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This study aims to research the influence mechanism of microtextured geometric parameters of dry gas seal end face on the tribological behavior under dry frictional conditions.

The microtexture was processed using laser processing, while the diamond-like carbon (DLC) film was applied through magnetron sputtering; the experimental platform of friction vibration was established, the frictional and vibrational properties of different geometric parameters were tested; the data signals of vibrational acceleration and frictional torque were collected and processed using data acquisition instrument. The entropy characteristic parameters of 3D vibrational acceleration were extracted based on wavelet packet decomposition method. The end-face topography was measured with ST400 three-dimensional noncontact surface topography instrument.

The geometry of pits plays a key role in influencing friction performance; the permutation entropy and fuzzy entropy of the vibration acceleration signal changed with variations in microtextured parameters. A textured surface with appropriately size parameters can trap debris, enhance the dynamic pressure effect, reduce impact between the friction interfaces and improve the frictional vibrational performance. In this research, microtextured surface with Φ150 µm-10% and Φ200 µm-5% can effectively reduce friction and vibration between the end faces of a dry gas seal.

DLC film improves the hardness of seal ring end face, and microtexture improves the dynamic effect; the tribological behavior monitoring can be realized by analyzing the characteristics of vibration acceleration sensitive parameter with friction state. The findings will provide a basis for further research in the field of tribology and the microtexture optimization of dry gas seal ring end face.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0389/

This study establishes a method for predicting ground vibrations caused by railway tunnels in unsaturated soils with spatial variability.

First, an improved 2.5D finite-element-method-perfect-matching-layer (FEM-PML) model is proposed. The Galerkin method is used to derive the finite element expression in the ub-pl-pg format for unsaturated soil. Unlike the ub-v-w format, which has nine degrees of freedom per node, the ub-pl-pg format has only five degrees of freedom per node; this significantly enhances the calculation efficiency. The stretching function of the PML is adopted to handle the unlimited boundary domain. Additionally, the 2.5D FEM-PML model couples the tunnel, vehicle and track structures. Next, the spatial variability of the soil parameters is simulated by random fields using the Monte Carlo method. By incorporating random fields of soil parameters into the 2.5D FEM-PML model, the effect of soil spatial variability on ground vibrations is demonstrated using a case study.

The spatial variability of the soil parameters primarily affected the vibration acceleration amplitude but had a minor effect on its spatial distribution and attenuation over time. In addition, ground vibration acceleration was more affected by the spatial variability of the soil bulk modulus of compressibility than by that of saturation.

Using the 2.5D FEM-PML model in the ub-pl-pg format of unsaturated soil enhances the computational efficiency. On this basis, with the random fields established by Monte Carlo simulation, the model can calculate the reliability of soil dynamics, which was rarely considered by previous models.

This paper aims to design an active shock absorber scheme for use in conjunction with a passive shock absorber to suppress the horizontal vibration of elevator cars in a smaller range and shorter time. The developed active shock absorber will also improve the safety and comfort of passengers driving in ultra-high-speed elevators.

A six-degree of freedom dynamic model is established according to the position and condition of the car. Then the active shock absorber and disturbance compensation-based adaptive control scheme are designed and simulated in MATLAB/Simulink. The results are analysed and compared with the traditional shock absorber.

The results show that, compared with traditional spring-based passive damping systems, the designed active shock absorber can reduce vibration displacement by 60%, peak acceleration by 50% and oscillation time by 2/3 and is more robust to different spring stiffness, damping coefficient and load.

The developed active shock absorber and its control algorithm can significantly reduce vibration amplitude and converged time. It can also adjust the damping strength according to the actual load of the elevator car, which is more suitable for high-speed elevators.

This paper aims to understand technology-based accelerators’ legitimation efforts in an emerging entrepreneurship ecosystem.

This research is based on qualitative inductive methodology using ten Turkish technology-based accelerators.

The analysis indicates that accelerators’ legitimation efforts are shaped around crafting a distinctive identity and mobilizing allies around this identity; and establishing new collaborations to enable collective action. Further, the authors observe two types of technology-based accelerators, namely, “deal flow makers” and “welfare stimulators” in Turkey. These variations among accelerators affect how they build their legitimacy. Different types of accelerators make alliances with different actors in the entrepreneurship ecosystem. Accelerators take collective action to build a collective identity and simultaneously imply how they are distinguished from other organizations in the same category and the ones in the old category.

This study presents a framework to understand how accelerators use strategies and actions to legitimize themselves as new organizations and advocate new norms, values and routines in an emerging entrepreneurship ecosystem. The framework also highlights how different accelerators support legitimacy building by managing the judgments of diverse audiences and increasing the variety of resources these audiences provide to the ecosystem.

This paper focuses on the application of a robotic technique for modeling a three-wheeled mobile robot (WMR), considering it as a multibody polyarticulated system. Then the dynamic behavior of the developed model is verified using a physical model obtained by Simscape Multibody.

