Response Surface Methodology Models for Engine Mount Optimization in Motorcycles

2006 ◽  
Author(s):  
Sudhir Kaul ◽  
Anoop K. Dhingra ◽  
Timothy G. Hunter
Keyword(s):  
Response Surface Methodology ◽  
Theoretical Model ◽  
Response Surface ◽  
Optimization Problem ◽  
Vibration Isolation ◽  
Statistical Significance ◽  
Substantial Reduction ◽  
Fractional Factorial ◽  
Engine Mount ◽  
Rsm Model

This paper presents Response Surface Methodology (RSM) modeling techniques to solve the engine mount optimization problem for motorcycle applications. A theoretical model that represents the structural dynamics of the engine mount system in motorcycles is first used to build the RSM model. The RSM model is then used to solve the engine mount optimization problem to enhance vibration isolation. This leads to a substantial reduction in computational effort and simplifies the governing model, yielding an input-output relationship between the variables of interest. Design of Experiments (DOE) techniques are used to build the RSM model from the theoretical model. Full factorial and fractional factorial formulations are used to construct the governing experiments. Normal probability plots are used to determine the statistical significance of the resulting coefficients. The statistically significant variables are then used to build the response surface. The design variables for the engine mount optimization problem include mount stiffness, position and orientation vectors. The influence of the orientation variables is highly non-linear and is difficult to model by using a response surface consisting of lower order terms only. Two separate algorithms are proposed to overcome this problem and the results from the RSM models are compared to those from the theoretical model.

Response Surface Algorithms for Engine Mount Optimization in Motorcycles

2009 ◽  
Author(s):  
Sudhir Kaul ◽  
Anoop K. Dhingra
Keyword(s):  
Response Surface ◽  
Optimization Problem ◽  
Vibration Isolation ◽  
Design Space ◽  
Substantial Reduction ◽  
Response Surface Model ◽  
Fractional Factorial ◽  
Surface Model ◽  
Design Variables ◽  
Engine Mount

This paper presents a Response Surface Modeling (RSM) approach for solving the engine mount optimization problem for a motorcycle application. A theoretical model that captures the structural dynamics of a motorcycle engine mount system is first used to build the response surface model. The response surface model is then used to solve the engine mount optimization problem for enhanced vibration isolation. Design of Experiments (DOE), full factorial and fractional factorial formulations, are used to construct the governing experiments. Normal probability plots are used to determine the statistical significance of the variables and the significant variables are then used to build the response surface. The design variables for the engine mount optimization problem include mount stiffness, position vectors and orientation vectors. It is seen that RSM leads to a substantial reduction in computational effort and yields a simplified input-output relationship between the variables of interest. However, as the number of design variables increases and as the response becomes irregular, conventional use of RSM is not viable. Two algorithms are proposed in this paper to overcome the issues associated with the size of the governing experiments and problems associated with modeling of the orientation variables. The proposed algorithms divide the design space into sub-regions in order to manage the size of the governing experiments without significant confounding of variables. An iterative procedure is used to overcome high response irregularity in the design space, particularly due to orientation variables.

Kriging Modeling for Engine Mount Optimization in Motorcycles

2009 ◽  
Author(s):  
Sudhir Kaul ◽  
Anoop K. Dhingra
Keyword(s):  
Correlation Function ◽  
Theoretical Model ◽  
Optimization Problem ◽  
Design Space ◽  
Substantial Reduction ◽  
Kriging Model ◽  
Surrogate Models ◽  
Fractional Factorial ◽  
Engine Mount ◽  
Selection Of

