Machines

Achieving the highest levels of repeatability and precision, especially in robot manipulators applied in automation manufacturing, is a practical pose-recognition problem in robotics. Deviations from nominal robot geometry could produce substantial errors at the end effector, which can be more than 0.5 inches for a 6 ft robot arm. In this research, a pose-recognition system is developed for estimating the position of each robot joint and end-effector pose using image processing. To generate the joint angle, the system is developed via the modeling of a pose obtained by combining a convolutional neural network (CNN) and a multi-layer perceptron network (MLP). The CNN categorizes the input image generated by a remote monocular camera and generates a classification probability vector. The MLP generates a multiple linear regression model based on the probability vector generated by a CNN and describes the values of each joint angle. The proposed model is compared with the P-n-Perspective problem-solving method, which is based on marker tracking using ArUco markers and the encoder values. The system was verified using a robot manipulator with four degrees of freedom. Additionally, the proposed method exhibits superior performance in terms of joint-by-joint error, with an absolute error that is three units less than that of the computer vision method. Furthermore, when evaluating the end-effector pose, the proposed method showed a lower average standard deviation of 9mm compared with the computer vision method, which had a standard deviation of 13 mm. Full article

Axial flux motors have a large output density with a large outer diameter of the motor and a short axial length. Since it is advantageous in short axial length, the axial thickness of motor components becomes a very important parameter when designing axial flux motors. Among the components, the back yoke exists to serve as a path for magnetic flux and must have a certain thickness to prevent magnetic saturation. However, as the thickness of the back yoke increases within the axial size limit of the motor, the output of the motor may decrease. In this paper, same-direction skew that increases the cross-sectional area of the magnetic flux path without increasing the thickness of the back yoke is presented. Same-direction skew is a way to increase the cross-sectional area of the back yoke by skewing the rotor and stator in the same direction. The back yoke thickness that can be reduced by same-direction skew was calculated. Performance with same-direction skew designed using the equations was analyzed and compared, and the effectiveness of each type of rotor was verified. The validity of the proposed model was examined using the finite element analysis method. Full article

The vibro-acoustic performance of a fluid-loaded periodic locally resonant (LR) plate was examined in this research, with a specific focus on the effect of water fluid on the vibration and sound radiation of the LR structure. The analytical models of the fluid-loaded LR plate’s band gap, vibration, and acoustic radiation were theoretically derived with closed-form solutions, which can be used to predict the general vibro-acoustic rules of underwater LR structure. The results show that the LR band-gap width and Bragg frequency are significantly reduced when water fluid is considered. Besides, the frequency range that can be tuned to control the vibration and sound radiation for the LR plate with fluid is much narrower than that without fluid. The reason for inducing the above effects was also given in this research, which can be physically explained by the attached mass caused by the water fluid. In addition, the reason for the enhanced radiation efficiency close above the band gap was also discussed, which is caused by the change of radiation mode from corner or edge radiation to monopole radiation. Furthermore, adding small damping into the resonator could reduce the vibration and sound radiation in the frequency range above or close below the band gap, inducing the attenuation zone to be significantly broadened. Thus, designing the periodic resonators with proper damping could be an efficient method to make the LR plate more beneficial for vibration and noise reduction in water-surrounding applications. Full article

Hybrid electric propulsion, using batteries for energy storage, is making significant inroads into railway transportation because of its potential for notable fuel savings and the related reductions in greenhouse gases emissions of hybrid railway traction over non-electrified railway lines. Due to the inherent complexity of hybridized powertrains, combining different power conversions and energy storage capabilities, the corresponding operation of their energy management needs to be precisely optimized in order to achieve the minimum possible fuel consumption. Having this in mind, this paper proposes a real-time energy management control strategy for a diesel–electric hybrid locomotive based on the optimization results obtained by means of a dynamic programming optimization algorithm aimed at fuel consumption minimization while honoring the battery state-of-charge constraints and powertrain physical constraints. The final optimization result, expressed in terms of the optimal battery state-of-charge reference (target), is used as an additional input into the state-of-charge controller within the real-time energy management system. The subsequent simulation analysis shows clear fuel economy improvement with 22.9% of fuel savings obtained for the locomotive featuring a hybrid powertrain equipped with batteries over the conventional one. Full article

