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Introduction

SIX SIGMA METHODLOGY FOR SOLVING AUTOMOTIVE ENGINEERING PROBLEMS ABSTRACT All engineering problems are dependent on multiple design variables and the solutions for these needs to satisfy different responses. Compromises in deciding values for these design variables need to be made so that all the functional requirements are met with in the budgeted cost and time. To arrive at the values for these design variables, there is a need to understand the main and interaction effects of all these input parameters on the response variables and hence, necessitates conducting scores to hundreds of experiments. Performing these numbers of experiments may not be feasible every time given the budget and cost constraints. Methods like DoE based on statistical basis are found useful in such scenario where the effects of all the input parameters are modeled with a minimum number of experiments. Application of mathematical tools to engineering problems poses several challenges in arriving feasible solutions and DoE is no different. This article describes the challenges that are faced in applying DoE to automotive engineering problems. A six-sigma based methodology thus arrived based on how these challenges were overcome with two case studies is presented in this article. Key Words: Design of Experiments, Response Variable, Automotive Seating System 1. Introduction The crucial factors that govern engineering and design of automotive systems are safety and cost. Several specifications, Legal and Customer specified, that guide the design of the systems from package and strength points of view need to be satisfied in designing these systems. Automotive Seating Systems also play a major role in providing safety to the passengers apart from satisfying other important requirements like comfort. ...read more.

Middle

Even though in the present problem, the length of the ground link, L4 is fixed, it has been varied along with the A, the angle of the front link in Design configuration for getting the required travel. Thus, four response variables are defined that are to be calculated from the simulation experimentation viz. Link Length Ratio ?, the ratio of the length of the Ground Link to that of the Intermediate Link, H-X, the H-point travel in X-direction when the mechanism is in High configuration, H-L, the H-point travel in X-direction when the mechanism is in Low configuration and angle A. From simple reasoning it can be deduced that for an efficient design angle A should be approximately equal to 45� and ? should be approximately equal to unity. Based on these conditions, the problem is stated mathematically as given in Table. 1. Figure 3. Travel of H-Point in Design, Low and High Configurations Figure 4. Constrained Wire-frame used in the study Table 1. Variable Definitions for Lift Mechanism Problem Input Variables Front Link Length, L1 Rear Link Length, L3 Inter-mediate Link Length, L2 Range to be included Response Variables H-Point travel along negative X-axis, H-X H-Point travel along positive X-axis, H+X Traverse angle, A Link Length Ration, ? NA Optimization Criteria 1. Maximize H-X 2. Maximize H+X 3. Target A ~ 45� 4. Target ? ?1 NA 3.1.3 Six-Sigma Three six-sigma tools - Design of Experiments, Regression and Optimization have been used in the present problem. The inputs with their possible ranges in levels of 3 has been considered and been fed into the six sigma tool so that a set of full factorial experiments to be conducted are designed. ...read more.

Conclusion

The results that are obtained from the simulation run show a close agreement between the values obtained from the Regression output confirming the validity of the exercise (Refer Table. 6). Table 6. Comparison between the Results obtained by solving Regression equations and from Simulation Run Sl. No. Output Parameter Results obtained by solving Regression Equations Results obtained from Simulation run 1 Percentage Plastic Strain in Front Inboard Foot 28 25 2 Percentage Plastic Strain in Front Outboard Foot 34 35 3 Percentage Plastic Strain in Rear Inboard Foot 23 25 4 Deflection of Seat back in X 405.3 410 5 Deflection of Seat back in Z - 341.5 -360 4. Discussion and Conclusion It has been evident from the two case studies that Six- Sigma tools have a definite applicability in the automotive engineering problems when proper transformations in converting the practical problem into a mathematical one for optimized solution. Also, the importance of these tools can be found in hitting the targets more precisely with in the ambit of the posed constraints. As there is no definite possibility in case of the problem described in the first case study, that one will hit the target with trial and error methods, application of such tools becomes inevitable. Moreover, tools like Design of Experiments help in achieving higher productivity, which is an important requirement for present day's corporations to maintain the competitive edge. 5. Acknowledgements The authors gratefully acknowledge the review efforts spent by Dr. M.S.S. Prabhu, Mr. Padmanabhan Venkataraman, Mr. M.R. Ravishankar, Mr. Hursh Kumar Donde and Mr. Ramanath, K.S. in finalizing the methodology proposed in the present article and in validating the case studies. ...read more.

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