Magnetorheological (MR) fluid is among the smart materials that can change its default properties with the influence of a magnetic field. Typical application of an MR fluid based device involves an adjustable damper which is commercially known as an MR fluid damper. It is used in vibration control as an isolator in vehicles and civil engineering applications. As part of the device development process, proper understanding of the device properties is essential for reliable device performance analysis. This study introduce an accurate and fast prediction model to analyse the dynamic characteristics of the MR fluid damper. This study proposes a new modelling technique called Extreme Learning Machine (ELM) to predict the dynamic behaviour of an MR fluid damper hysteresis loop. This technique was adopted to overcome the limitations of the existing models using Artificial Neural Networks (ANNs). The results indicate that the ELM is extremely faster than ANN, with the capability to produce high accuracy prediction performance. Here, the hysteresis loop, which represents the relationship of force-displacement for the MR fluid damper, was modelled and compared using three different activation functions, namely, sine, sigmoid and hard limit. Based on the results, it was found that the prediction performance of ELM model using the sigmoid activation functions produced highest accuracy, and the lowest Root Mean Square Error (RMSE).
This study introduces a novel platform to predict complex modulus variables as a function of the applied magnetic field and other imperative variables using machine learning. The complex modulus prediction of magnetorheological (MR) elastomers is a challenging process, attributable to the material’s highly nonlinear nature. This problem becomes apparent when considering various possible fabrication parameters. Furthermore, traditional parametric modeling methods are limited when applied to solve larger-scale cases involving large databases. Consequently, the application of non-parametric modeling such as machine learning has gained increasing attraction in recent years. Therefore, this work proposes a data-driven approach for predicting multiple input-dependent complex moduli using feedforward neural networks. Besides excitation frequency and magnetic flux density as operating conditions, the inputs consider compositions and curing conditions represented by magnetic particle weight percentage and the curing magnetic field, respectively. Extreme learning machines and artificial neural networks were used to train the models. The simulation results obtained at various curing conditions and other inputs confirm that the predicted complex modulus has high accuracy with an R2 of about 0.997, as compared to the experimental results. Furthermore, the predicted complex modulus pattern and magnetorheological effect agree with the experimental data using both the learned and unlearned data.
Human-robot interactions carry several challenges, the most important being the risk of injury to the human. In industrial robotic systems, robots are mostly caged and isolated from humans in a safety guard environment. However, as time has passed, the use of domestic robots has emerged, leading to a high need in research on robot safety in domestic settings. Human-Robot collaboration is still in an initial stage; thus, safety assessments in domestic environments are critical in the field of collaborative robots or cobots, with simulations being the first stage of research. In this study, a preliminary investigation on the simulation of human’s safety throughout human-robot interactions in home surroundings with no safety fence is presented. A simulation model is designed and developed with Gazebo in the Robot Operating System, ROS-based, to simulate the human-robot interaction. In the robot trajectory, safety interaction can be simulated. In one example, the robot’s speed can be reduced before a collision with a human about to happen, and it can be minimized the risk of the collision or reduce the damage of the risk. After the successful simulation, this can be applied to the real robot in a domestic working environment.
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