Response surface estimation and sensitivity analysis via efficient change of measure

Kinematic accuracy is a crucial indicator for evaluating the performance of mechanisms. Low-mobility parallel mechanisms are examples of parallel robots that have been successfully employed in many industrial fields. Previous studies analyzing the kinematic accuracy analysis of parallel mechanisms typically ignore the randomness of each component of input error, leading to imprecise conclusions. In this paper, we use homogeneous transforms to develop the inverse kinematics models of an improved Delta parallel mechanism. Based on the inverse kinematics and the first-order Taylor approximation, a model is presented considering errors from the kinematic parameters describing the mechanism’s geometry, clearance errors associated with revolute joints and driving errors associated with actuators. The response surface method is employed to build an explicit limit state function for describing position errors of the end-effector in the combined direction. As a result, a mathematical model of kinematic reliability of the improved Delta mechanism is derived considering the randomness of every input error component. And then, reliability sensitivity of the improved Delta parallel mechanism is analyzed, and the influences of the randomness of each input error component on the kinematic reliability of the mechanism are quantitatively calculated. The kinematic reliability and proposed sensitivity analysis provide a theoretical reference for the synthesis and optimum design of parallel mechanisms for kinematic accuracy.

Download Full-text

Numerical investigation and sensitivity analysis on bioconvective tangent hyperbolic nanofluid flow towards stretching surface by response surface methodology

Alexandria Engineering Journal ◽

10.1016/j.aej.2020.08.007 ◽

2020 ◽

Vol 59 (6) ◽

pp. 4533-4548 ◽

Cited By ~ 1

Author(s):

Anum Shafiq ◽

Tabassum Naz Sindhu ◽

Chaudry Masood Khalique

Keyword(s):

Sensitivity Analysis ◽

Response Surface Methodology ◽

Response Surface ◽

Numerical Investigation ◽

Stretching Surface ◽

Nanofluid Flow

Download Full-text

Predictive modelling to support sensitivity analysis for robust design in aerospace engineering

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-019-02467-5 ◽

2020 ◽

Vol 61 (5) ◽

pp. 2177-2192 ◽

Cited By ~ 3

Author(s):

Siva Krishna Dasari ◽

Abbas Cheddad ◽

Petter Andersson

Keyword(s):

Sensitivity Analysis ◽

Response Surface ◽

Robust Design ◽

Aerospace Engineering ◽

Multivariate Adaptive Regression Splines ◽

Linear Functions ◽

Computational Time ◽

Surface Modelling ◽

Non Linear ◽

Response Surface Modelling

AbstractThe design of aircraft engines involves computationally expensive engineering simulations. One way to solve this problem is the use of response surface models to approximate the high-fidelity time-consuming simulations while reducing computational time. For a robust design, sensitivity analysis based on these models allows for the efficient study of uncertain variables’ effect on system performance. The aim of this study is to support sensitivity analysis for a robust design in aerospace engineering. For this, an approach is presented in which random forests (RF) and multivariate adaptive regression splines (MARS) are explored to handle linear and non-linear response types for response surface modelling. Quantitative experiments are conducted to evaluate the predictive performance of these methods with Turbine Rear Structure (a component of aircraft) case study datasets for response surface modelling. Furthermore, to test these models’ applicability to perform sensitivity analysis, experiments are conducted using mathematical test problems (linear and non-linear functions) and their results are presented. From the experimental investigations, it appears that RF fits better on non-linear functions compared with MARS, whereas MARS fits well on linear functions.

Download Full-text