This study addresses the challenges associated with incomplete or missing information in obstacle detection methods that employ a single sensor. Additionally, it tackles the issue of motion distortion in LiDAR point cloud data during synchronization and mapping in complex environments. The research introduces two significant contributions. Firstly, a novel obstacle detection method, named the point-map fusion (PMF) algorithm, was proposed. This method integrates point cloud data from the LiDAR, camera, and odometer, along with local grid maps. The PMF algorithm consists of two components: the point-fusion (PF) algorithm, which combines LiDAR point cloud data and camera laser-like point cloud data through a point cloud library (PCL) format conversion and concatenation, and selects the most proximate point cloud to the quadruped robot dog as the valid data; and the map-fusion (MF) algorithm, which incorporates local grid maps acquired using the Gmapping and OctoMap algorithms, leveraging Bayesian estimation theory. The local grid maps obtained by the Gmapping and OctoMap algorithms are denoted as map A and map B, respectively. This sophisticated methodology enables seamless map fusion, which significantly enhances the precision and reliability of the approach. Secondly, a motion distortion removal (MDR) method for LiDAR point cloud data based on odometer readings was proposed. The MDR method utilizes legged odometer data for linear data interpolation of the original distorted LiDAR point cloud data, facilitating the determination of the corresponding pose of the quadruped robot dog. Subsequently, the LiDAR point cloud data are then transformed to the quadruped robot dog coordinate system, efficiently mitigating motion distortion. Experimental results demonstrated that the proposed PMF algorithm achieved a 50% improvement in success rate compared to using only LiDAR or the PF algorithm in isolation, while the MDR algorithm enhanced mapping accuracy by 45.9% when motion distortion was taken into account. The effectiveness of the proposed methods was confirmed through rigorous experimentation. Full article

This work proposes a novel enriched finite element method (E-FEM) for structural dynamics analysis. We developed the enriched 3-node triangular and 4-node tetrahedral displacement-based elements (T-elements). The standard linear shape functions of these T-elements were enriched using interpolation cover functions over each patch of elements. We also introduced and compared different orders of cover functions; higher-order functions obtained higher computational performance. Subsequently, the forced and free vibration analyses were performed on various typical numerical examples. The proposed enriched finite element method generated more precise numerical results and ensured faster convergence than the original linear elements. Full article

Spindles are key components of machine tools. An efficient estimation of the spindle condition and its prognosis can improve production efficiency and quality due to predictive maintenance planning. This paper proposes a method for predicting the remaining useful life (RUL) of machine tool spindle bearings using a combined calculation and experimental approach. The calculation model based on the ISO 281 standard uses monitored real loading conditions caused by the machining process and the machine tool operation. The model enables the updated calculation of the spindle lifetime L_10h using real load distribution. Since the operation hours of the spindle are also monitored, the remaining useful life (RUL) of the spindle can be calculated. This RUL value is corrected using a bearing condition assessment based on the effective value of the vibration velocity RMS according to the ISO 20816 standard and measured data from the machine tool control system. The proposed method is tested on two different spindle types featuring three pieces of every type. The experimental results of six spindles are compared and validated with a concurrent blind evaluation conducted by a skilled expert. The validation shows a very good match of the proposed method and the expert opinion. The method combining a calculation of the spindle lifetime using monitored real load distribution and subsequent result correction using vibration signal enables the implementation of a full automated estimation of the spindle RUL. Full article

The Fixed Point Iteration-based Adaptive Control design methodology is an alternative to the Lyapunov function-based technology. It contains higher-order feedback terms than the standard resolved acceleration rate control. This design approach strictly separates the kinematic and dynamic issues. At first, a purely kinematic prescription is formulated for driving the components of the tracking error to zero. Then an available approximate dynamic model is used to calculate the approximated necessary control forces. Before exerting on the controlled system, these forces are adaptively deformed in order to precisely obtain the prescribed kinematic behavior. The necessary deformation is iteratively found by the use of a contractive map that results in a sequence that converges to the unique fixed point of this map. In the case of underactuated systems, when the relative order of the control task also increases, the highest-order time-derivative depends on the lower-order ones according to the dynamic model of the system. This makes it impossible to realize the arbitrarily constructed kinematic design. In the paper, a resolution to this discrepancy is proposed. The method is demonstrated using two non-linear paradigms, a three-degree-of-freedom robot arm, and a two-degree-of-freedom system, i.e., two coupled non-linear springs. The operation of the method was investigated via simulations made by the use of Julia language and simple sequential programs. It was found that the suggested solution could be considered as a new variant of the fixed point iteration-based model reference adaptive control that is applicable for underactuated systems even if the relative order of the task is increased. Full article