This paper addresses two critical aspects associated with the successful use of a Kriging model for solving the engine mount optimization problem. The two aspects are the selection of an appropriate correlation function and the use of a suitable governing design for sampling within the design space. The selection of a correlation function is critical in building a Kriging model since the function should accurately represent the behavior of the response over the entire design space. Whereas the Gaussian correlation function is most commonly used for building Kriging models, it is generally suitable for only those processes or systems which have a relatively smooth response within the entire design space. The correlation functions that have been evaluated in this paper for building the Kriging models for solving the engine mount optimization problem are as follows: Exponential, Linear Spline, Matern’s 3/2, Matern’s 5/2 and Gaussian. Three types of experimental designs – Fractional Factorial, D-optimal and Latin Hypercube, have been used to select the sampling points for making simulation runs in order to build the Kriging models. A theoretical model that represents the dynamics of the engine mount system in a motorcycle application has been used to build all the surrogate models. The Kriging models are then used to solve the engine mount optimization problem for enhanced vibration isolation with mount stiffness, mount orientation and mount location as the design variables. The optimization results of the Kriging models are compared to the results of the theoretical model. It is found that the D-optimal design in conjunction with Matern’s 3/2 correlation function provides the best results. This can be attributed to the high irregularity of the response function in the design space, especially due to the influence of orientation variables. The use of the surrogate Kriging model simplifies the governing model and leads to a substantial reduction in computational effort for solving the optimization problem. Based on the results, it can be concluded that the Kriging modeling technique can be successfully used to build surrogate models for the engine mount problem for design iterations as well as for design optimization if the correlation function and the governing design are judiciously chosen.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

scholarly journals Efficient Removal of Levofloxacin by Activated Persulfate with Magnetic CuFe2O4/MMT-k10 Nanocomposite: Characterization, Response Surface Methodology, and Degradation Mechanism

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3583
Author(s):  
Junying Yang ◽  
Minye Huang ◽  
Shengsen Wang ◽  
Xiaoyun Mao ◽  
Yueming Hu ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Photoelectron Spectroscopy ◽  
Degradation Mechanism ◽  
Sol Gel ◽  
Combustion Method ◽  
Copper Ferrite ◽  
X Ray ◽  
Rsm Model

In this study, a magnetic copper ferrite/montmorillonite-k10 nanocomposite (CuFe2O4/MMT-k10) was successfully fabricated by a simple sol-gel combustion method and was characterised by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), the Brunner–Emmett–Teller (BET) method, vibrating sample magnetometer (VSM), and X-ray photoelectron spectroscopy (XPS). For levofloxacin (LVF) degradation, CuFe2O4/MMT-k10 was utilized to activate persulfate (PS). Due to the relative high adsorption capacity of CuFe2O4/MMT-k10, the adsorption feature was considered an enhancement of LVF degradation. In addition, the response surface methodology (RSM) model was established with the parameters of pH, temperature, PS dosage, and CuFe2O4/MMT-k10 dosage as the independent variables to obtain the optimal response for LVF degradation. In cycle experiments, we identified the good stability and reusability of CuFe2O4/MMT-k10. We proposed a potential mechanism of CuFe2O4/MMT-k10 activating PS through free radical quenching tests and XPS analysis. These results reveal that CuFe2O4/MMT-k10 nanocomposite could activate the persulfate, which is an efficient technique for LVF degradation in water.

Optimization of Magnesium Separation and Extraction from Boron Slurry Using Response Surface Methodology

2011 ◽  
Vol 366 ◽  
pp. 366-369
Author(s):  
Feng Gao ◽  
Rong Fu ◽  
Ming Yang Qian ◽  
Zhu Min Wang ◽  
Xiang Zhang
Keyword(s):  
Response Surface Methodology ◽  
Reaction Time ◽  
Factorial Design ◽  
Response Surface ◽  
Reaction Temperature ◽  
Fractional Factorial Design ◽  
Design Criterion ◽  
Fractional Factorial ◽  
Effective Factors

Response surface methodology was used to optimize the soaking Mg leaching ratio from the boron slurry screened by 25 fractional factorial design. Five effective factors such as H2SO4 concentrations, reaction time, reaction temperature and stir velocity were tested by using 25 fractional factorial design criterion and three effective factors H2SO4 concentrations, reaction time and reaction temperature showed significant effect(P2SO4 concentrations of 0.29mol/l, reaction time of 90 min and reaction temperature of 50°C. Three runs of additional confirmation experiments were conducted. The mixture magnesium leaching value was 58.20%.

