Machines

Causes of the in-service damage to hydrocylinder liners were investigated, and the requirements to their working surfaces were systematized. Roughness parameter Ra was found not to provide a precise estimate of the surface quality because its reduction did not affect surface microgeometry. Additionally, the surface quality was assessed by the Abbott-Firestone curve during the finishing operation. The optimized manufacturing technology for obtaining hydrocylinder liners was offered based on having the required microgeometry and surface quality provided by cutting operations. The quality and service characteristics of internal surfaces of hydrocylinder liners were improved by changing technological operations. In particular, the semi-finish turning was chosen to provide for the surface roughness parameter Ra within 6.3–8.0 μm and the roughness pitch parameter S within 0.4–0.6 mm and homogeneous surface structure. The finishing rolling was replaced by burnishing to form a regular microrelief. Full article

In this paper, a method to suppress the vibration of a double-beam system with nonlinear synchronized switch damping on the inductor via a network (SSDI-net) is proposed. Unlike the classical linear piezoelectric shunt damping, SSDI-net is a nonlinear piezoelectric damping. A double-beam system with SSDI-net was simplified to a lumped parameter electromechanical coupling model and analyzed by using the multi-harmonic balance method, at first with alternating frequency–time techniques (MHBM/AFT). Then, a new lower-power autonomous switching control circuit board was designed, based on SSD technique, and vibration control experiments using a double-beam system with an SSDI network are conducted, to verify the validity of the proposed analysis method and its calculation results. The nonlinear switching piezoelectric network proposed in this article can increase the voltage inversion factor. Furthermore, future applications of this switching piezoelectric network technology in the vibration suppression of bladed-disk structures in aero engines can reduce the number of switches by at least half and obtain almost the same damping effect. Full article

This paper presents the two-phase condensation heat transfer and pressure drop characteristics of R-513A as an alternative refrigerant to R-134a in a 9.52-mm OD horizontal microfin copper tube. The test facility had a straight, horizontal test section with an active length of 2.0 m and was cooled by cold water circulated in a surrounding annular space. The annular-side heat transfer coefficients were obtained using the Wilson plot method. The average heat transfer coefficient and pressure drop data are presented at the condensation temperature of 35 °C in the range of 100–440 kg·m⁻²·s⁻¹ mass flux. The test data of R-513A are compared with those of R-134a, R-1234yf, and R-1234ze(E). The average condensation heat transfer coefficients of the R-513A and R-1234ze(E) refrigerants were similar to R-134a at the lower mass flux (100~150 kg·m⁻²·s⁻¹), while they were up to 10% higher than R-134a as the mass flux increased. The pressure drop of R-513A was similar to R-1234yf and 10% lower than that of R-134a at the higher mass flux. The R-1234ze(E) pressure drops were 20 % higher compared to those of R-134a at the higher mass flux. Full article

In a human–robot collaboration scenario, operator safety is the main problem and must be guaranteed under all conditions. Collision avoidance control techniques are essential to improve operator safety and robot flexibility by preventing impacts that can occur between the robot and humans or with objects inadvertently left within the operational workspace. On this basis, collision avoidance algorithms for moving obstacles are presented in this paper: inspired by algorithms already developed by the authors for planar manipulators, algorithms are adapted for the 6-DOF collaborative manipulators by Universal Robots, and some new contributions are introduced. First, in this work, the safety region wrapping each link of the manipulator assumes a cylindrical shape whose radius varies according to the speed of the colliding obstacle, so that dynamical obstacles are avoided with increased safety regions in order to reduce the risk, whereas fixed obstacles allow us to use smaller safety regions, facilitating the motion of the robot. In addition, three different modalities for the collision avoidance control law are proposed, which differ in the type of motion admitted for the perturbation of the end-effector: the general mode allows for a 6-DOF perturbation, but restrictions can be imposed on the orientation part of the avoidance motion using 4-DOF or 3-DOF modes. In order to demonstrate the effectiveness of the control strategy, simulations with dynamic and fixed obstacles are presented and discussed. Simulations are also used to estimate the required computational effort in order to verify the transferability to a real system. Full article

The paper at hand presents a novel and versatile method for tracking the pose of varying products during their manufacturing procedure. By using modern Deep Neural Network techniques based on Attention models, the most representative points to track an object can be automatically identified using its drawing. Then, during manufacturing, the body of the product is processed with Aluminum Oxide on those points, which is unobtrusive in the visible spectrum, but easily distinguishable from infrared cameras. Our proposal allows for the inclusion of Artificial Intelligence in Computer-Aided Manufacturing to assist the autonomous control of robotic handlers. Full article

