مجله مهندسی مکانیک امیرکبیر
سال پنجاه و پنجم شماره 12 (اسفند 1402)

Dimensional Design of four-bar mechanisms, as the simplest and most widely used linkage, is always one of the main issues raised in the field of mechanism design. In the past, geometric and graphical analysis methods were used to design these mechanisms, but with the advancement of technology, numerical optimization methods, and meta-heuristic algorithms, it is possible to solve problems more precisely and consider a higher number of precision points. The path generation synthesis problem of this mechanism has been solved using different algorithms and in different design modes, including with and without prescribed timing, which the synthesis problem with prescribed timing is more difficult and has a higher path generation error. In this research, to solve the synthesis problem of four-bar mechanism path generation with prescribed timing, a novel objective function is utilized, which includes two terms, path error and angle error. This new objective function leads to a lower error for path generation with prescribed timing. Four different designs are considered and their results, where extracted by the PSO algorithm, are compared. The results of solving this problem for three numerical examples show that the design in the fourth state, which uses the new objective function, has fewer path generation errors.

Fault diagnosis of mechanical systems is of special importance for better system performance as well as its protection. In this work, a rotary motor laboratory system is used to generate signals and data. The obtained data are placed in the pre-processing process. In this article, to improve the performance of signal analysis, the combined analysis methods using signal features and Kalman filter are proposed. At first, the Kalman filter is used to reduce the signal noise. And in the following, for signal pre-processing, the features of the signal in the time domain and frequency domain are suggested, which have been used as one-dimensional signal pre-processing. In the following, several neural networks such as support vector machine, multilayer perceptron, and convolutional neural networks have been used to analyze the obtained features. To check the results, the data is divided into training data and validation data. Accuracy results for validation data are examined in different methods. The results indicate the better performance of the AlexNet convolutional neural network in the presence of the Kalman filter denoising process. In this case, this network has reached an average of 96.1% accuracy for validation data, which has been improved compared to other classifiers and fault diagnosis without noise reduction.

Surface roughness is one of the most important characteristics in nanomachining products. One of the parameters that always has a great impact on the surface quality is the forces caused by the nanocutting process. In this research, using molecular dynamics simulation in LAMMPS software, the process of dynamic nano ploughing of single-crystal copper workpiece by a diamond tool is investigated. The effect of parameters of dynamic nano- ploughing process such as depth of cut, amplitude and frequency of cutting tool vibration on cutting force and surface roughness has been investigated by calculating average roughness. Also, in order to more closely examine the effect of parameters and their interaction on each other, Taguchi method has been used to design experiments. The simulation results show that the characteristics of cutting depth, amplitude and frequency of cutting tool vibration have the greatest effect on surface smoothness and cutting forces, respectively. Based on the presented results, it has been determined that the surface roughness can be improved in different machining conditions based on the selection of parameters, and it is necessary to check their conditions together well before selecting these parameters. Also, using Taguchi method, the optimal values of dynamic ploughing parameters were obtained to achieve the best surface smoothness and the lowest cutting force in certain dimensions as described by DOC=2.5 , R=0.1 and ω=50 KHz.

This paper studies the autonomous vehicle leader following and collision avoidance problem. In this paper, like as a real car, geometric dimensions, mass and moment of inertia are considered for the car; steering-wheel and driving-wheel torques are the two control inputs. The nonlinear dynamics equation of the vehicle is derived. At first, an algorithm is proposed for changing the direction of the vehicle to follow the leader, then the suitable path for multiple obstacle avoidance and leader following is proposed, and then a nonlinear model predictive controller (MPC) is used to follow the reference trajectory. The desired trajectory is designed according to the elastic band method which is a powerful method for obstacle avoidance and leader following. The performances of the closed-loop system are illustrated through simulations. During the simulation the vehicle first changes its direction and then follows the leader without colliding with obstacles. Although the vehicle is inertial and non-holonomic in behavior, the simulations show that the two path planning methods with MPC scheme works well. For the future works the authors aim to solve the problem with moving obstacles.

Aeroelastic instability in blades is one of the most important sources of instability in helicopter rotors, and the most critical of these instabilities is flutter. In this paper, in order to investigate the blade flutter and its relationship with the rotor structural parameters, using the Hamilton's principle and considering the Euler-Bernoulli beam theory, the coupled nonlinear partial differential equations governing the rotating elastic blade of a helicopter in the hover flight mode are extracted and converted into a set of ODEs by applying Galerkin method. Then the obtained equations for small perturbations are linearized around the steady state conditions. The effects of the gyroscopic couplings are investigated and assuming the harmonic response, the natural frequencies of the blade in three motion axes are calculated and the relationship between the natural frequency and flutter frequency of the blade with structural and aerodynamic parameters are shown. Using numerical simulation, the results for two types of soft and stiff blades with given characteristics in terms of different parameters such as blade twist angle, pre-cone angle and rotation speed of rotor for the first mode shape are extracted. Finally, the effect of each of the mentioned parameters on the flutter frequency and also, the blade stability region is analyzed. It is shown that by increasing the blade stiffness, the flutter frequency will increase and the system will be stable.

This paper presents the design and experimental implementation of the extended state observer (ESO) for a fabricated quarter-car suspension platform with a McPherson mechanism equipped with different sensors. This algorithm aims to estimate uncertainties and road input, and determines an accurate dynamic model for the vehicle suspension system. In the proposed method, the terms including uncertainties and unknown road input are added to the system as new state variables. Then, using data of sprung mass and unsprung mass displacements, these uncertainties and unknown inputs are estimated along with other state variables of the system. A nonlinear Kalman filter with unknown input is also designed to be compared with the ESO. The comparison results using the experimental data with measurement errors indicate the high accuracy of the ESO in constructing a precise dynamic model for the system. Meanwhile, the ESO uses fewer sensors and its regulation is easier. Both observers are used within the structure of active suspension system under an optimal nonlinear controller to provide the objectives of the suspension system. Co-simulation results of Adams/MATLAB show the better performance of the proposed controller using the ESO.