Monte Carlo Simulation of the Manufacturing Tolerance of FDBs to Identify the Sensitive Design Variables Affecting the Performance of a Disk-Spindle System

2014 ◽  
Author(s):  
J. H. Lee ◽  
M. H. Lee ◽  
H. K. Jang ◽  
G. H. Jang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Journal Bearing ◽  
Fluid Dynamic ◽  
Critical Mass ◽  
Groove Depth ◽  
Friction Torque ◽  
Spindle System ◽  
Manufacturing Tolerance ◽  
Design Variables

This research investigates the Monte Carlo simulation of manufacturing tolerance of FDBs to identify the sensitive design variables for the friction torque of fluid dynamic bearings (FDBs) and the critical mass of disk-spindle system supported by FDBs. We analyze the characteristics according to design variables of FDBs and it shows that the clearance of journal bearing is most sensitive design variable of both friction torque and critical mass. Also the groove to groove and ridge ratio and groove depth of grooved journal bearing which are manufactured by ECM are also sensitive to determine the friction torque and the critical mass of the FDBs, respectively. This research can be utilized to manage manufacturing tolerance to maintain the consistent performance of FDBs and a disk-spindle system in a HDD.

Effect of an Hourglass-Shape Tapered Sleeve on the Performance of the Fluid Dynamic Bearings of a HDD Spindle Motor

2013 ◽  
Author(s):  
J. H. Lee ◽  
M. H. Lee ◽  
G. H. Jang
Keyword(s):  
Rough Surface ◽  
Fluid Dynamic ◽  
Dynamic Performance ◽  
Rotating Disk ◽  
Groove Depth ◽  
Spindle Motor ◽  
Spindle System ◽  
Thrust Bearings ◽  
Steel Sleeve ◽  
Bearing Ball

Fluid dynamic bearings (FDBs) of a HDD spindle motor support the rotating disk-spindle system through the pressure generated in the fluid lubricant. The radial and axial clearances of a 2.5″ HDD spindle motor are approximately 2 and 30 micro-meters, respectively, and herringbone or spiral grooves are inscribed in the sleeve of journal or thrust bearings to provide pumping pressure. One of the difficult manufacturing processes is to inscribe uniform grooves, especially groove depth in the range of several micro meters. Grooves are inscribed on the surface of the stainless steel sleeve by the electro chemical machining (ECM) which generally generates rough surface of the sleeve in grooved bearing. Ball-sizing process is used to scrape down rough surface. When a ball passes through the sleeve of FDBs to make rough surface smooth, compressive pressure is generated between ball and sleeve inlet and between ball and sleeve outlet, respectively. It forms an hourglass-shape tapered sleeve as shown in Figure 1, and tapered sleeve generally decreases the static and dynamic performance of the FDBs and the HDD spindle system, consequently.

Design Uncertainty Effects Quantification Using Monte Carlo Simulation

2012 ◽  
Author(s):  
Hesham Kamel
Keyword(s):  
Monte Carlo Simulation ◽  
Finite Element ◽  
Monte Carlo ◽  
Rigid Wall ◽  
Design Variables ◽  
Element Simulation ◽  
Thin Walled Tube ◽  
Final Deformation ◽  
Thin Walled ◽  
Dynamic Impact Loading

This paper presents an approach to evaluate the effect of uncontrolled and un-avoided variation within design variables on the performance of nonlinear finite element models. The approach employs Monte Carlo simulation to reveal this effect using descriptive statistics to present useful information to the designer. A case study of a thin walled tube under dynamic impact loading is used to demonstrate the proposed approach. The thin walled tube is modeled using LS-DYNA for finite element simulation. Wall thickness distributions are selected as design variables where the amount of impact energy absorbed, maximum rigid wall force and final deformation are selected as the important responses. The results clearly show that the proposed approach can provide the designer with useful information of the effect of variation within the design variables on the structure responses. Ultimately, the designer can use this helpful information in creating a design that is minimally sensitive to those uncontrolled and un-avoided variation within design variables.

