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Machine learning’s prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learning applications for predicting fluid rheology across various fields is rare. This article aims to identify and overview effective machine learning strategies for analyzing and predicting fluid rheology. Covering flow curve identification, yield stress characterization, and viscosity prediction, it compares machine learning techniques in these areas. The study finds common objectives across fluid models: flow curve correlation, rheological behavior dependency on variables, soft sensor applications, and spatial–temporal analysis. It is noted that models for one type can often adapt to similar behaviors in other fluids, especially in the first two categories. Simpler algorithms, such as feedforward neural networks and support vector regression, are usually sufficient for cases with narrow range variability and small datasets. Advanced methods, like hybrid approaches combining metaheuristic optimization with machine learning, are suitable for complex scenarios with multiple variables and large datasets. The article also proposes a reproducibility checklist, ensuring consistent research outcomes. This review serves as a guide for future exploration in machine learning for fluid rheology prediction.

This article aims to investigate the structural behavior of beam–column joints subjected to axial force. The geometry used is the addition of a number of beam connections to the column, and the differences in the numbers of beams used are 1, 2, 4, and 4, denoted as V1, V2, V3, and V4, respectively.

The dynamic relationship between current and pressure in magnetorheological (MR) valves is essential for the design of adaptive rehabilitation devices aimed at health rehabilitation for disabled individuals, yet it remains under-explored in existing modeling approaches. Accurately capturing this relationship is vital to predict the pressure drop response to current variations, facilitating the development of effective control systems in such rehabilitation applications. This study employs a linear black-box modeling approach to characterize the current–pressure dynamics of an annular MR valve. Experimental data are used to develop a set of transfer function models, with parameters identified through MATLAB's system identification tools, utilizing invariant variable regression and the Levenberg–Marquardt (LM) iteration. The modeling yielded a 14th-order transfer function, labeled TF14, which closely aligns with experimental data, achieving a root mean square error of 12.64%. These findings contribute valuable insights into the current–pressure dynamics of MR valves and establish a foundational model for adaptive rehabilitation devices designed for individuals with disabilities. [ABSTRACT FROM AUTHOR]

Rim wheel testing through the SAE standard is necessary for driving safety. This study focused on rim wheel tests carried out using the dynamic radial fatigue test method, which has been included in the SAE standard using Fusion360 for the design and ANSYS for the simulation. With different parameters for the rim wheel type, only some parameters of the tested rim wheels were able to pass the standardization by SAE; 16 rim wheels passed the test, while the other 11 rim wheels did not pass. Simulation results suggested that variations in the thickness, geometry, and material affected the displacement of the safety factor, which was inversely proportional. In addition, the variation in the rim wheel produced a change in the safety factor due to changes in its mass and cost, which were directly proportional. The results of this study will aid in rim wheel design, not only in terms of achieving the best performance but also with regard to cost efficiency.

The development of the global economy has led to a rise in ship traffic. As a result, the risk of accidents, such as collisions between ships and grounding, has also increased. Different failure criteria to capture these accidents have been introduced by researchers. Therefore, the purpose of this study was to determine the essential distinction between these failure criteria. The simulations suggest that failure criteria based on the maximum stress result in a slightly higher rupture strain value, greater crack propagation, and higher internal energy than those based on the maximum strain. Furthermore, using a larger mesh size compared with the size of the test specimen appears to greatly affect the validity of the simulation results.

This work aims to investigate the mechanical behavior of steel-plated structures under a raking incident and to quantify the effect of the mesh size in nonlinear finite element (NLFE) analysis. To conveniently comprehend nonlinear phenomena, i.e., the grounding which takes place in this work, a series of theoretical contact formulations was defined. In the main analysis, raking, which is a part of the grounding scenario, was strictly assumed as contact between a tanker, which was assumed to have thin-walled steel, and a seabed rock in the form of a solid obstruction. Designed raking scenarios were calculated using the FE method by using the nonlinear phenomena of the material behavior in the calculation. The findings of this work indicated that the possibility of expanding the recommended mesh size in FE simulation should be evaluated by quantifying the behavior of structural responses, such as energy, the force damage pattern, and acceleration, subjected to a variety of applied meshing techniques. The results concluded that a notable difference occurred when the mesh size was more than 132 mm (ratio 11 based on the plate dimension in this work), and this size is strictly recommended to be used for calculation of the element length-to-thickness (ELT) ratio. Assessment in time simulation showed that applying larger mesh sizes will reduce the simulation time but increase the maximum values of the crashworthiness parameters, i.e., energy, force, acceleration, and displacement.

