For the application of parallel robots in the grinding industry, a parallel robot equipped with a constant force actuator that produces a constant force for grinding is designed. To study the characteristics of the parallel robot’s spatial positions and poses, the inverse solutions of the moving platform’s spatial positions and poses as well as the workspace where objects were ground were established by using DH parameters and geometric methods. The experimental results showed that the workspace where objects were ground was a cylinder with a cross section similar to a symmetric circular sector. To analyze the characteristics of the forces produced by the parallel robotic system, the dynamics equation was established via the Newton–Euler method to verify the rationality of the force decoupling design. Theoretical calculation combined with simulation and experimental analyses confirmed the viability of the theoretical analyses which lay a theoretical foundation for the design, manufacture and control of the parallel robotic system proposed in this paper.

An algorithm for automatically planning trajectories designed for painting large objects is proposed in this paper to eliminate the difficulty of painting large objects and ensure their surface quality. The algorithm was divided into three phases, comprising the target point acquisition phase, the trajectory planning phase, and the UR5 robot inverse solution acquisition phase. In the target point acquisition phase, the standard triangle language (STL) file, algorithm of principal component analyses (PCA), and k-dimensional tree (k-d tree) were employed to obtain the point cloud model of the car roof to be painted. Simultaneously, the point cloud data were compressed as per the requirements of the painting process. In the trajectory planning phase, combined with the maximum operating space of the UR5 robot, the painting trajectory of the target points was converted into multiple traveling salesman problem (TSP) models, and each TSP model was created with a genetic algorithm (GA). In the last phase, in conformity with the singularities of the UR5 robot’s motion space, the painting trajectory was divided into a recommended area trajectory and a non-recommended area trajectory and created by the analytical method and sequential quadratic programming (SQP). Finally, the proposed algorithm for painting large objects was deployed in a simulation experiment. Simulation results showed that the accuracy of the algorithm could meet the requirements of painting technology, and it has promising engineering practicability.

For analyzing the influence of the system parameters on the density of the powder layers in laser powder bed fusion (LPBF) technology, an experimental method is proposed to improve the structure of the recoater in the powder laying system and optimize the parameters of the powder laying system. With this experimental method, the appropriate density of the powder layers can be attained. In the proposed experimental method, the recoater in the powder laying system was taken as the research object and the forces affecting the powder and recoater when the powder was in contact with the recoater were analyzed. The discrete element model of the powder laying system was established to simulate and analyze the influences of the recoater’s radius, translational velocity and angular velocity on the density of powder layers. In addition, orthogonal experiments were designed to discuss the magnitude of the influence of each of the powder laying system’s parameters on the density of powder layers. Finally, the optimized parameter combination plan was put forward. The results show that increasing the recoater’s radius can enhance the density of powder layers within a certain range; but, as the recoater’s radius is increased continuously, its impact on the recoater’s radius on f powder layers’ density decreases. When the translational velocity of the recoater rises, powder layers’ density increases first and then decreases. The coater’s angular velocity has little effect on powder layers’ density. Eventually, the optimized processing parameters were determined, which are 25 mm for the recoater’s radius, 30 mm/s for the recoater’s translational velocity, and 12 s−1 for the recoater’s angular velocity. The results provide some significance and guidance in improving the recoater’s structure and optimizing the powder laying system’s parameters.

Active control of structural modal vibration is an effective strategy to enhance the sound quality of cabs in commercial vehicles. However, accurate determination of the positioning and quantity of modal active control sensors and actuators is crucial for cabs with intricate structures, owing to the presence of multiorder modes and their coupling. The study presented herein focuses on the cab of a commercial vehicle and contemplates the features of the irregular large-space structure of the cab. By capitalizing on the modal frequency and mode shape of the cab, utilizing the piezoelectric control principle and modal vibration energy as the assessment index, an advanced multimode composite control criterion is postulated to ascertain the configuration of primary sensors and actuators. The particle swarm optimization (PSO) objective function is constructed to accomplish the optimal position matching of the actuator/sensor, using the multimodal surface velocity vector of the vibration sensor as the core parameter. Furthermore, an improved linear adaptive particle swarm optimization (LA-PSO) technique is advanced to satisfy the requirements of optimal convergence performance and accuracy of the complex cab structure. The optimization culminates in a 9 × 9 multichannel active control scheme for determining the optimal position of the actuators/sensors. This investigation provides a technical foundation for the active control of sound quality in automotive cabs and presents an innovative method for implementing effective noise control systems in large-scale machinery and equipment.

Two algorithms that are distinct from the closed algorithm are proposed to create the inverse kinematics model of the UR10 robot: the Sequential Quadratic Programming (SQP) algorithm and the Back Propagation-Sequential Quadratic Programming (BP-SQP) algorithm. The SQP algorithm is an iterative algorithm in which the fundamental tenet is that the joint’s total rotation radian should be at a minimum when the industrial robot reaches the target attitude. With this tenet, the SQP algorithm establishes the inverse kinematics model of the robot. Since the SQP algorithm is overly reliant on the initial values, deviations occur easily and the solution speed, and the accuracy of the algorithm is undermined. To assuage this disadvantage of the SQP algorithm, a BP-SQP algorithm incorporating a neural network is introduced to optimize the initial values. The results show that the SQP algorithm is an iterative algorithm that relies excessively on the initial values and has a narrow range of applications. The BP-SQP algorithm eliminates the limitations of the SQP algorithm, and the time complexity of the BP-SQP algorithm is greatly reduced. Subsequently, the effectiveness of the SQP algorithm and the BP-SQP algorithm is verified. The results show that the SQP and BP-SQP algorithms can significantly reduce the operation time compared with the closed algorithm, and the BP-SQP algorithm is faster but requires a certain number of samples as a prerequisite.