Reliability Analysis of a Robot Manipulator Operation Employing Single Monte-Carlo Simulation

2006 ◽  
Vol 321-323 ◽  
pp. 1568-1571 ◽  
Author(s):  
Dong Hwan Choi ◽  
Hong Hee Yoo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Robot Manipulator ◽  
Computing Time ◽  
Manufacturing Cost ◽  
Robot System ◽  
Performance Requirement ◽  
Manufacturing Tolerance ◽  
Design Engineers

The operation error of a robot that occurs inevitably due to the manufacturing tolerance needs to be controlled within a certain range to achieve proper performance of the robot system. The reduction of manufacturing tolerance, however, increases the manufacturing cost in return. Therefore, design engineers try to solve the problem of maximizing the tolerance to reduce the manufacturing cost while minimizing the operation error to satisfy the performance requirement. In the present study, a revolute joint model considering uncertainties due to joint clearance is employed to perform a reliability analysis of the robot manipulator operation. The reliability analysis procedure employs single Monte-Carlo simulation and a statistical relation between the tolerance and the operation error. Significant reduction of computing time can be achieved with the proposed method. The present method is especially effective if sensitivity information is hard to be obtained for the analysis.

Manufacturing Tolerance Analysis of a Robot Manipulator’s Joint Axis Orientation Employing Single Monte-Carlo Simulation

2006 ◽  
Author(s):  
Dong Hwan Choi ◽  
Hong Hee Yoo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Robot Manipulator ◽  
Computing Time ◽  
Tolerance Analysis ◽  
Manufacturing Cost ◽  
Axis Orientation ◽  
Manufacturing Tolerance ◽  
Design Engineers ◽  
Joint Axis

The operation error of a robot manipulator that occurs inevitably due to the manufacturing tolerance needs to be controlled within a certain range to achieve proper performance. The reduction of manufacturing tolerance, however, increases the manufacturing cost in return. Therefore, system design engineers try to solve the problem of maximizing the tolerance to reduce the manufacturing cost while minimizing the operation error to satisfy the performance requirement. In the present study, a revolute joint model considering the variation of joint axis orientation due to joint clearance is employed to perform a tolerance analysis of the robot manipulator operation. This paper presents a hybrid method which employs the sensitivity-based analytic method and the single Monte-Carlo simulation. The proposed method provides rapid implementation and the accurate statistical properties using the only single integration or single iteration for one sample set, whereas the Monte-Carlo method necessitates integrations as the number of samples and cases. Significant reduction of computing time can be achieved with the proposed method. The present method is especially effective if sensitivity information is hard to be obtained for the analysis.

Monte Carlo Simulation of Gas Spring in an Energy Storing Prosthetic Knee

2014 ◽  
Vol 660 ◽  
pp. 916-920
Author(s):  
Cucuk Nur Rosyidi ◽  
Rahmaniyah Dwi Astuti ◽  
Ilham Priadythama
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Quality Improvement ◽  
Significant Contribution ◽  
Prosthetic Knee ◽  
Cylinder Length ◽  
Cylinder Diameter ◽  
Design Variables ◽  
Important Design ◽  
Monte Carlo Simulation Model

Gas Spring is an important component of an energy storing prosthetic knee. The spring stored energy during flexion and released the energy while in the extension. In this research, we discuss a Monte Carlo simulation model of a gas spring in an Energy Storing Prosthetic Knee (ESPK) using Oracle Crystal Ball software. The simulation is used to predict the effects of three important design variables of a gas spring which are cylinder diameter, cylinder length, and displacement to the energy storing performance of the spring. The results of simulation show that there are two design variables which have significant contribution to the variations of energy storing performance: cylinder diameter and displacement. Those design variables account for 99.3% to the total variance of energy storing. Quality improvement must be conducted to lowering the resulted energy storing variance. We proportionally decrease the variance of the design variables to lowering the energy storing variance. The simulation results show a significant quality improvement of about 50% in term of energy storing standard deviation. The results also show that cylinder diameter is more sensitive than the other two design variables in energy storing quality improvement.

