Robust Design Chapter Six. Training Manual January 30, 2001 Inventory #001449 6-2 6. Robust Design In this section, we will present a practical application.

Slides:



Advertisements
Similar presentations
Prof. Steven D.Eppinger MIT Sloan School of Management.
Advertisements

Robust Design – The Taguchi Philosophy
The Basics of Experimentation I: Variables and Control
Experimental Design Fr. Clinic II. Planning Begins with carefully considering what the objectives (or goals)are –How do our filters work? –Which filter.
World Health Organization
Experimental Design Fr. Clinic II Dr. J. W. Everett.
Stat Today: Start Chapter 10. Additional Homework Question.
Chapter 7. Random Process – Spectral Characteristics
8/12/2015 Advanced Manufacturing Choices MAE Spring 2013, Dr. Marc Madou Class 1.
Chapter 5 Vibration Analysis
Chapter Five Vibration Analysis.
Company Confidential 1 Copyright 2009 Bell Helicopter Textron Inc. All rights reserved Voice of the Process Panel Discussion Lee Tait, Vice President Quality.
Introduction to Robust Design and Use of the Taguchi Method.
8/25/2015ENGM 720: Statistical Process Control1 ENGM Lecture 02 Introduction to Statistical Process Control.
Training Presentation on Design Control Ryan McLaughlin OISM 470W.
2.810T.Gutowski Quality and Variation Part Tolerance Process Variation Taguchi “Quality Loss Function” Random Variables and how variation grows.
Introduction to Design of Experiments
©2003 Prentice Hall, Inc.To accompany A Framework for Marketing Management, 2 nd Edition Slide 0 in Chapter 11 Chapter 11 Setting Product and Brand Strategy.
Chapter 9 CAD & Parameters
Robust Design and Reliability-Based Design ME 4761 Engineering Design 2015 Spring Xiaoping Du.
Fall 2003 – Session 6Problem Solving, Design, and System Improvement Process Capability  C p = (design tolerance width)/(process width) = (max-spec –
Design of Engineering Experiments Part 4 – Introduction to Factorials
1 Design and Analysis of Engineering Experiments Chapter 1: Introduction.
DOX 6E Montgomery1 Design of Engineering Experiments Part 4 – Introduction to Factorials Text reference, Chapter 5 General principles of factorial experiments.
PRESENTED BY: Navjot Sandhu The Innovator’s Toolkit- Techniques and tools for optimizing and finalizing designs.
Multivariate Analysis. One-way ANOVA Tests the difference in the means of 2 or more nominal groups Tests the difference in the means of 2 or more nominal.
ROBUST DESIGN.
Chapter 5 – Part 1 Solutions to SHW. 1.What do we mean by a process that is consistently on target? Amount of Toner LSL Target USL Process is consistently.
Chapter 16: Product Design and Manufacturing
9.0 New Features Min. Life for a Titanium Turbine Blade Workshop 9 Robust Design – DesignXplorer.
Digital Intuition Cluster, Smart Geometry 2013, Stylianos Dritsas, Mirco Becker, David Kosdruy, Juan Subercaseaux Welcome Notes Overview 1. Perspective.
CONTROL ENGINEERING IN DRYING TECHNOLOGY FROM 1979 TO 2005: REVIEW AND TRENDS by: Pascal DUFOUR IDS’06, Budapest, 21-23/08/2006.
DOX 6E Montgomery1 Design of Engineering Experiments Part 8 – Overview of Response Surface Methods Text reference, Chapter 11, Sections 11-1 through 11-4.
Chapter 3: Maximum-Likelihood Parameter Estimation l Introduction l Maximum-Likelihood Estimation l Multivariate Case: unknown , known  l Univariate.
What is a DOE? Design of Experiment Adapted from experiments.cfm#preparation.
Chapter Six: The Basics of Experimentation I: Variables and Control.
Chapter 3 Response Charts.
Review of fundamental 1 Data mining in 1D: curve fitting by LLS Approximation-generalization tradeoff First homework assignment.
Probabilistic Design Systems (PDS) Chapter Seven.
7-1 ANSYS, Inc. Proprietary © 2009 ANSYS, Inc. All rights reserved. February 23, 2009 Inventory # Workbench - Mechanical Introduction 12.0 Chapter.
Written by Changhyun, SON Chapter 5. Introduction to Design Optimization - 1 PART II Design Optimization.
Why/When is Taguchi Method Appropriate? Friday, 11 th May 2001 Tip #4 design stage Robust design improves “QUALITY ” at all the life stages at the design.
Exploring the Design Domain Chapter Four. Training Manual January 30, 2001 Inventory # Exploring the Design Domain A. Overview Exploring the.
Introduction Chapter 1. Training Manual March 15, 2001 Inventory # Prerequisites Prerequisites for the Heat Transfer Seminar include: –Successful.
Introduction Chapter One. Training Manual January 30, 2001 Inventory # Introduction Course Overview This Design Optimization course is designed.
Contact Stiffness Chapter Three. Training Manual October 15, 2001 Inventory # Contact Stiffness A. Basic Concepts Review: Recall that all ANSYS.
X. EXPERIMENTAL DESIGN FOR QUALITY
Magnetic Actuator Workshop 6 Robust Design. Workshop Supplement January 30, 2001 Inventory # W Robust Design Magnetic Actuator Description.
Mode Superposition Module 7. Training Manual January 30, 2001 Inventory # Module 7 Mode Superposition A. Define mode superposition. B. Learn.
Aron, Aron, & Coups, Statistics for the Behavioral and Social Sciences: A Brief Course (3e), © 2005 Prentice Hall Chapter 10 Introduction to the Analysis.
Heat Transfer Su Yongkang School of Mechanical Engineering # 1 HEAT TRANSFER CHAPTER 6 Introduction to convection.
TAGUCHI PHILOSOPHY-- CONCEPT OF PARAMETER DESIGN &ROBUST DESIGN THREE PRODUCT DEVELOPMENT STAGES:- PRODUCT DESIGN PROCESS DESIGN PRODUCTION SOURCES OF.
Chapter 6 Sampling and Sampling Distributions
Appendix A 12.0 Workbench Environment
2015 JMP Discovery Summit, San Diego
TOTAL QUALITY MANAGEMENT
Six-Sigma : DMAIC Cycle & Application
Design Control What Will Be Covered
36.1 Introduction Objective of Quality Engineering:
Design Control What Will Be Covered
Why/When is Taguchi Method Appropriate?
Why/When is Taguchi Method Appropriate?
Why/When is Taguchi Method Appropriate?
Chapter 10 Introduction to the Analysis of Variance
Why/When is Taguchi Method Appropriate?
Why/When is Taguchi Method Appropriate?
Why/When is Taguchi Method Appropriate?
Presentation transcript:

Robust Design Chapter Six

Training Manual January 30, 2001 Inventory # Robust Design In this section, we will present a practical application of optimization tools - using Robust Design concepts for mass production. The following topics will be covered: A.Define robust design B.Using optimization tools to achieve a robust design C.A workshop demo and/or exercise

Training Manual January 30, 2001 Inventory # Robust Design A. Definition What is Robust Design? A design that is not sensitive to variations in noise parameters such as: –manufacturing tolerances –material properties –environmental conditions - temperature, humidity, etc. I.e, a design that is robust!

Training Manual January 30, 2001 Inventory # * Quality Engineering Using Robust Design, Madhav S. Phadke, Prentice Hall, 1989 Robust Design...Definition Robust design concepts are especially useful for mass production, where minimizing the variation in the product characteristics is just as important as obtaining the optimum design. In a study*, SONY television sets were manufactured in U.S. and Japan to identical designs and tolerances. –The U.S. factory used the fraction-defective criterion. –The Japanese factory used the Robust Design concept. –Japan produced more sets near target color density and were preferred by U.S. consumers.

Training Manual January 30, 2001 Inventory # Robust Design B. How to Implement ANSYS optimization tools - Gradient, Factorial, and Sweep - can be used effectively to implement robust design concepts. One way is as follows: 1.Identify noise parameters and control parameters. 2.Create a parametric model using noise and control parameters. 3.Determine which noise parameters have the most effect on the design. 4.Determine which control parameters can be changed to reduce the effect of the noise parameters.

Training Manual January 30, 2001 Inventory # Robust Design...How to Implement 1. Identify Noise and Control Parameters Noise Parameters are factors which can vary randomly and are beyond the control of the designer, e.g: –Material properties –Manufacturing tolerances –Environment, e.g, temperature and humidity –Component degradation with time Control Parameters are factors that the designer can change to decrease the effect of the noise parameters.

Training Manual January 30, 2001 Inventory # Robust Design...How to Implement 2. Create Parametric Model Use both noise parameters and control parameters to build the parametric model. Specify noise parameters and control parameters as DVs. Remember that DVs can only take on positive values. For example, if a dimension thk = 2.5 has a manufacturing tolerance of thktol =  0.001, you would build the model using thk+thktol as the thickness dimension: thk = tol = 0.002

Training Manual January 30, 2001 Inventory # Robust Design...How to Implement 3. Determine Noise Parameters That Have the Most Effect The obvious tool for this is the Gradient Tool, which shows how a given design (the reference design) would respond to a 1% change in each DV. The reference design in this case would be the current, accepted design. The curve with the steepest slope indicates the noise parameter that has the most effect on the design.

Training Manual January 30, 2001 Inventory # Robust Design...How to Implement At this point, one option is to somehow reduce the variability of the critical noise parameters. For example, if a tolerance is deemed critical, you might be able to tighten the manufacturing tolerance. However, most noise parameters, by definition, are beyond the designer’s control. The other option is to move to step 4 to investigate whether the effect of these noise parameters can be reduced by changing the control parameters.

Training Manual January 30, 2001 Inventory # Robust Design...How to Implement 4. Determine Which Control Parameters Can Be Changed The Factorial Tool is the obvious choice for this. It can be used to determine two-way and three-way interactions between noise parameters and control parameters. For example, what value of THK will make THKTOL least effective? Another way is to use the Sweep Tool to sweep through several control parameter values for a given noise parameter value.

Training Manual January 30, 2001 Inventory # Robust Design...How to Implement If you determine that a different value of a control parameter, say THK, does indeed reduce the effectiveness of a noise parameter, you will need to make sure that the new design still performs to desired standards. This is just a brief introduction to basic Robust Design concepts and how ANSYS optimization tools can be used to implement them. For more information, please refer to the Appendix in your Workshop Supplement entitled: World Class Quality through Robust Design.

Training Manual January 30, 2001 Inventory # See your Design Optimization Workshop Supplement for details. Robust Design Workshop In this workshop, we will do a factorial analysis of a magnetic actuator.