Quantification of tool chatter and metal removal rate using wavelet denoising and statistical approach

2018 ◽  
Vol 49 (2) ◽  
pp. 62-81 ◽  
Author(s):  
Shailendra Kumar ◽  
Bhagat Singh
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Removal ◽  
Removal Rate ◽  
Statistical Significance ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Chatter Index ◽  
Tool Chatter

Tool chatter is an unavoidable phenomenon encountered in machining processes. Acquired raw chatter signals are contaminated with various types of ambient noises. Signal processing is an efficient technique to explore chatter as it eliminates unwanted background noise present in the raw signal. In this study, experimentally recorded raw chatter signals have been denoised using wavelet transform in order to eliminate the unwanted noise inclusions. Moreover, effect of machining parameters such as depth of cut ( d), feed rate ( f) and spindle speed ( N) on chatter severity and metal removal rate has been ascertained experimentally. Furthermore, in order to quantify the chatter severity, a new parameter called chatter index has been evaluated considering aforesaid denoised signals. A set of 15 experimental runs have been performed using Box–Behnken design of experiment. These experimental observations have been used to develop mathematical models for chatter index and metal removal rate considering response surface methodology. In order to check the statistical significance of control parameters, analysis of variance has been performed. Furthermore, more experiments are conducted and these results are compared with the theoretical ones in order to validate the developed response surface methodology model.

Engine Mount Optimization for Vibration Isolation in Motorcycles

2006 ◽  
Author(s):  
Sudhir Kaul ◽  
Anoop K. Dhingra ◽  
Timothy G. Hunter
Keyword(s):  
Optimization Problem ◽  
Vibration Isolation ◽  
Total Force ◽  
Engine Mounts ◽  
The Road ◽  
Design Variables ◽  
Swing Arm ◽  
Engine Mount ◽  
Six Degree Of Freedom

This paper presents a comprehensive model to capture the dynamics of a motorcycle system in order to evaluate the quality of vibration isolation. The two main structural components in the motorcycle assembly - the frame and the swing-arm - are modeled using reduced order finite element models; the power-train assembly is modeled as a six degree-of-freedom (DOF) rigid body connected to the frame through the engine mounts and to the swing-arm through a shaft assembly. The engine mounts are modeled as tri-axial spring-damper systems. Models of the front-end assembly as well as front and rear tires are also included in the overall model. The complete vehicle model is used to solve the engine mount optimization problem so as to minimize the total force transmitted to the frame while meeting packaging and other side constraints. The mount system parameters - stiffness, position and orientation vectors - are used as design variables for the optimization problem. The imposed loads include forces and moments due to engine imbalance as well as loads transmitted due to irregularities in the road surface through the tire patch.

Shape Optimization of Engine Mounts for Enhanced Vibration Isolation

2013 ◽  
Author(s):  
Fadi Alkhatib ◽  
Anoop K. Dhingra
Keyword(s):  
Finite Element Analysis ◽  
Optimization Problem ◽  
Vibration Isolation ◽  
Elastic Stiffness ◽  
Nonlinear Finite Element ◽  
Optimum Shape ◽  
Parametric Approach ◽  
Element Analysis ◽  
Engine Mounts ◽  
Engine Mount

In this article, a parametric approach is used to determine the optimum geometric shape of an engine mount in order to minimize the vibrations transmitted to and from the engine. The engine mount used is an elastomeric mount which is made of rubber. For proper vibration isolation, elastomeric mounts are designed such that they have the necessary elastic stiffness rate characteristics in all directions. An optimization problem is first solved to determine the optimum values of stiffness, orientation and location of the mount system such that vibrations transmitted are minimal. Besides determining the optimum mount stiffness values, knowing the optimum shape of the rubber mount is also vital. The shape of the mount is determined such that it meets the required stiffness of the mounting system obtained from the dynamic analysis. A nonlinear finite element analysis is used to determine the final optimum shape and stiffness of the mount.

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