Design Under Uncertainty Using Monte Carlo Simulation and Probabilistic Sufficiency Factor

2003 ◽  
Author(s):  
Xueyong Qu ◽  
Raphael T. Haftka
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Failure Modes ◽  
Computational Cost ◽  
Probability Of Failure ◽  
Design Under Uncertainty ◽  
Random Errors ◽  
Safety Index ◽  
Design Variables ◽  
Variable Fidelity

Monte Carlo simulation is commonly employed to evaluate system probability of failure for problems with multiple failure modes in design under uncertainty. The probability calculated from Monte Carlo simulation has random errors due to limited sample size, which create numerical noise in the dependence of the probability on design variables. This in turn may lead the design to spurious optimum. A probabilistic sufficiency factor (PSF) approach is proposed that combines safety factor and probability of failure. The PSF represents a factor of safety relative to a target probability of failure, and it can be calculated from the results of Monte Carlo simulation (MCS) with little extra computation. The paper presents the use of PSF with a design response surface (DRS), which fits it as function of design variables, filtering out the noise in the results of MCS. It is shown that the DRS for the PSF is more accurate than DRS for probability of failure or for safety index. The PSF also provides more information than probability of failure or safety index for the optimization procedure in regions of low probability of failure. Therefore, the convergence of reliability-based optimization is accelerated. The PSF gives a measure of safety that can be used more readily than probability of failure or safety index by designers to estimate the required weight increase to reach a target safety level. To reduce the computational cost of reliability-based design optimization, a variable-fidelity technique and deterministic optimization were combined with probabilistic sufficiency factor approach. Example problems were studied here to demonstrate the methodology.

scholarly journals Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft

2020 ◽  
Vol 10 (21) ◽  
pp. 7593
Author(s):  
Ju-Yeol Yun ◽  
Ho-Yon Hwang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Equations ◽  
Design Objective ◽  
Design Variables ◽  
Objective Parameter ◽  
Identification Evaluation ◽  
Design Requirements ◽  
Technology Identification ◽  
Independent Design

In this paper, sensitivity analysis and optimization of a high altitude long endurance (HALE) solar aircraft was implemented. Zephyr S was referred to for the aircraft conference configuration, and OpenVSP and XFLR5 were employed to create configuration and perform aerodynamic analysis. In the conceptual design stage of the HALE solar aircraft, technology identification, evaluation, and selection (TIES) methodology was employed. According to the design requirements, problem definition was established, and design goal, variations, and targeted values were set up to implement independent design variables to meet the design requirements. Based on the design of experiments (DOE), modeling of the relationship between design objective parameters and independent design values was implemented. The independent design variables with the largest influence were selected in the screening test. By employing the selected independent design variables, regression equations and sensitivity profiles were produced through response surface method. Inter-factor relationship was easily analyzed through the sensitivity profile. Regression equations were employed in the Monte Carlo simulation to draw design objective parameter values for 10,000 combinations of independent design variables. As a result of the Monte Carlo simulation, the design feasibility of design objective parameters was assessed. Optimization was performed using the desirability function of JMP software, and constraints were applied to each design objective parameter to derive the optimum values of independent design variables. Then, the values of optimized design independent variables were applied to the solar aircraft design framework and analyzed for the endurance flight performance. By comparing the endurance of the optimized configuration with the reference configuration, it was confirmed that the endurance could be improved by using the methodology proposed in this study.

A New Deterministic Approach in Probabilistic Design Optimization

2009 ◽  
Author(s):  
Hami Golbayani ◽  
Kazem Kazerounian
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simple Zero ◽  
Linear Mapping ◽  
Probabilistic Design ◽  
Engineering Systems ◽  
Deterministic Approach ◽  
Design Variables ◽  
Design Values ◽  
Zero Degree

In this paper, a simple and powerful formulation is presented for probabilistic design of engineering systems. The challenging task of optimum allocation of errors to design variables is transformed into a simple zero degree of difficulty geometric programming problem. This method is based on a known state of the design (the design values as well as the linear mapping between the input and output of the system). Uncertainties of design variables are assumed to be independent, and normally distributed. Failure is defined as a constraint in the optimization process, and has the form of the probability of divergence of outputs from their allowable bounds. Then, this constraint is simplified into a deterministic bound within six sigma spread. Having a zero DOD problem, the optimal solutions are readily available for any system regardless of the complexity. Several numerical experiments are conducted to assess the efficiency of the proposed formulation. The results are compared with more exhaustive searches using Monte Carlo simulation. For higher order and complex systems, it is demonstrated that this formulation will be %20 more conservative than the exact Monte Carlo simulation.

Sign in / Sign up

Export Citation Format

Share Document