in the deburring tool. The experimental findings also demonstrated that the wheel hub edge was free of burrs or sharp edges postdeburring, and the chamfer size was within the 1.5 mm limit, demonstrating effective deburring quality and efficiency.

The proposed method provides an innovative experimental teaching case and implementation approach for experimental teaching in intelligent manufacturing engineering and industrial robot technology. It improves students’ practical skills in industrial robot deburring and offers a model for cultivating high-quality professionals in intelligent manufacturing.