Generally, in the laser measurement of gears, the laser beam passes through the center of the gear, and the laser displacement sensor reads the spatial distance from the gear involute tooth surface to the laser displacement sensor. However, in this method, the angle between the laser beam and the normal vector of the measured tooth surface is too large, which affects the accuracy of the measurement and the stability of the data. This paper proposes an offset laser measurement method. The laser beam is offset from the center of the gear by a certain distance to form a larger incident angle with the tooth surface, which can effectively address the problem and increase the measurement accuracy. Through a selection of the optimal offset distance, the range of optimal offset measurement positions was obtained and clarified by experiments. We solved the data conversion problem caused by the change in measuring position, and we measured the pitch deviation and helix angle of the gear to confirm the feasibility of this method. According to the theoretical calculation and experimental verification, it was found that this method has the advantages of better measurement accuracy and less fluctuation in measurement data. It is, thus, suitable for precision gear measurement. Full article

Most existing finger rehabilitation robots are structurally complex and cannot be adapted to multiple work conditions, such as clinical and home. In addition, there is a lack of attention to active adduction/abduction (A/A) movement, which prevents stroke patients from opening the joint in time and affects the rehabilitation process. In this paper, an end-effector finger rehabilitation robot (EFRR) with active A/A motion that can be applied to a variety of applications is proposed. First, the natural movement curve of the finger is analyzed, which is the basis of the mechanism design. Based on the working principle of the cam mechanism, the flexion/extension (F/E) movement module is designed and the details used to ensure the safety and reliability of the device are introduced. Then, a novel A/A movement module is proposed, using the components that can easily individualized design to achieve active A/A motion only by one single motor, which makes up for the shortcomings of the existing devices. As for the control system, a fuzzy proportional-derivative (PD) adaptive impedance control strategy based on the position information is proposed, which can make the device more compliant, avoid secondary injuries caused by excessive muscle tension, and protect the fingers effectively. Finally, some preliminary experiments of the prototype are reported, and the results shows that the EFRR has good performance, which lays the foundation for future work. Full article

Autonomous underwater vehicle is an effective tool for humans to explore the ocean. It can be used for the monitoring of underwater structures and facilities, which puts forward more accurate and stable requirements for the system operation of the autonomous underwater vehicle. This paper studies the system and structural design, including the parameter identification design and control system design, of a novel autonomous underwater vehicle called “Arctic AUV”. The dynamic mathematical model of the “Arctic AUV” was established, and the system parameter identification method based on the multi-sensor least squares centralized fusion algorithm was proposed. The simplification of the mathematical model of the robot was theoretically derived, and the online parameter identification and motion control were combined, so that the robot could cope with the influence of the arctic water velocity and external turbulence. Based on the hybrid control scheme of adaptive PID and predictive control, the accurate motion control of the “Arctic AUV” was realized. A prototype of “Arctic AUV” was developed, and system parameter identification experiments were carried out in indoor pool water. Hybrid adaptive and predictive control experiments were also carried out. The validity of the parametric design method in this paper was verified, and by comparative experiment, the effect of the control method proposed in this paper was better than the traditional method. Full article

Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application. Full article

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components. Full article

Methods used earlier for choosing profile shift coefficients were based on presumed advantages without physical proof justifying them. In this paper, a new method is proposed which guarantees a positive influence on gear failures and the operational conditions of gear pairs. The author proposes the introduction of a new concept: the cumulative effectiveness of profile shift coefficients. Full article

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively. Full article

A kind of swing micro-mirror structure with high stability for gravitational wave observatory in space is proposed in this paper. As the key interface instrument in the gravitational wave observatory, the swing micro-mirror structure plays a very important role. Firstly, the 3D model of the mechanism is designed and established. Then, the solution method of the index of stability, pointing jitter, is researched. After that, the thermal stability and the first-order natural frequency of the mechanism are researched via finite element analysis. The first-order natural frequency of the mechanism is 247.55 Hz, which can meet the requirements of the design. It can be seen from the results of the simulation, the amplitude spectral density of the mirror angle deviation is 3.975 nrad/√Hz when the range of temperature variation is 0.1 °C, which is able to meet the requirements of the design. The thermal stability has a closed relationship with the structural stability around the X-axis. In addition, this article also studies the thermal stability of the mechanism in the case of temperature changes in different directions. It is found that the thermal stability of the mechanism around the Y-axis would be significantly affected by the temperature changes along the Y-axis. Full article

