An Optimization Approach Toward a Robust Design of Six Degrees of Freedom Haptic Devices

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Aftab Ahmad ◽  
Kjell Andersson ◽  
Ulf Sellgren
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Degrees Of Freedom ◽  
Design Parameters ◽  
Manufacturing Cost ◽  
Optimization Approach ◽  
Performance Indices ◽  
New Approach ◽  
Haptic Devices ◽  
Six Degrees Of Freedom

This work presents an optimization approach for the robust design of six degrees of freedom (DOF) haptic devices. Our objective is to find the optimal values for a set of design parameters that maximize the kinematic, dynamic, and kinetostatic performances of a 6-DOF haptic device while minimizing its sensitivity to variations in manufacturing tolerances. Because performance indices differ in magnitude, the formulation of an objective function for multicriteria performance requirements is complex. A new approach based on Monte Carlo simulation (MCS) was used to find the extreme values (minimum and maximum) of the performance indices to enable normalization of these indices. The optimization approach presented here is formulated as a methodology in which a hybrid design-optimization approach, combining genetic algorithm (GA) and MCS, is first used. This new approach can find the numerical values of the design parameters that are both optimal and robust (i.e., less sensitive to variation and thus to uncertainties in the design parameters). In the following step, with design optimization, a set of optimum tolerances is determined that minimizes manufacturing cost and also satisfies the allowed variations in the performance indices. The presented approach can thus enable the designer to evaluate trade-offs between allowed performance variations and tolerances cost.

scholarly journals Efficient Robust Design Optimization of Optical Systems

2019 ◽  
Vol 215 ◽  
pp. 02001
Author(s):  
Stephanie Kunath
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Optical Design ◽  
Simulation Software ◽  
Robust Design Optimization ◽  
Optical Systems ◽  
Optical Simulation ◽  
Optimization Approach ◽  
Trade Off ◽  
Virtual Product Development

To accelerate the virtual product development of using optical simulation software, the Robust Design Optimization approach is very promising. Optical designs can be explored thoroughly by means of sensitivity analysis. This includes the identification of relevant input parameters and the modelling of inputs vs. outputs to understand their dependencies and interactions. Furthermore, the intelligent definition of objective functions for an efficient subsequent optimization is of high importance for multi-objective optimization tasks. To find the best trade-off between two or more merit functions, a Pareto optimization is the best choice. As a result, not only one design, but a front of best designs is obtained and the most appropriate design can be selected by the decision maker. Additionally, the best trade-off between output variation of the robustness (tolerance) and optimization targets can be found to secure the manufacturability of the optical design by several advanced approaches. The benefit of this Robust Design Optimization approach will be demonstrated.

Dynamics Simulation of a Six-DOF Tunnel Segment Erector for Tunnel Boring Machine Based on Virtual Prototype

2012 ◽  
Vol 251 ◽  
pp. 231-234
Author(s):  
Gang Li ◽  
Ya Dong Chen ◽  
Bo Wang ◽  
Wan Shan Wang
Keyword(s):  
Degrees Of Freedom ◽  
Tunnel Boring Machine ◽  
Virtual Prototype ◽  
Dynamics Simulation ◽  
Design Parameters ◽  
Six Degrees Of Freedom ◽  
Tunnel Boring ◽  
Six Dof ◽  
Freedom Movement ◽  
Segment Erector

In this paper, we present the modeling and dynamics simulation of a six-DOF tunnel segment erector for tunnel boring machine (TBM), which is performed in the virtual prototype platform. The 3D virtual assembling model of a tunnel segment erector is built based on Pro/E software according to its design parameters such as structure and size. After the interference inspection, the model is imported into ADAMS through the interface module of Mech/Pro. The model is simplified and optimized reasonably and various constraints are applied under variety working conditions. The results of simulation show that the design has six degrees of freedom movement capacity which meets the design requirements. At the same time the dynamics characteristics of drives and the forces of each part are obtained and they will provide a boundary condition for strength check and basis for the power system design which is important for the further optimal design.