The induction motor (IM) drives are prone to various uncertainties, disturbances, and non-linear dynamics. A high-performance control system is essential in the outer loop to guarantee the accurate convergence of speed and torque to the required value. Super-twisting sliding mode control (ST-SMC) and fractional-order calculus have been widely used to enhance the sliding mode control (SMC) performance for IM drives. This paper combines the ST-SMC and fractional-order calculus attributes to propose a novel super-twisting fractional-order sliding mode control (ST-FOSMC) for the outer loop speed control of the model predictive torque control (MPTC)-based IM drive system. The MPTC of the IM drive requires some additional sensors for speed control. This paper also presents a novel machine learning-based Gaussian Process Regression (GPR) framework to estimate the speed of IM. The GPR model is trained using the voltage and current dataset obtained from the simulation of a three-phase MPTC based IM drive system. The performance of the GPR-based ST-FOSMC MPTC drive system is evaluated using various test cases, namely (a) electric fault incorporation, (b) parameter perturbation, and (c) load torque variations in Matlab/Simulink environment. The stability of ST-FOSMC is validated using a fractional-order Lyapunov function. The proposed control and estimation strategy provides effective and improved performance with minimal error compared to the conventional proportional integral (PI) and SMC strategies. Full article

An experimental study of the process of friction between a steel spherical indenter and a soft elastic elastomer, with a strongly pronounced adhesive interaction between the surfaces of the contacting bodies, is presented. We consider sliding of the indenter at low speed (quasi-static contact) for different indentation depths. The forces, displacements and contact configuration as functions of time were recorded. The most important finding is that under conditions of uni-lateral continuous sliding, the tangential stress in the contact area remains constant and independent on the indentation depth and details of loading. We suggest a simple numerical model in which the elastic substrate is considered as a simple elastic layer (thus reminding a two-dimensional elastic foundation), although with in-plane elastic interactions. It is found that this model leads to the dynamic scenarios which qualitatively resemble the experimentally observed behavior of the considered system. Full article

Reducing the deviation effect from the specified machining conditions on the quality of the process in real time is the desired result of the intelligent spindle control system. To implement such a control system, a dynamic interaction model of the technological machining system with the cutting process was developed. The transfer matrix method of a multibody system was used in the development of the dynamic model. The physical closure condition of the technological machining system for using the transient matrix method is implemented in the developed model by introducing into this model an additional elastic coupling of the contact between the tool and the machined workpiece. The model is presented as a dynamic model of the elastic system “spindle unit–workpiece/tool–cutting process–tool/workpiece”. To develop the dynamic model, the system decomposition was performed with an analytical description of the joint deformation conditions of the subsystems and the use of the transient matrix method to calculate the harmonic influence coefficients of these subsystems. The proposed approach is used to calculate the native vibration frequencies of the spindle with the workpiece fixed in the chuck and supported with the tool. The calculation results correspond to the experimental ones and quite accurately represent their trends for different contact interaction conditions. Full article

