System Level Six Sigma Robust Optimization of a Drive System With PM Transverse Flux Machine

2012 ◽  
Vol 48 (2) ◽  
pp. 923-926 ◽  
Author(s):  
Gang Lei ◽  
Y. G. Guo ◽  
J. G. Zhu ◽  
T. S. Wang ◽  
X. M. Chen ◽  
...  
Keyword(s):  
Robust Optimization ◽  
Six Sigma ◽  
Drive System ◽  
System Level ◽  
Transverse Flux

scholarly journals Robust Optimization in HTS Cable Based on DEPSO and Design for Six Sigma

2008 ◽  
Author(s):  
Shuhong Wang ◽  
Xinying Liu ◽  
Jie Qiu ◽  
Jian Guo Zhu ◽  
Youguang Guo ◽  
...  
Keyword(s):  
Robust Optimization ◽  
Six Sigma ◽  
Design For Six Sigma

Numerical Simulation Methodology for Robust Optimization Using Six Sigma Analysis

2021 ◽  
Author(s):  
Hari Venkata Santosh Bhatta ◽  
Naveenkumar V ◽  
Abdur-Rasik Khajamohideen ◽  
Benoit Couturier ◽  
Rajeshkumar Ganesan
Keyword(s):  
Numerical Simulation ◽  
Robust Optimization ◽  
Six Sigma ◽  
Simulation Methodology

scholarly journals Robust Optimization in HTS Cable Based on Design for Six Sigma

2008 ◽  
Vol 44 (6) ◽  
pp. 978-981 ◽  
Author(s):  
Xinying Liu ◽  
Shuhong Wang ◽  
Jie Qiu ◽  
Jian Guo Zhu ◽  
Youguang Guo ◽  
...  
Keyword(s):  
Robust Optimization ◽  
Six Sigma ◽  
Design For Six Sigma

Research on dynamic characteristics and fatigue robust optimization of integrated vehicle model

2018 ◽  
Vol 232 (14) ◽  
pp. 1982-1999
Author(s):  
Bobo Li ◽  
Huiqun Yuan ◽  
Tianyu Zhao ◽  
Guangding Wang
Keyword(s):  
Experimental Data ◽  
Robust Optimization ◽  
Dynamic Characteristics ◽  
Six Sigma ◽  
Multiple Frequency ◽  
Heavy Duty ◽  
Vehicle Model ◽  
Design For Six Sigma ◽  
Frequency Components ◽  
Simulation Results

This paper investigates the dynamic characteristics and fatigue robust optimization of heavy-duty tractor. First, this paper presents a vehicle model with sub-structure method. Based on the theory of base motion, the structure dynamic characteristics are analyzed. Second, the accuracy of the method is verified by comparing the experimental results with the simulation results. Also, the dynamic response and the transfer function of vehicle are obtained using the above methods. Combined with the experimental data, the methods of random multiple frequency components and multi-axial fatigue life are adopted to analyze the fatigue damage of the heavy-duty tractor under different road conditions. Finally, the Design for Six Sigma is used to optimize the vehicle’s structure. The results show that by using the proposed method, the dynamic characteristics of the vehicle can be analyzed accurately and effectively, robustness of the vehicle can be improved, and mass of the vehicle can be reduced.

Combining Lean and Six Sigma in the context of Systems Engineering design

2015 ◽  
Vol 6 (4) ◽  
pp. 290-312 ◽  
Author(s):  
TR Sreeram ◽  
Asokan Thondiyath
Keyword(s):  
Product Development ◽  
Engineering Design ◽  
Systems Engineering ◽  
Social Action ◽  
Six Sigma ◽  
Systems Design ◽  
System Level ◽  
Future Research ◽  
Content Type ◽  
Future Work

Purpose – The purpose of this paper is to present a combined framework for system design using Six Sigma and Lean concepts. Systems Engineering has evolved independently and there are numerous tools and techniques available to address issues that may arise in the design of systems. In the context of systems design, the application of Six Sigma and Lean concepts results in a flexible and adaptable framework. A combined framework is presented here that allows better visualization of the system-level components and their interactions at parametric level, and it also illuminates gaps that make way for continuous improvement. The Deming’s Plan-Do-Check-Act is the basis of this framework. Three case studies are presented to evaluate the application of this framework in the context of Systems Engineering design. The paper concludes with a summary of advantages of using a combined framework, its limitations and scope for future work. Design/methodology/approach – Six Sigma, Lean and Systems Engineering approaches combined into a framework for collaborative product development. Findings – The present framework is not rigid and does not attempt to force fit any tools or concepts. The framework is generic and allows flexibility through a plug and play type of implementation. This is important, as engineering change needs vary constantly to meet consumer demands. Therefore, it is important to engrain flexibility in the development of a foundational framework for design-encapsulating improvements and innovation. From a sustainability perspective, it is important to develop techniques that drive rationality in the decisions, especially during tradeoffs and conflicts. Research limitations/implications – Scalability of the approach for large systems where complex interactions exist. Besides, the application of negotiation techniques for more than three persons poses a challenge from a mathematical context. Future research should address these in the context of systems design using Six Sigma and Lean techniques. Practical implications – This paper provides a flexible framework for combining the three techniques based on Six Sigma, Lean and Systems Engineering. Social implications – This paper will influence the construction of agent-based systems, particularly the ones using the Habermas’s theory of social action as the basis for product development. Originality/value – This paper has not been published in any other journal or conference.

scholarly journals Six Sigma Quality Approach to Robust Optimization

2015 ◽  
Vol 51 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Song Xiao ◽  
Yinjiang Li ◽  
Mihai Rotaru ◽  
Jan K. Sykulski
Keyword(s):  
Robust Optimization ◽  
Six Sigma ◽  
Quality Approach
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