Background: The primary function of a suspension system is to isolate the vehicle body from road irregularities thus providing the ride comfort and to support the vehicle and provide stability. The suspension system has to perform conflicting requirements; hence, a passive suspension system is replaced by the active suspension system which can supply force to the system. Active suspension supplies energy to respond dynamically and achieve relative motion between body and wheel and thus improves the performance of suspension system. Methods: This study presents modelling and control optimization of a nonlinear quarter car suspension system. A mathematical model of nonlinear quarter car is developed and simulated for control and optimization in Matlab/ Simulink® environment. Class C road is selected as input road condition with the vehicle traveling at 80 kmph. Active control of the suspension system is achieved using FLC and PID control actions. Instead of guessing and or trial and error method, genetic algorithm (GA)-based optimization algorithm is implemented to tune PID parameters and FLC membership functions' range and scaling factors. The optimization function is modeled as a multi-objective problem comprising of frequency weighted RMS seat acceleration, Vibration dose value (VDV), RMS suspension space, and RMS tyre deflection. ISO 2631-1 standard is adopted to assess the ride and health criterion. Results: The nonlinear quarter model along with the controller is modeled and simulated and optimized in a Matlab/Simulink environment. It is observed that GA-optimized FLC gives better control as compared to PID and passive suspension system. Further simulations are validated on suspension system with seat and human model. Parameters under observation are frequency-weighted RMS head acceleration, VDV at the head, crest factor, and amplitude ratios at the head and upper torso (AR_h and AR_ut). Simulation results are presented in time and frequency domain. Conclusion: Simulation results show that GA-based FLC and PID controller gives better ride comfort and health criterion by reducing RMS head acceleration, VDV at the head, CF, and AR_h and AR_ut over passive suspension system.

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IntroductionOptimization of machining parameters is essential for improving expected outcome of any machining operation.Case DescriptionThe aim of this work is to find out optimum values of machining parameters to achieve minimal surface roughness during milling operation of GFRP.Discussion and EvaluationIn this machining operation speed, depth of cut and feed rate are considered as parameters affecting surface roughness and Design of Experiment (DOE)-Taguchi method tool is used to plan experiments and analyse results.ConclusionAnalysis of experimental results presents optimum values of these three parameters to achieve minimal surface roughness with speed as a major contributing factor. Speed—200 rpm, depth of cut—1.2 mm and feed—40 mm/min are an optimal combination of machining parameter to produce minimal surface roughness during milling of GFRP.

This article presents a method for the electrochemical preparation of a coating of nickel-silica nanocomposites on a carbon steel substrate. The incorporation of hydrophilic silica particles into the Ni composite coating during co-electrodeposition is so difficult due to the small size and the hydrophilicity of SiO 2 particle, generally less than 2 v% of silica is incorporated into the composite at different current densities, agitation speeds and silica concentrations. The effect of the presence of four surfactants, namely cocamidopropyl betaine (CAPB), decylglycoside (DG), cetyltrimethyl ammonium chloride (CTAC) and ammonium lauryl ether sulfate (ALES), on overcoming this problem was investigated in this research, and the surfactants were found to greatly influence the surface charge of silica, silica incorporation percentage and the microstructure of the composite. In fact, upon increasing the internal stresses, the products prepared in the presence of CAPB and DG were found to crack to some degree. CTAC was found to lead to entrapment mode silica co-deposition in the Ni coating. Furthermore, the addition of ALES into an electrolyte bath negatively supercharged silica surfaces and increased silica dispersion, which led to a dramatic increase in the silica incorporation percentages to around 14 v%. The results showed that Ni-SiO 2 composites prepared in the presence of ALES had better corrosion resistance, hardness and wear properties.

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This paper presents the modeling and optimization of quarter car suspension system using Macpherson strut. A mathematical model of quarter car is developed, simulated and optimized in Matlab/Simulink ® environment. The results are validated using test rig. The suspension system parameters are optimized using a genetic algorithm for objective functions viz. vibration dose value (VDV), frequency weighted root mean square acceleration (hereafter called as RMS acceleration), maximum transient vibration value, root mean square suspension space and root mean square tyre deflection. ISO 2631-1 standard is adopted to assess ride and health criterion. Results shows that optimum parameters provide ride comfort and health criterions over classical design. The optimization results are experimentally validated using quarter car test setup. The genetic algorithm optimization results are further extended to the artificial neural network simulation and prediction model. Artificial neural network model is carried out in Matlab/Simulink ® environment and Neuro Dimensions. Simulation, experimental and predicted results are in close correlation. The optimized system reduces the values of VDV by 45%. Also, RMS acceleration is reduced by 47%. Thus, the optimized system improved ride comfort by reducing RMS acceleration and improved health criterion by reducing the VDV. Finally ANN can be used for predicting the optimum suspension parameters values with good agreement.

In this study, the integrated Taguchi-simulated annealing (SA) approach is applied to examine the wear behaviour of silicon nitride (Si 3 N 4)hexagonal boron nitride (hBN). Wear tests for Si 3 N 4-hBN composite versus steel (ASTM 316L) disc were carried out for a dry sliding conditions in a so-called pin-on-disc arrangement. The tests were realized at % volume of hBN 0, 4, 8, 12, 16 in Si 3 N 4 under the loads of 5, 10, 15, 20, 25 N. The wear rate (WR) was analyzed using Taguchi-signal to noise ratio approach with the aim of finding optimal combination of load and % volume of hBN in Si 3 N 4. By applying the analysis of variance, it was also found that the greatest impact on wear rate has interaction of load and % volume of hBN with percentage effect of 51.89%, then % volume of hBN with 35.04% and load with 13.06%. The experimental results are further utelized to develop the second-order, linear mathematical model. Further, this model is processed with simulated annealing (SA) to find the optimal combination of load and % volume of hBN to minimize wear rate. Combined Taguchi-SA approach was successfully used to predict the optimal combination of load and % volume of hBN in Si 3 N 4 to minimize wear rate of Si 3 N 4. The dominant wear mechanism is adhesive wear as confirmed by scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS).

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