Microdrills are specific cutting tools widely used to drill microholes and microvias. For certain microdrill manufacturers, a conventional sampling inspection procedure is still manually operated for carrying out the destructive and visual measurements of two essential cross-sectional geometric parameters (CSGPs), called the cross-sectional web thickness (CSWT) and the cross-sectional outer diameter (CSOD), of their straight (ST) and undercut (UC) type microdrill products. In order to comprehensively automate the conventional sampling inspection procedure, a destructive and visual measuring system improved from an existing vision-aided automation system, for both the hardware and the automated measuring process (AMP), is presented in this paper. The major improvement of the hardware is characterized by a machine vision module consisting of several conventional machine vision components in combination with an innovative and lower cost optical subset formed by a set of plano-concave achromatic (PCA) lenses and a reflection mirror, so that the essential functions of visually positioning the drilltip and visually measuring the CSGPs can both be achieved via the use of merely one machine vision module. The major improvement of the AMP is characterized by the establishment of specific image processing operations for an auto-focusing (AF) sub-process based on two-dimensional discrete Fourier transform (2D-DFT), for a web thickness measuring (WTM) sub-process based on an iterative least-square (LS) circle-fitting approach, and for an outer diameter measuring (ODM) sub-process based on integrated applications of an iterative LS circle-fitting approach and an LS line-fitting-based group-dividing approach, respectively. Experiments for measuring the CSGPs of microdrill samples were conducted to evaluate the actual effectiveness of the developed system. It showed that the developed system could achieve good repeatability and accuracy for the measurements of the CSWTs and CSODs of both ST and UC type microdrills. Therefore, the developed system could effectively and comprehensively automate the conventional sampling inspection procedure. Full article

In the operation of the imported container area of the container yard, the fault of the yard crane often occurs, and the fault is random and unpredictable, which greatly affects the operational efficiency of the container yard. To improve the operation efficiency of the container yard, this paper studies the rescheduling optimization problem of the multi-container area and multi-yard crane when random faults occur in container lifting operations in the container import area. Considering the different impacts of different fault conditions on the container yard operation, the fault impact judgment mechanism is established. The waiting time of external container trucks and customer satisfaction is considered for yard crane rescheduling. Yard crane rescheduling model after the fault is constructed, aiming at the minimum deviation from the original scheduling scheme. And the AEA (annealing evolution algorithm) algorithm is used to solve it. The effectiveness of magic and the specificity of the algorithm are verified by the analysis of numerical examples in different scales. The research data of Dalian Port is used to carry out experiments, and the experimental analysis of examples in different scales verifies the effectiveness of the model and the scientific nature of the algorithm. Compared with the existing scheme, this scheme is more practical, which can not only give the treatment scheme immediately when the fault occurs but also effectively improve the working efficiency of the container yard and provide a reference for the port to enhance customer satisfaction. Full article

A bridge crane is often used in a complex environment and is often subject to the interference of all loads. Some uncertain factors often have an inevitable impact on its swing. So the force situation of the bridge crane during a working cycle is analyzed, and a three-dimensional dynamic mathematical model of the bridge crane is built. Through the simulation analysis of the model under the action of a driving force and wind load, the change law of the swing angle of the bridge crane is studied. Then, the fuzzy control theory is used to determine the control parameter in the anti-sway control process. The position, swing angle deviation, and deviation rate of the bridge crane are taken as the input, and the parameter correction is obtained after the fuzzification by using the center of gravity method. The anti-sway fuzzy control system of the bridge crane is designed and simulated. The research results show that the swing model of the crane is reasonable and the fuzzy PID anti-sway controller can not only improve the adaptability of the control system, but also overcome the large overshoot, quickly restrain the swing, and effectively realize the anti-sway function of the bridge crane. Full article

This article addresses the problem of balancing an inverted spherical pendulum on a quadrotor. The full dynamic model is obtained via the Euler-Lagrange formalism, where the dynamics of the pendulum is coupled to the dynamics of the quadrotor, taking as control inputs the torques associated with the yaw, roll, and pitch dynamics, and a control input for the vertical displacement in height. A trajectory tracking control scheme is proposed by means of an active disturbance rejection control based on a discontinuous extended state observer (ADRC-DESO) that allows controlling the system in the translational dynamics of the quadrotor including the rotational dynamics and the inverted pendulum dynamics. To address this problem, the dynamic model is linearized around an equilibrium point, taking into consideration that the system operates in close vicinity of the equilibrium points, thus considerably simplifying the dynamic model. Proving that the linear model is controllable and therefore differentiable flat, flat outputs are proposed around the displacements associated with the three cartesian axes of the Euclidean space, including a dynamic associated with the yaw dynamics of the quadrotor allowing to parameterize the full linear system. Simulation results as well as a convergence analysis validate the performance of the strategy. Full article

