Design Improvement by Sensitivity Analysis Under Interval Uncertainty Using Multi-Objective Optimization

2010 ◽  
Vol 132 (8) ◽  
Author(s):  
J. Hamel ◽  
M. Li ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Degrees Of Freedom ◽  
Interval Uncertainty ◽  
Engineering System ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Design Improvement ◽  
Multi Objective ◽  
Original Analysis ◽  
Input Parameters

Uncertainty in the input parameters to an engineering system may not only degrade the system’s performance but may also cause failure or infeasibility. This paper presents a new sensitivity analysis based approach called design improvement by sensitivity analysis (DISA). DISA analyzes the interval uncertainty of input parameters and using multi-objective optimization, determines an optimal combination of design improvements that will ensure a minimal variation in the objective functions of the system, while also ensuring the feasibility. The approach provides a designer with options for both uncertainty reduction and, more importantly, slight design adjustments. A two-stage sequential framework is used that can employ either the original analysis functions or their metamodels to greatly increase the computational efficiency of the approach. This new approach has been applied to two engineering examples of varying difficulty to demonstrate its applicability and effectiveness. The results produced by these examples show the ability of the approach to ensure the feasibility of a preexisting design under interval uncertainty by effectively adjusting available degrees of freedom in the system without the need to completely redesign the system.

Design Improvement by Sensitivity Analysis (DISA) Under Interval Uncertainty Using Multi-Objective Optimization

2009 ◽  
Author(s):  
J. Hamel ◽  
M. Li ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Interval Uncertainty ◽  
Minimal Amount ◽  
Engineering System ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Design Improvement ◽  
Multi Objective ◽  
System A

Uncertainty in the input parameters to an engineering system may not only degrade the system’s performance, but may also cause failure or infeasibility. This paper presents a new sensitivity analysis based approach called Design Improvement by Sensitivity Analysis (DISA). DISA analyzes the interval parameter uncertainty of a system and, using multi-objective optimization, determines an optimal combination of design improvements required to enhance performance and ensure feasibility. This is accomplished by providing a designer with options for both uncertainty reduction and, more importantly, slight design adjustments. The approach can provide improvements to a design of interest that will ensure a minimal amount of variation in the objective functions of the system while also ensuring the engineering feasibility of the system. A two stage sequential framework is used in order to effectively employ metamodeling techniques to approximate the analysis function of an engineering system and greatly increase the computational efficiency of the approach. This new approach has been applied to two engineering examples of varying difficulty to demonstrate its applicability and effectiveness.

Interval Uncertainty Reduction and Single-Disciplinary Sensitivity Analysis With Multi-Objective Optimization

2009 ◽  
Author(s):  
M. Li ◽  
N. Williams ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Uncertainty ◽  
Uncertainty Reduction ◽  
Interval Uncertainty ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Limited Budget ◽  
Output Entropy ◽  
Global Uncertainty ◽  
Significant Attention

Sensitivity analysis has received significant attention in engineering design. While sensitivity analysis methods can be global, taking into account all variations, or local, taking into account small variations, they generally identify which uncertain parameters are most important and to what extent their effect might be on design performance. The extant methods do not, in general, tackle the question of which ranges of parameter uncertainty are most important or how to best allocate investments to partial uncertainty reduction in parameters under a limited budget. More specifically, no previous approach has been reported that can handle single-disciplinary multi-output global sensitivity analysis for both a single design and multiple designs under interval uncertainty. Two new global uncertainty metrics, i.e., radius of output sensitivity region and multi-output entropy performance, are presented. With these metrics, a multi-objective optimization model is developed and solved to obtain fractional levels of parameter uncertainty reduction that provide the greatest payoff in system performance for the least amount of “investment”. Two case studies of varying difficulty are presented to demonstrate the applicability of the proposed approach.

Lifting and parallel lifting optimization by using sensitivity and fuzzy set for an earthmoving mechanism

2016 ◽  
Vol 231 (2) ◽  
pp. 192-203 ◽  
Author(s):  
Yongjun Pan ◽  
Liang Hou
Keyword(s):  
Sensitivity Analysis ◽  
Degrees Of Freedom ◽  
Mechanism Analysis ◽  
Multi Objective Optimization ◽  
Six Degrees Of Freedom ◽  
Multi Objective ◽  
Design Variables ◽  
Hybrid Mechanism ◽  
Analysis And Synthesis ◽  
Critical Components