A row-following system based on machine vision for a picking robot was designed in our previous study. However, the visual perception could not provide reliable information during headland turning according to the test results. A complete navigation system for a picking robot working in an orchard needs to support accurate row following and headland turning. To fill this gap, a headland turning method for an autonomous picking robot was developed in this paper. Three steps were executed during headland turning. First, row end was detected based on machine vision. Second, the deviation was further reduced before turning using the designed fast posture adjustment algorithm based on satellite information. Third, a curve path tracking controller was developed for turning control. During the MATLAB simulation and experimental test, different controllers were developed and compared with the designed method. The results show that the designed turning method enabled the robot to converge to the path more quickly and remain on the path with lower radial errors, which eventually led to reductions in time, space, and deviation during headland turning. Full article

In this paper, we consider the problem of selecting the most efficient optimization algorithm for neural network approximation—solving optimal control problems with mixed constraints. The original optimal control problem is reduced to a finite-dimensional optimization problem by applying the necessary optimality conditions, the Lagrange multiplier method and the least squares method. Neural network approximation models are presented for the desired control functions, trajectory and conjugate factors. The selection of the optimal weight coefficients of the neural network approximation was carried out using the gravitational search algorithm and the basic particle swarm algorithm and the genetic algorithm. Computational experiments showed that evolutionary optimization algorithms required the smallest number of iterations for a given accuracy in comparison with the classical gradient optimization method; however, the multi-agent optimization methods were performed later for each operation. As a result, the genetic algorithm showed a faster convergence rate relative to the total execution time. Full article

The main objective of this paper is to present a position control design to a DC-motor, where the set-point is externally supplied. The controller is conceived by using vibrational control theory and implemented by just processing the time derivative of a Hall-effect sensor signal. Vibrational control is robust against model uncertainties. Hence, for control design, a simple mathematical model of a DC-Motor is invoked. Then, this controller is realized by utilizing analog electronics via operational amplifiers. In the experimental set-up, one extreme of a flexible beam attached to the motor shaft, and with a permanent magnet fixed on the other end, is constructed. Therefore, the control action consists of externally manipulating the flexible beam rotational position by driving a moveable Hall-effect sensor that is located facing the magnet. The experimental platform results in a low-priced device and is useful for teaching control and electronic topics. Experimental results are evidenced to support the main paper contribution. Full article

Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated. Full article

This paper addresses one of the current areas of interest in electrical engineering, which is controlled switching of high voltage circuit breakers. During their operation, the problem of controlled switching of high voltage circuit breakers in commutation regimes was studied. Several types of switching were analyzed, considered representative of the transient regime, depending on the type of load, on the defect that may occur on the power supply lines, as well as depending on the position of this defect (near or far). The study carried out in the paper includes simulations of the controlled connection/disconnection operations in a transient regime, assuming the existence of different kinds of defects. To perform the study and simulations in the transient regime, a model, implemented in Matlab, was used for a time interval located around the origin of the time axis. The study included the dependence of the SF6 circuit breaker switching process on the following parameters: the DC voltage supply, ambient temperature and oil pressure in the circuit breaker actuator. The validity of the theory presented in this paper, in addition to being validated by simulations, is proven by the fact that the protection system currently in use at the power station of an 800 MW power plant, at the 400 kV power line, is based on the principles presented in this paper. The theory presented in the paper has been implemented in industry for nearly two years, and the results confirm that the theory presented in the paper is fully applicable in high voltage power stations. Full article

The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dictionary learned by the training samples, including thedifferent classes, shift invariant sparse feature for the training as well as test samples can be formed through sparse codes and employed as the input of classifier. A support vector machine (SVM) with optimized parameters has been extensively used in intelligent diagnosis of machinery fault. Hence, in this study, a novel fault diagnosis method of rolling bearings using shift invariant sparse feature and optimized SVM is proposed. Firstly, dictionary learning by shift invariant K-SVD algorithm is conducted. Then, shift invariant sparse feature is constructed with the learned over-complete dictionary. Finally, optimized SVM is employed for classification of the shift invariant sparse feature corresponding to different classes, hence, bearing fault diagnosis is achieved. With regard to the optimized SVM, three methods including grid search, generic algorithm (GA), and particle swarm optimization (PSO) are respectively carried out. The experiment results show that the shift invariant sparse feature using shift invariant K-SVD can effectively distinguish the bearing vibration signals corresponding to different running states. Moreover, optimized SVM can significantly improve the diagnosis precision. Full article