Quantitative Exploratory Techniques to Simplify Multiple Objectives in Product Design Optimization

2003 ◽  
Author(s):  
Liem Ferryanto ◽  
Agus Sudjianto ◽  
Mahesh Vora
Keyword(s):  
Sensitivity Analysis ◽  
Factor Analysis ◽  
Design Optimization ◽  
Product Design ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
Road Noise ◽  
Multi Objective ◽  
Noise Vibration ◽  
Total Sensitivity

The objective of this paper is to develop techniques to sub-group multiple responses of a system. The proposal is intended for explorative preprocessing steps prior to the execution of a more formal multi-objective optimization. The sub-grouping techniques are developed based on orthonormal expansions. Factor Analysis (FA) and Total Sensitivity Analysis (TSA) are suggested when the relationships among responses are linear and non-linear, respectively. Automotive road Noise Vibration and Harshness (NVH) data is used to illustrate the application of the proposed methodologies.

Simultaneous Requirement and Design Optimization of an Industrial Robot Family Using Multi-Objective Optimization

2011 ◽  
Author(s):  
Daniel Wa¨ppling ◽  
Xiaolong Feng ◽  
Hans Andersson ◽  
Marcus Pettersson ◽  
Bjo¨rn Lunden ◽  
...  
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Industrial Robot ◽  
Simultaneous Optimization ◽  
Engineering Practice ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Research Experience ◽  
Requirement Specification ◽  
Multi Objective

Simultaneous development of an industrial robot family, consisting typically of 2–10 robots, has been an engineering practice in robotics industry. In this process, significant scenario studies on defining product requirement specifications and associated design change are conducted. This implies that understanding the relation between product requirements and design of the robot family is of critical importance. However, in the current engineering practice, any change in requirement specification results in tremendous efforts in the re-design of the robot family. This discloses the need for efficient methodology and tools for simultaneously optimizing product requirements and design of an industrial robot family. In this work, methodology and tools have been successfully developed for simultaneously optimizing product requirements and design of an industrial robot family in a fully automated way. This problem is formulated to a multi-objective optimization problem and solved using multi-objective genetic algorithm (MOGA). Results of this work have demonstrated clearly the efficiency of this approach and the insight obtained on the relation between product requirement and product design. The developed methodology and results of simultaneous requirement specification and design optimization will be detailed in this paper. In addition, research experience and future work will also be discussed. To our best knowledge, the simultaneous optimization of product requirement and product design has not been widely investigated and explored in academia. The trade-off information explored by such approach is crucial in product development in industrial practice. Such approach will further increase the complexity of traditional design optimization approach where product requirement is normally pre-defined and used as constraint. It is certain that discussions of the addressed problem and developed methodology will contribute to promoting the significance of efforts in the research society of multi-objective design optimization, multi-objective design optimization of product families, and design automation.

Integrated Design and Multi-Objective Optimization of a Single Stage Heat-Pump Turbocompressor

2013 ◽  
Author(s):  
J. Schiffmann
Keyword(s):  
Design Optimization ◽  
High Efficiency ◽  
Integrated Design ◽  
Heat Pumps ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Design Constraints ◽  
Multi Objective ◽  
Wide Range ◽  
Standard Design

Small scale turbomachines in domestic heat pumps reach high efficiency and provide oil-free solutions which improve heat-exchanger performance and offer major advantages in the design of advanced thermodynamic cycles. An appropriate turbocompressor for domestic air based heat pumps requires the ability to operate on a wide range of inlet pressure, pressure ratios and mass flows, confronting the designer with the necessity to compromise between range and efficiency. Further the design of small-scale direct driven turbomachines is a complex and interdisciplinary task. Textbook design procedures propose to split such systems into subcomponents and to design and optimize each element individually. This common procedure, however, tends to neglect the interactions between the different components leading to suboptimal solutions. The authors propose an approach based on the integrated philosophy for designing and optimizing gas bearing supported, direct driven turbocompressors for applications with challenging requirements with regards to operation range and efficiency. Using previously validated reduced order models for the different components an integrated model of the compressor is implemented and the optimum system found via multi-objective optimization. It is shown that compared to standard design procedure the integrated approach yields an increase of the seasonal compressor efficiency of more than 12 points. Further a design optimization based sensitivity analysis allows to investigate the influence of design constraints determined prior to optimization such as impeller surface roughness, rotor material and impeller force. A relaxation of these constrains yields additional room for improvement. Reduced impeller force improves efficiency due to a smaller thrust bearing mainly, whereas a lighter rotor material improves rotordynamic performance. A hydraulically smoother impeller surface improves the overall efficiency considerably by reducing aerodynamic losses. A combination of the relaxation of the 3 design constraints yields an additional improvement of 6 points compared to the original optimization process. The integrated design and optimization procedure implemented in the case of a complex design problem thus clearly shows its advantages compared to traditional design methods by allowing a truly exhaustive search for optimum solutions throughout the complete design space. It can be used for both design optimization and for design analysis.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

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.

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