Robust Design Through the Use of a Hybrid Genetic Algorithm

2002 ◽  
Author(s):  
Kurt Hacker ◽  
Kemper Lewis
Keyword(s):  
Robust Design ◽  
Hybrid Genetic Algorithm ◽  
Design Parameters ◽  
Optimization Approach ◽  
Engineering Systems ◽  
Local Optima ◽  
Good Design ◽  
Mean And Variance ◽  
The Mean

In this paper we present a hybrid optimization approach to perform robust design. The motivation for this work is the fact that many realistic engineering systems are mutimodal in nature with multiple local optima, and moreover may have one or more uncertain design parameters. The approach that is presented utilizes both local and global optimization algorithms to find good design points more efficiently than either could alone. The mean and variance of the objective function at a design point is calculated using Monte Carlo simulation and is used to drive the optimization process. To demonstrate the usefulness of this approach a case study is considered involving the design of a beam with dimensional uncertainty.

scholarly journals Structural optimization considering the probabilistic system response

2004 ◽  
Vol 31 (3-4) ◽  
pp. 361-394 ◽  
Author(s):  
M. Papadrakakis ◽  
N.D. Lagaros ◽  
V. Plevris
Keyword(s):  
Structural Optimization ◽  
Design Optimization ◽  
Robust Design ◽  
Large Scale ◽  
Optimization Problem ◽  
Structural Parameters ◽  
Dominant Role ◽  
Robust Design Optimization ◽  
Design Parameters ◽  
System Response

In engineering problems, the randomness and uncertainties are inherent and the scatter of structural parameters from their nominal ideal values is unavoidable. In Reliability Based Design Optimization (RBDO) and Robust Design Optimization (RDO) the uncertainties play a dominant role in the formulation of the structural optimization problem. In an RBDO problem additional non deterministic constraint functions are considered while an RDO formulation leads to designs with a state of robustness, so that their performance is the least sensitive to the variability of the uncertain variables. In the first part of this study a metamodel assisted RBDO methodology is examined for large scale structural systems. In the second part an RDO structural problem is considered. The task of robust design optimization of structures is formulated as a multi-criteria optimization problem, in which the design variables of the optimization problem, together with other design parameters such as the modulus of elasticity and the yield stress are considered as random variables with a mean value equal to their nominal value. .

A Gaussian Process Modeling Approach for Fast Robust Design With Uncertain Inputs

2018 ◽  
Author(s):  
Kevin M. Ryan ◽  
Jesper Kristensen ◽  
You Ling ◽  
Sayan Ghosh ◽  
Isaac Asher ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Design Optimization ◽  
Robust Design ◽  
Optimization Problem ◽  
Robust Design Optimization ◽  
Design Parameters ◽  
Input Uncertainty ◽  
Single Level ◽  
Output Uncertainty ◽  
Gp Model

Many engineering design and industrial manufacturing applications are tasked with finding optimum designs while dealing with uncertainty in the design parameters. The performance or quality of the design may be sensitive to the input variation, making it difficult to optimize. Probabilistic and robust design optimization methods are used in these scenarios to find the designs that will perform best under the presence of known input uncertainty. Robust design optimization algorithms often require a two-level optimization problem (double-loop) to find a solution. The design optimization outer-loop repeatedly calls a series of inner loops that calculate uncertainty measures of the outputs. This nested optimization problem is computationally expensive and can sometimes render the task infeasible for practical engineering robust design problems. This paper details a single-level metamodel-assisted approach for probabilistic and robust design. An enhanced Gaussian Process (GP) metamodel formulation is used to provide exact values of output uncertainty in the presence of uncertain inputs. The GP model utilizes a squared-exponential kernel function and assumes normally distributed input uncertainty. These two factors together allow for an exact calculation of the first and second moments of the marginal predictive distribution. Predictions of output uncertainty are directly calculated, creating an efficient single-level robust optimization problem. We demonstrate the effectiveness of the single-level GP-assisted robust design approach on multiple engineering example problems, including a beam vibration problem, a cantilevered beam with multiple constraints, and a robust autonomous aircraft flight controller design problem. For the optimization problems investigated in this study, the single-level framework found the robust optimum with a 99.9% savings in function evaluations over the standard two-level approach.