Earthmoving equipment in motor graders, which can be considered to be complex multibody systems (MBSs), are critical components for earthwork, compaction and re-handling. They have not yet received much attention due to their unusual applications and complicated structures. In this paper, a comprehensive study of an earthmoving MBS, from the mechanism identification and sensitivity analysis to the multi-objective optimization, is presented. First, the earthmoving MBS is identified to be a six degrees-of-freedom spatial hybrid mechanism, where a three revolute-revolute-prismatic-spherical (RRPS) and one spherical subchain (so, RRPS-S) spatial parallel mechanism is the key subsystem, through the mechanism analysis and synthesis. An earthmoving virtual prototyping model is built according to the system topology and connectivity. The kinematic simulations are carried out by imposing corresponding driving functions. Afterwards, the sensitivity analysis is introduced to extract several most relevant design variables from the global ones. A multi-objective optimization process is carried out to improve working performance, where fuzzy sets are used to define different objectives. Results show that the optimal earthmoving mechanism provides better lifting and parallel lifting capabilities.

Interval Uncertainty Reduction and Single-Disciplinary Sensitivity Analysis With Multi-Objective Optimization

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
M. Li ◽  
N. Williams ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Uncertainty ◽  
Uncertainty Reduction ◽  
Interval Uncertainty ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Limited Budget ◽  
Output Entropy ◽  
Global Uncertainty ◽  
Significant Attention

Sensitivity analysis has received significant attention in engineering design. While sensitivity analysis methods can be global, taking into account all variations, or local, taking into account small variations, they generally identify which uncertain parameters are most important and to what extent their effect might be on design performance. The extant methods do not, in general, tackle the question of which ranges of parameter uncertainty are most important or how to best allocate Investments to partial uncertainty reduction in parameters under a limited budget. More specifically, no previous approach has been reported that can handle single-disciplinary multi-output global sensitivity analysis for both a single design and multiple designs under interval uncertainty. Two new global uncertainty metrics, i.e., radius of output sensitivity region and multi-output entropy performance, are presented. With these metrics, a multi-objective optimization model is developed and solved to obtain fractional levels of parameter uncertainty reduction that provide the greatest payoff in system performance for the least amount of “Investment.” Two case studies of varying difficulty are presented to demonstrate the applicability of the proposed approach.

scholarly journals A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

2021 ◽  
Vol 11 (10) ◽  
pp. 4575
Author(s):  
Eduardo Fernández ◽  
Nelson Rangel-Valdez ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan
Keyword(s):  
Portfolio Optimization ◽  
Imperfect Information ◽  
Model Parameters ◽  
Final Decision ◽  
Project Portfolio ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Group Member

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone

2017 ◽  
Vol 231 (11) ◽  
pp. 1012-1024 ◽  
Author(s):  
Vahid Tahmasbi ◽  
Majid Ghoreishi ◽  
Mojtaba Zolfaghari
Keyword(s):  
Sensitivity Analysis ◽  
Feed Rate ◽  
Process Temperature ◽  
Drilling Process ◽  
Drilling Speed ◽  
Multi Objective Optimization ◽  
Bone Drilling ◽  
Multi Objective ◽  
Tool Diameter ◽  
First Time

The bone drilling process is very prominent in orthopedic surgeries and in the repair of bone fractures. It is also very common in dentistry and bone sampling operations. Due to the complexity of bone and the sensitivity of the process, bone drilling is one of the most important and sensitive processes in biomedical engineering. Orthopedic surgeries can be improved using robotic systems and mechatronic tools. The most crucial problem during drilling is an unwanted increase in process temperature (higher than 47 °C), which causes thermal osteonecrosis or cell death and local burning of the bone tissue. Moreover, imposing higher forces to the bone may lead to breaking or cracking and consequently cause serious damage. In this study, a mathematical second-order linear regression model as a function of tool drilling speed, feed rate, tool diameter, and their effective interactions is introduced to predict temperature and force during the bone drilling process. This model can determine the maximum speed of surgery that remains within an acceptable temperature range. Moreover, for the first time, using designed experiments, the bone drilling process was modeled, and the drilling speed, feed rate, and tool diameter were optimized. Then, using response surface methodology and applying a multi-objective optimization, drilling force was minimized to sustain an acceptable temperature range without damaging the bone or the surrounding tissue. In addition, for the first time, Sobol statistical sensitivity analysis is used to ascertain the effect of process input parameters on process temperature and force. The results show that among all effective input parameters, tool rotational speed, feed rate, and tool diameter have the highest influence on process temperature and force, respectively. The behavior of each output parameters with variation in each input parameter is further investigated. Finally, a multi-objective optimization has been performed considering all the aforementioned parameters. This optimization yielded a set of data that can considerably improve orthopedic osteosynthesis outcomes.

Sign in / Sign up

Export Citation Format

Share Document