Magnetorheological elastomers (MREs) are smart materials. Their rheological properties can be controlled by applying an external magnetic field. MRE is a solid rubbery material that can change its modulus through the application of a magnetic field. This article presents the research on the simulation, manufacturing, and testing of vibration insulators. The first step in MRE vibration isolator design. A sufficient magnetic field for the stiffness change is considered in the material selection of the vibration isolator. The effectiveness of electromagnetic circuits in generating magnetic fields to minimize vibration is demonstrated using finite element method magnetics software. A vibration isolator test rig is installed as a second step. Lastly, different effects of current input on MRE isolators have been examined. The higher current input is more effective in eliminating vibration using the MRE isolation system. [ABSTRACT FROM AUTHOR]

Shell Eco-Marathon (SEM) is an international competition among university students that involves designing, building, and driving energy-efficient cars. The car frame is the most crucial aspect influencing the strength of the car. This research aims to obtain maximum torsional strength with variations in the material and thickness of the frame. Calculation and testing are done using the simulation method to obtain a strong car frame. This simulation method is calculated by a series of finite element analyses. Then, data from the simulation method are obtained in the form of deformation and safety factors. By comparing the moment received with its deformation, torsional stiffness is then obtained. Furthermore, the torsional stiffness is divided by the weight to produce a value ratio. It is known that the factor which has the most significant influence on the difference in torsional stiffness of each variation is the shear modulus of the material used. In contrast, the weight of the chassis is influenced by the density of the material and the thickness of the chassis. Additionally, the safety factor of each variation is strongly influenced by the strength of the chassis structure itself. The results of this study will demonstrate the car frame design with the best performance.

A research subject in structural engineering is the problem of vibration under a loading object. The two-dimensional (2D) model of a structure under loading is an example. In general, this case uses an object that is given a random frequency, which then causes various changes in shape depending on the frequency model. To determine the difference in performance by looking at the different forms of each mode, modal analysis with ANSYS was used. The samples to be simulated were metal plates with three variations of the model, namely, a virgin metal plate without any holes or stiffness, plates with given holes, and metal plates with stiffness on one side. The model was simulated with modal analysis, so that 20 natural frequencies were recorded. The sample also used different materials: low-carbon steel materials (AISI 304), marine materials (AISI 1090), and ice-class materials (AR 235). Several random-frequency models proved the deformation of different objects. Variations of sheet-metal designs were applied, such as pure sheet metal, giving holes to the sides, and stiffening the simulated metal sheet.

This study aims to review research the progress on factors that affect the 3D printing results of the fused deposition modeling (FDM) process. The review is carried out by mapping critical parameters and characteristics determining FDM parameters, the effects of each parameter, and their interaction with other parameters. The study started from the filament manufacturing process, filament material types, and printing parameters of FDM techniques. The difference in each section has determined different parameters, and the respective relationships between parameters and other determinants during printing have a significant effect on printing results. This study also identifies several vital areas of previous and future research to optimize and characterize the critical parameters of the FDM printing process and FDM filament manufacturing.

A passive controlled ankle foot orthosis (PICAFO) used a passive actuator such as Magnetorheological (MR) brake to control the ankle stiffness. The PICAFO used two kinds of sensors, such as Electromyography (EMG) signal and ankle position (two inputs) to determine the amount of stiffness (one output) to be generated by the MR brake. As the overall weight and design of an orthotic device must be optimized, the sensor numbers on PICAFO wanted to be reduced. To do that, a machine learning approach was implemented to simplify the previous stiffness function. In this paper, Non-linear Autoregressive Exogeneous (NARX) neural network were used to generate the simplified function. A total of 2060 data were used to build the network with detail such as 1309 training data, 281 validation data, 281 testing data 1, and 189 testing data 2. Three training algorithms were used such as Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The result shows that the function can be simplified into one input (ankle position) – one output (stiffness). Optimized result was shown by the NARX neural network with 15 hidden layers and trained using Bayesian Regularization with delay 2. In this case, the testing data shows R-value of 0.992 and MSE of 19.16.

Collisions arising from lane departures have contributed to traffic accidents causing millions of injuries and tens of thousands of casualties per year worldwide. Many related studies had shown that single vehicle lane departure crashes accounted largely in road traffic deaths that results from drifting out of the roadway. Hence, automotive safety has becoming a concern for the road users as most of the road casualties occurred due to driver's fallacious judgement of vehicle path. This paper proposes a vision-based lane departure warning framework for lane departure detection under daytime and night-time driving environments. The traffic flow and conditions of the road surface for both urban roads and highways in the city of Malacca are analysed in terms of lane detection rate and false positive rate. The proposed vision-based lane departure warning framework includes lane detection followed by a computation of a lateral offset ratio. The lane detection is composed of two stages: pre-processing and detection. In the pre-processing, a colour space conversion, region of interest extraction, and lane marking segmentation are carried out. In the subsequent detection stage, Hough transform is used to detect lanes. Lastly, the lateral offset ratio is computed to yield a lane departure warning based on the detected X-coordinates of the bottom end-points of each lane boundary in the image plane. For lane detection and lane departure detection performance evaluation, real-life datasets for both urban roads and highways in daytime and night-time driving environments, traffic flows, and road surface conditions are considered. The experimental results show that the proposed framework yields satisfactory results. On average, detection rates of 94.71% for lane detection rate and 81.18% for lane departure detection rate were achieved using the proposed frameworks. In addition, benchmark lane marking segmentation methods and Caltech lanes dataset were also considered for comparison evaluation in lane detection. Challenges to lane detection and lane departure detection such as worn lane markings, low illumination, arrow signs, and occluded lane markings are highlighted as the contributors to the false positive rates.