A FRAMEWORK FOR CAPTURING DEGRADATION BEHAVIOR IN RELIABILITY-BASED ROBUST DESIGN OPTIMIZATION

2011 ◽  
Vol 18 (06) ◽  
pp. 531-554 ◽  
Author(s):  
OM PRAKASH YADAV ◽  
SUNIL S. BHAMARE ◽  
AJAY PAL SINGH RATHORE
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Degradation Process ◽  
Development Stage ◽  
Robust Design Optimization ◽  
Design Parameters ◽  
Leaf Spring ◽  
Degradation Behavior ◽  
Multi Objective ◽  
Target Values

The increasing customer awareness and global competition have forced manufacturers to capture the entire life cycle issues during product design and development stage. The thorough understanding of product behavior (degradation process) and various uncertainties associated with product performance is paramount to produce reliable and robust design. This paper proposes a multi-objective framework for reliability-based robust design optimization, which captures degradation behavior of quality characteristics to provide optimal design parameters. The objective function of the multi-objective optimization problem is defined as quality loss function considering both desirable and undesirable deviations between target values and the actual results. The degradation behavior is captured by using empirical model to estimate amount of degradation accumulated in time t. The applicability of the proposed methodology is demonstrated by considering a leaf spring design problem.

Efficient Gradient-Based Tolerance Optimization Using Monte Carlo Simulation

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
R. Alan Bowman
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Manufacturing Cost ◽  
Optimization Approach ◽  
Functional Requirements ◽  
Mechanical Assembly ◽  
New Approach ◽  
Tolerance Optimization ◽  
Gradient Based ◽  
Derivatives Of

A gradient-based optimization approach is employed to select design tolerances for the component dimensions of a mechanical assembly to minimize manufacturing cost while achieving a desired probability of meeting functional requirements, known as the yield. Key to the feasibility of such an approach is to be able to use Monte Carlo simulation to make estimates of the derivatives of the yield with respect to the design tolerances quickly and accurately. A new approach for making these estimates is presented and is shown to be far faster and more accurate than previous approaches. Gradient-based optimization using the new approach for estimating the derivatives is applied to example problems from the literature. The solutions are superior to all previously published solutions and are obtained with very reasonable computer run times. Additional advantages of a gradient-based approach are described.

Design Optimization of a Rocker-Joint Silent Chain and Sprocket Drive Based on the Mesh Performance Indices

2012 ◽  
Vol 197 ◽  
pp. 104-109 ◽  
Author(s):  
Wen Yi Su ◽  
Yu Ren Wu
Keyword(s):  
Design Optimization ◽  
Design Procedure ◽  
Optimization Procedure ◽  
Design Parameters ◽  
Chain Drive ◽  
Performance Indices ◽  
Tooth Contact Analysis ◽  
Noise Vibration ◽  
Chain Drives ◽  
Novel Design

Improvement on noise, vibration and wear in silent chain drives is always an important research subject. However, design methods revealed in public are few because the silent chain shapes are variable and complex. A feasible design procedure is extremely required for improving transmission performance of chain drives. Therefore, a novel design optimization procedure for the rocker-joint silent (RJS) chain and sprocket drive is proposed in this paper. The mathematical models of geometry generation, tooth contact analysis and impact velocities at different mesh stages and chain raise amount in the RJS chain drive have been established. Besides, impact velocities and raise amount which may produce ill effects in the chine drive are incorporated as a multi-objective function to carry out the global minimization trying to find out the optimal design parameters for RJS chain drives. The single-objective optimization trends have also been verified with the previous references.

Hybrid (Symbolic-Numerical) Optimization in Mechanism Design for the Elimination of Redundant Constraints

1991 ◽  
Vol 113 (2) ◽  
pp. 110-123 ◽  
Author(s):  
D. A. Hoeltzel ◽  
Wei-Hua Chieng
Keyword(s):  
Design Optimization ◽  
Numerical Optimization ◽  
Design Methodology ◽  
Degrees Of Freedom ◽  
Systematic Approach ◽  
Hybrid Design ◽  
New Approach ◽  
Symbolic Reasoning ◽  
Algorithmic Analysis ◽  
Programming Algorithms

Hybrid optimization, a new approach to design optimization employing both symbolic reasoning and algorithmic analysis, has been applied to the design of kinematic pairs in mechanisms. This hybrid design methodology provides a three-step systematic approach for (1) combining the degrees-of-freedom found in simple, lower kinematic pairs to obtain more complex but robust higher pairs, (2) judging inappropriately assigned joints for the elimination of redundant kinematic constraints and harmful mobilities, and (3) assisting nonexpert designers in applying nonlinear programming algorithms for detailed numerical design optimization of kinematic pairs. An example taken from the design of a spatial mechanism, specifically a universal joint, is presented and serves to demonstrate the utility of this procedure for detailed hybrid design optimization of kinematic pairs in mechanisms.

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