Firstly, a geometric model is developed using the modified Denavit–Hartenberg method. Then the dynamic model is derived using the algorithm of Newton–Euler. The developed model is performed for a three-wheeled differentially driven robot, which incorporates the slippage of wheels by including the Kiencke tire model to take into account the interaction of wheels with the ground. For the physical model, the mobile robot is designed using Solidworks. Then it is exported to Matlab using Simscape Multibody. The control of the WMR for both models is realized using Matlab/Simulink and aims to ensure efficient tracking of the desired trajectory.

Simulation results show a good similarity between the two models and verify both longitudinal and lateral behaviors of the WMR. This demonstrates the effectiveness of the developed model using the robotic approach and proves that it is sufficiently precise for the design of control schemes.

The motivation to adopt this robotic approach compared to conventional methods is the fact that it makes it possible to obtain models with a reduced number of operations. Furthermore, it allows the facility of implementation by numerical or symbolical programming. This work serves as a reference link for extending this methodology to other types of mobile robots.

The purpose of this paper is the dynamic analysis and seismic damage assessment of steel sheet pile quay wall with inelastic behavior underground motions using several accelerograms.

Finite element analysis is conducted using the Plaxis 2D software to generate the numerical model of quay wall. The extension of berth 25 at the port of Bejaia, located in northeastern Algeria, represents a case study. Incremental dynamic analyses are carried out to examine variation of the main response parameters under seismic excitations with increasing Peak ground acceleration (PGA) levels. Two global damage indices based on the safety factor and bending moment are introduced to assess the relationship between PGA and the damage levels.

The results obtained indicate that the sheet pile quay wall can safely withstand seismic loads up to PGAs of 0.35 g and that above 0.45 g, care should be taken with the risk of reaching the ultimate moment capacity of the steel sheet pile. However, for PGAs greater than 0.5 g, it was clearly demonstrated that the excessive deformations with material are likely to occur in the soil layers and in the structural elements.

The main contribution of the present work is a new double seismic damage index for a steel sheet pile supported quay wharf. The numerical modeling is first validated in the static case. Then, the results obtained by performing several incremental dynamic analyses are exploited to evaluate the degradation of the soil safety factor and the seismic capacity of the pile sheet wall. Computed values of the proposed damage indices of the considered quay wharf are a practical helping tool for decision-making regarding the seismic safety of the structure.

The purpose of this study is to get benefits from manufacturing harmful wastes is by using them as a reinforcement with epoxy matrix composite materials to improve the damping characteristics in applications such as machine bases, rockets, satellites, missiles, navigation equipment and aircraft as large structures, and electronics as such small structures. Vibration causes damaging strains in these components.

By adding machining chips with weight percentages of 5, 10, 15 and 20 Wt.%, with three different chip lengths added for each percentage (0.6, 0.8 and 1.18 mm), the three-point bending and damping characteristics tests are utilized to examine how manufacturing waste impacts the mechanical properties. Following that, the optimal lengths and the chip-to-epoxy ratio are determined. The chip dispersion and homogeneity are assessed using a field emission scanning electron microscope.

Waste copper alloys can be used to enhance the vibration-dampening properties of epoxy resin. The interface and bonding between the resin and the chip are crucial for enhancing the damping capabilities of epoxy. Controlling the flexural modulus by altering the chip size and quantity can change the damping characteristics because the two variables are inversely related. The critical chip size is 0.8 mm, below which smaller chips cannot evenly transfer, and disperse the vibration force to the epoxy matrix and larger chips may shatter and fracture.

The main source of problems in machine tools, aircraft and vehicle manufacturing is vibrations generated in the structures. These components suffer harmful strains due to vibration. Damping can be added to these structures to get over these problems. The distribution of energy stored as a result of oscillatory mobility is known as damping. To optimize the serving lifetime of a dynamic suit, this is one of the most important design elements. The use of composites in construction is a modern method of improving a structure's damping capacity. Additionally, it has been demonstrated that composites offer better stiffness, strength, fatigue resistance and corrosion resistance. This research aims to reduce the vibration effect by using copper alloy wastes as dampers.

This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit environmental constraints, while ensuring the reliability of the robot system.

The authors propose a novel DMP that takes into account environmental constraints to enhance the generality of the robot motion skill learning method. First, based on the real-time state of the robot and environmental constraints, the task space is divided into different regions and different control strategies are used in each region. Second, to ensure the effectiveness of the generalized skills (trajectories), the control barrier function is extended to DMP to enforce constraint conditions. Finally, a skill modeling and learning algorithm flow is proposed that takes into account environmental constraints within DMPs.

By designing numerical simulation and prototype demonstration experiments to study skill learning and generalization under constrained environments. The experimental results demonstrate that the proposed method is capable of generating motion skills that satisfy environmental constraints. It ensures that robots remain in a safe position throughout the execution of generation skills, thereby avoiding any adverse impact on the surrounding environment.

This paper explores further applications of generalized motion skill learning methods on robots, enhancing the efficiency of robot operations in constrained environments, particularly in non-point-constrained environments. The improved methods are applicable to different types of robots.

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.