New approaches to Materials Education - a course authored by Mike Ashby and David Cebon, Cambridge, UK, 2007 © MFA and DC 2007 UNIT 6. Objectives in conflict:

Slides:



Advertisements
Similar presentations
Page Materials Selection Lecture #11 Materials Selection Software Tuesday October 4 th, 2005.
Advertisements

CHEN 4460 – Process Synthesis, Simulation and Optimization
Copyright © 2014, 2011 Pearson Education, Inc. 1 Chapter 20 Curved Patterns.
IFB 2012 Materials Selection in Mechanical Design
Melih Papila, Piezoresistive microphone design Pareto optimization: Tradeoff between sensitivity and noise floor Melih PapilaMark Sheplak.
Fundamentals for the Up-and-Coming Bridge Engineer Forces on Beams and Material Properties OSU College of Engineering Summer Institute - Robotics.
Advanced Methods in Materials Selection
IFB 2012 INTRODUCTION Material Indices1/12 IFB 2012 Materials Selection in Mechanical Design INTRODUCTION Materials Selection Without Shape (1/2) Textbook.
2003 International Congress of Refrigeration, Washington, D.C., August 17-22, 2003 Application of Multi-objective Optimization in Food Refrigeration Processes.
Lecture 9: Optimization with a Min objective AGEC 352 Spring 2011 – February 16 R. Keeney.
Spring, 2013C.-S. Shieh, EC, KUAS, Taiwan1 Heuristic Optimization Methods Pareto Multiobjective Optimization Patrick N. Ngatchou, Anahita Zarei, Warren.
Mutli-Attribute Decision Making Scott Matthews Courses: /
In this chapter, look for the answers to these questions:
Materials Selection Without Shape...when function is independent of shape...
1 Civil Systems Planning Benefit/Cost Analysis Scott Matthews Courses: / / Lecture /5/2005.
Design Optimization School of Engineering University of Bradford 1 Formulation of a multi-objective problem Pareto optimum set consists of the designs.
Tier I: Mathematical Methods of Optimization
Combining materials for composite-material cars Ford initiated research at a time when they took a look at making cars from composite materials. Graphite-epoxy.
(Problems with) Optimising Brake Disc Design by Simulation (Problems with) Optimising Brake Disc Design by Simulation Bill Young Senior Consultant, Design.
Q = F(K, L | given Tech) Or Output = F(Inputs | Chosen Tech)
Selection Strategies. Making selections Let’s attempt to remove the emotional and intangibles from this discussion: It’s cool Johnny has one I love the.
In this chapter, look for the answers to these questions:
The Theory of Consumer Choice
New approaches to Materials Education - a course authored by Mike Ashby and David Cebon, Cambridge, UK, 2007 © MFA and DC 2007 UNIT 7. The economics: cost.
Benjamin Welle Stanford University Grant Soremekun Phoenix Integration
Chapter 12 Discrete Optimization Methods
100% of B-TOS architectures have cost increase under restrictive launch policy for a minimum cost decision maker Space Systems, Policy, and Architecture.
Ken YoussefiMechanical Engineering Dept. 1 Design Optimization Optimization is a component of design process The design of systems can be formulated as.
Higher Higher Unit 1 What is Integration The Process of Integration Area between to curves Application 1.4.
Mechanics Unit 5: Motion and Forces 5.6 Motion in one Dimension - Speed and Velocity, Acceleration...
1 Mutli-Attribute Decision Making Scott Matthews Courses: / /