This study offers a complete analysis of the use of deep learning or machine learning, as well as precise recommendations on how these methods could be used in the creation of machine components and nodes. The examples in this thesis are intended to identify areas in mechanical design and optimization where this technique could be widely applied in the future, benefiting society and advancing the current state of modern mechanical engineering. The review begins with a discussion on the workings of artificial intelligence, machine learning, and deep learning. Different techniques, classifications, and even comparisons of each method are described in detail. The most common programming languages, frameworks, and software used in mechanical engineering for this problem are gradually introduced. Input data formats and the most common datasets that are suitable for the field of machine learning in mechanical design and optimization are also discussed. The second half of the review describes the current use of machine learning in several areas of mechanical design and optimization, using specific examples that have been investigated by researchers from around the world. Further research directions on the use of machine learning and neural networks in the fields of mechanical design and optimization are discussed. Full article

Hybrid electric vehicles that can combine the advantages of traditional and new energy vehicles have become the optimal choice at present in the face of increasingly stringent fuel consumption restrictions and emission regulations. Range-extended hybrid electric vehicles have become an important research topic because of their high energy mixing degree and simple transmission system. A compact traditional fuel vehicle is the research object of this study and the range-extended hybrid system is developed. The design and optimization of the condition prediction energy management strategy are investigated. Vehicle joint simulation analysis and bench test platforms were built to verify the proposed control strategy. The vehicle tracking method was selected to collect real vehicle driving data. The number of vehicles in the field of view and the estimation of the distances between the front and following vehicles are calculated by means of the mature algorithm of the monocular camera and by computer vision. Real vehicle cycle conditions with driving environment and slope information were constructed and compared with all driving data, typical working conditions under NEDC, and typical working conditions under UDDS. The BP neural network and fuzzy logic control were used to identify the road conditions and the driver’s intention. The results showed that the equivalent fuel consumption of the control strategy was lower than that of the fixed-point power following control strategy and vehicle economy improved. Full article

The article describes the implementation of road driving tests with a vehicle in urban and extra-urban traffic conditions. Descriptions of the hardware and software needed for archiving the data obtained from the vehicle’s on-board diagnostic connector are presented. Then, the routes are analyzed using artificial intelligence methods. In this article, the reference of the route was defined as the trajectory of the driving process, represented by the engine rotational speed, the driving speed, and acceleration in the state space. The state space was separated into classes based on the results of the cluster analysis. In the experiment, five classes were clustered. The K-Means clustering algorithm was employed to determine the clusters in the variant without prior labelling of the classes using the teaching method and without participation of a teacher. In this way, the trajectories of the driving process in the five-state state space were determined. The article compares the signatures of routes created in urban and extra-urban driving conditions. Significant differences between the obtained results were indicated. Interesting methods of displaying the saved data are presented and the potential practical applications of the proposed method are indicated. Full article

This paper proposes a nonlinear flux linkage observer for the PMSM speed controls without motion sensors, introducing the deviation among the real stator flux linkage and an estimated stator flux linkage to suppress feedback and integral flux drift. In the position detection of an interior PMSM without a speed sensor, the traditional back EMF integration method uses a pure integrator, or LPF, to estimate the stator flux. Its inherent defects inevitably lead to inaccurate flux estimation, which directly affects the estimation of the motor mover position, resulting in the decline in motor control operation and the distortion of phase current. This paper uses an improved integrator with adaptive compensation. The projected value of the stator flux linkage has been derived from the estimated value of the rotor permanent magnetic flux linkage position angle and the algebraic model (m-model) of the stator flux linkage, along with a synchronous coordinate system. The IPMSM stator coil flux linkage obtained from the stator coil current and integral voltage models in the static coordinate system is compared to form a feedback closed-loop to suppress the integral drift, and using the cross-product approach of the actual and estimated flux linkage yields the projected value of the IPMSM rotor speed and position through a PLL. Compared with the existing motion-sensorless observers, the methodology proposed in this article is simple and exhibits better dynamic and static estimation performance. Extensive and comprehensive MATLAB computer simulation and experimental findings validate the proposed motion-sensorless control mechanism. Full article