John Pieprzak

Essentials of Design Robustness in Design for Six Sigma (DFSS) Methodology

Design for Six Sigma (DFSS) is a systematic process and a disciplined problem prevention approach to achieve business excellence. Robust design is the heart of DFSS. To enable the success of robust parameter design, one should start with good design concept. Axiomatic Design, a fundamental set of principles that determine good design practice, can help to facilitate a project team to accelerate the generation of good design concept. Axiomatic Design holds that uncoupled designs are to be preferred over coupled design. Although uncoupled designs are not always possible, application of axiomatic design principles in DFSS presents an approach to help DFSS team focus on functional requirements to achieve design intents and maximize product reliability. As a result of the application of axiomatic design followed by parameter design, the DFSS team achieved design robustness and reliability. A hydraulic lash adjuster case study will be presented.

Robustness and Performance Optimization of Engine Bearing System Using Computer Model and Surrogate Noise

Designing an internal combustion engine involves compromising among multiple performance metrics and targets with multiple control and noise factors. The main challenges are in determining the critical performance metrics, finding the optimal compromise between these metrics, and correctly represent the most important control and noise factors through CAE modeling and optimization. This paper presents a methodology for practical application of robustness and performance optimization using a CAE model. The key element of the methodology is a concept of surrogate noise. With this concept, the multiple noise factors affecting the system performance are represented through a limited number of noise factors for CAE modeling. The other part of the methodology is to substitute complicated and computationally time intensive CAE modeling with a cheap-to-compute Gaussian Kriging model through Optimal Sampling and Design of Experiment. The final part of the methodology is performing multi-criteria robustness and performance optimization as well as performance and robustness confirmation of the optimal design point. The proposed methodology has been applied to a practical problem of designing the IC engine main bearing system. The results of the analysis have provided practical recommendations and directions to drive the main bearing system design. In this paper, the methodology is demonstrated through the presentation of a simplified form of this investigation.© 2004 ASME

Using axiomatic design to improve conceptual design robustness in Design for Six Sigma (DFSS) methodology

Design for Six Sigma (DFSS) is a systematic process and a disciplined problem-prevention approach to achieve business excellence. Robust design is the heart of DFSS. To ensure the success of robust parameter design, one should start with good design concepts. Axiomatic Design, a fundamental set of principles that determine good design practice, can help to facilitate a project team to accelerate the generation of good design concepts. Axiomatic Design holds that uncoupled designs are to be preferred over coupled designs. Although uncoupled designs are not always possible, application of axiomatic design principles in DFSS presents an approach to help the DFSS team focus on functional requirements to achieve design intents and maximise product reliability. As a result of the application of axiomatic design followed by parameter design, the DFSS team achieved design robustness and reliability. A hydraulic lash adjuster case study will be presented.

Six Sigma Disciplines Utilizing Design for Six Sigma Strategy in Automotive Applications

Doing the right things is important for a company to stay in business while developing the right products to satisfy customers and make profits. Design for Six Sigma (DFSS) is a disciplined problem prevention approach and a systematic process to prevent defects in what is important to the customer. This paper builds on the rationale and opportunities presented in the SAE paper of Six Sigma Disciplines in Automotive Applications for improving design robustness. The methodology to increase system robustness through DFSS is presented and demonstrated through the extension of the case study of crankshaft journal lobing design robustness improvements realized from the traditional DMAIC Six Sigma project presented in the SAE paper of Six Sigma Disciplines in Automotive Applications.

Transportation and transformation of air bubbles in aerated oil through an engine lubrication system

The amount of free air in an engine oil can affect the performance of some engine components. Part of the air in an aerated oil can be dissolved into the oil, while some may remain as free air when the oil reaches these components. A methodology of analyzing how much air dissolves into the oil and how much remains as free air in a lubrication system is presented. A V6 gasoline engine is used as an example to calculate the changes of air bubble sizes due to compression and dissolution into the oil. The amount of air dissolved and the amount of free air in the oil when it reaches various locations along the lubrication passageways are estimated. It is concluded in the case studied that small air bubbles will be dissolved entirely before the oil reaches oil galleries in the heads, while most air in large air bubbles will stay as free air.