Chandramohan Sujatha

Magnetorheological (MR) dampers are extensively utilized across several technical fields owing to their capacity to modify the rheological characteristics of MR fluid. Significant effort has been invested on enhancing MR damper (MRD). A prevalent method to enhance the performance of the MRD is by structural optimization. Equations governing shapes can provide other opportunities and yield superior results, despite the fact that most MRD optimizations concentrate on regions with conventional geometries. This study develops and modifies a novel MRD utilizing parametric Bezier and B-spline curves to optimize the regulated output damping force while minimizing power and mass of the MR valve for the prosthetic ankle. Initially, we provide many basic design concepts based on the two primary categories of MRD components. Subsequently, parametric curves are employed to generate the piston form, a crucial element of the magnetic circuit. Several geometric factors within the magnetic circuit, together with the optimal parametric curve, are determined following optimization and simulation. The shape of the MR valve is enhanced through the integration of an optimization with multiple goals functions combined using NSGA-II in MATLAB and finite element electromagnetic analysis in the ANSYS-APDL environment. Furthermore, we have developed and assessed the improved MRD. Finally, we assess the proposed MRD against our previous studies. The experimental characterization outcomes and the modeling results are significantly complementary. The proposed MRD exhibits a significant improvement compared to conventional MRD.

Bearing elements are widely used in rotating machines and their failure results in a considerable amount of downtime of the machines. The aim of this work is to classify defects in a bearing. Three types of classification have been done: (i) Binary classification: classification as non-defective or defective bearing, (ii) 3-class classification such as non-defective, defective with inner ring defect and defective with roller defect and finally (iii) 7-class classification corresponding to no defect condition, three ring defect conditions pertaining to indentations of three different sizes on the inner ring and three roller defect conditions corresponding to indentations of three different sizes on the roller. The open-access data generated using a rolling bearing test rig from the Politecnico Di Torino, Italy, has been used for this work. The data had been obtained using 2 accelerometers on two bearing housings for multiple load and speed combinations. For classification, in the present work, classical ML algorithms such as logistic regression (LR), K-Nearest Neighbour (K-NN) classification algorithm, random forest (RF), support vector classifier (SVC) and kernel support vector machine (KSVM) have been used. All these techniques gave very promising results, the classification accuracy varying from 0.7969 to 0.9996 for all speed-load conditions. Such classification work across multiple operational conditions, with multiple fault conditions and multiple signatures with faulty components, has not been reported.

Extremely uncomfortable levels of bounce and pitch vibrations are produced when a CV moves over uneven terrain. The present study was carried out to ascertain the vibrational response at the driver’s, commander’s and trooper’s seats. A 23 -degrees of freedom (DOF) lumped parameter 3-D model of a combined CV and human body was made. The vehicle had 15 DOF corresponding to the bounce, pitch and roll of the hull (sprung mass) and bounce motions of the 12 wheel stations (unsprung masses) on either side. The human body was idealized as having 8 DOF corresponding to bounce motions of the pelvis, abdomen, diaphragm, thorax, torso, back and head. The seat was also assigned a bounce DOF. The lumped masses of the body parts were distributed and connected by springs. The differential equations of motion for the linear rigid body model were formulated and the natural frequencies of different parts of the human body and the military tank were determined by eigenvalue analysis using MATLAB. Subsequently, vertical and longitudinal accelerations at the driver's, commander's and trooper's seats were measured on both the Sinusoidal track and Aberdeen Proving Ground (APG) using accelerometers connected to the DEWESoft data acquisition system (DAS). Fast Fourier transform (FFT) spectra and power spectral densities (PSDs) were determined at each location. Also, the measured root mean square (RMS) accelerations computed in 1/3 octave bands were multiplied with ISO weighting factors and were compared with ISO ride comfort boundaries for assessment of ride comfort. Thereafter, exposure values, A(8) and vibration dose value (VDV) were calculated. A full multi-body dynamics model (MBD) was idealized to get insight into the dynamic behavior of the CV. The vibrational response of the CV was simulated during steady-state runs on Sinusoidal and Aberdeen Proving Ground (APG) tracks through MSC ADAMS (ATV) and the results were compared with those obtained from experimental work. The measured acceleration data correlated well with the computed results.