MOTION GRAPHS CREATING AND INTERPRETING GRAPHS. WHAT DO WE KNOW On the paper provided, write down everything you know about graphs and graphing.
1 of 32 © 2014 Pearson Education, Inc. Publishing as Prentice Hall CHAPTER OUTLINE 7 The Production Process: The Behavior of Profit-Maximizing Firms The.
A graph represents the relationship between a pair of variables.
New approaches to Materials Education - a course authored by Mike Ashby and David Cebon, Cambridge, UK, 2007 © MFA and DC 2007 Unit 12. Using CES in research:
New approaches to Materials Education - a course authored by Mike Ashby and David Cebon, Cambridge, UK, 2007 © MFA and DC 2007 Unit 10. Structural sections:
New approaches to Materials Education - a course authored by Mike Ashby and David Cebon, Cambridge, UK, 2007 © MFA and DC 2007 Unit 2. Materials charts.
© MFA and DC 2007 New approaches to Materials Education - a course authored by Mike Ashby and David Cebon, Cambridge, UK, 2007 Unit 3. Translation, Screening,
MECH L# 10 Conflicting Objectives 1/30 MECH , Lecture 10 Objectives in Conflict: Trade-off Methods and Penalty Functions Textbook Chapters.
Lecture 8: Optimization with a Min objective AGEC 352 Spring 2012 – February 8 R. Keeney.
© MFA and DC 2007 New approaches to Materials Education - a course authored by Mike Ashby and David Cebon, Cambridge, UK, 2007 Unit 4. Ranking: refining.
4-1 Economics: Theory Through Applications. 4-2 This work is licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License.
Multi-objective Optimization
Chapter 21 Cost Minimization
Advanced Methods in Materials Selection
Density Graph Rules of Construction. Density Graphing Rules  First, look at all your values for mass and volume.  Next, decide on a scale that best.
Application to economics Consumer choice Profit maximisation.
Evolutionary Computing Chapter 12. / 26 Chapter 12: Multiobjective Evolutionary Algorithms Multiobjective optimisation problems (MOP) -Pareto optimality.
ME 330 Engineering Materials Lecture 3 Tension/Bending/Torsion/Material Selection Bending Torsion Material Selection Techniques Please read Chapter 6.
Structural & Multidisciplinary Optimization Group Deciding How Conservative A Designer Should Be: Simulating Future Tests and Redesign Nathaniel Price.
Chapter Four Consumer Choice Chapter Four. Chapter Four Consumer Choice Chapter Four.
© 2011 South-Western, a part of Cengage Learning, all rights reserved C H A P T E R 2011 update The Theory of Consumer Choice M icroeconomics P R I N C.
Progettazione di Materiali e Processi
PowerPoint Lectures for Principles of Economics, 9e
Progettazione di Materiali e Processi
Progettazione di Materiali e Processi
PowerPoint Lectures for Principles of Economics, 9e
Materials Selection Lecture #11 Materials Selection Software
Chapter 12 Case Studies: Hybrids
Chapter 15 Materials and the Environment
Chapter 11 Designing Hybrid Materials
Heuristic Optimization Methods Pareto Multiobjective Optimization
PowerPoint Lectures for Principles of Microeconomics, 9e
Chapter 15 Materials and the Environment
Multi-Objective Optimization
PowerPoint Lectures for Principles of Economics, 9e
Chapter 5 Materials Selection The Basics
Chapter 6 Case Studies: Materials Selection
PowerPoint Lectures for Principles of Economics, 9e
Chapter 4 Material Property Charts
Presentation transcript:

New approaches to Materials Education - a course authored by Mike Ashby and David Cebon, Cambridge, UK, 2007 © MFA and DC 2007 UNIT 6. Objectives in conflict: trade-off methods and penalty functions

© MFA and DC 2007 Conflicting objectives Outline More info: “Materials Selection in Mechanical Design”, Chapters 9 and 10 Multi-objective optimisation Trade-off methods Penalty functions and exchange constants Exercises

© MFA and DC 2007 Conflicting objectives in design Each defines a performance metric. Example mass, mwe wish to minimize both cost, C (all constraints being met) Common design objectives: Minimizing mass ( sprint bike; satellite components ) Minimizing volume ( mobile phone; minidisk player ) Minimizing environmental impact ( packaging, cars ) Maximizing performance ( speed, acceleration of a car ) Minimizing cost ( everything ) Objectives Conflict : the choice that optimises one does not optimise the other. Best choice is a compromise.

© MFA and DC 2007 Light Metric 1: Mass m Heavy Cheap Metric 2: Cost C Expensive Multi-objective optimisation: the terminology Trade-off surface: the surface on which the non-dominated solutions lie (also called the Pareto Front) Solution: a viable choice, meeting constraints, but not necessarily optimum by either criterion. Dominated solution: one that is unambiguously non-optimal (as A) A Dominated solution Non-dominated solution: one that is optimal by one metric (as B: optimal by one criterion but not necessarily by both) B Non-dominated solution Trade-off surface Plot solutions as function of performance metrics. (Convention: express objectives to be minimized)

© MFA and DC 2007 Finding a compromise: strategy 1 Make trade-off plot Sketch trade-off surface Use intuition to select a solution on the trade-off surface “Solutions” on or near the surface offer the best compromise between mass and cost Choose from among these; the choice depends on how highly you value a light weight, -- a question of relative values Light Metric 1: Mass m Heavy Cheap Metric 2: Cost C Expensive Trade-off surface

© MFA and DC 2007 Cost of ownership (pence/mile) Reciprocal of performance (1/Top speed) Cost-performance trade-off: cars Cars: Cost-Performance trade off Trade-off surface My car Cheaper and faster Slower and more expensive Cheaper but slower Faster but more expensive

© MFA and DC 2007 Finding a compromise: strategy 2 Reformulate all but one of the objectives as constraints, setting an an upper limit for it Good if budget limit Trade-off surface gives the best choice within budget Optimum solution minimising m Light Metric 1: Mass m Heavy Cheap Metric 2: Cost C Expensive Trade-off surface Best choice BUT….not true optimisation; cost is treated as constraint, not objective. Upper limit on C

© MFA and DC 2007 Light Metric 1: Mass m Heavy Cheap Metric 2: Cost C Expensive Finding a compromise: strategy 3 Optimum solution, minimising Z Z1Z1 Z2Z2 Z3Z3 Z4Z4 Contours of constant Z Decreasing values of Z Seek material with smallest Z: Either evaluate Z for each solution, and rank, Or make trade-off plot But what is the meaning of  ? plot on it contours of Z -- lines of constant Z have slope -  Read off solution with lowest Z Define locally-linear Penalty function Z

© MFA and DC 2007 Space vehicle 3000 to 10,000 The exchange constant  The quantity  is called an “exchange constant” -- a measure of the value of performance, here: value of saving 1 kg of mass. How get values of  ?  Full life costing: fuel saving, extra payload  Analysis of historic data;  Interviews with informed planners Transport system  ($ per kg) Exchange constants for mass saving Family car 0.5 to 1.5 Truck 5 to 20 Civil aircraft 100 to 500 Military hardware 500 to 2000

© MFA and DC 2007 Penalty function on log scales Log scales Lighter mass, m Heavier Cheap Cost, C Expensive Decreasing values of Z A linear relation, on log scales, plots as a curve Linear scales Lighter mass, m Heavier Cheap Cost, C Expensive Decreasing values of Z --

© MFA and DC 2007 Trade off: mass vs. cost for given stiffness Exchange constant  = 5 $/kg Exchange constant  = 500 $/kg Mass Material cost The light, stiff beam

© MFA and DC 2007 Plotting the penalty function Mass Material cost Plot this for chosen The penalty function is defined as The light, stiff beam

© MFA and DC 2007 The main points Real design problems involve conflicting objectives -- often technical performance vs. economic performance (cost). Trade-off plots reveal the options, and (when combined with the other constraints of the design) frequently point to a final choice If the relative value of the two metrics of performance (measured by and exchange constant) is known, a penalty function allows an unambiguous selection

© MFA and DC 2007 Demo: trade off plots

© MFA and DC 2007 Exercise: a trade-off plot 6.1 Freezers have panel-walls that provide thermal insulation and at the same time are stiff and strong. They are of sandwich construction with two skins of steel separated and bonded to an insulating core. In choosing the core we seek to minimize thermal conductivity while at the same time maximizing stiffness (and so Young’s modulus), allowing thinner, and thus lighter and cheaper faces. Make an appropriate trade-off plot to find materials that best do both. Remember that both objectives must be minimized – so use the reciprocal of Young’s modulus. Start with Level 2, then import Level 3 Graph stage  X-axis: 1/Young’s modulus (use the Advanced facility)  Y-axis: Thermal conductivity  Copy, paste into WORD or Adobe Illustrator and sketch in a trade-off surface. Trade-off surface

© MFA and DC 2007 Exercise: strength a min. weight vs. price 6.2 Explore the trade-off between minimizing mass and price for a component loaded in tension. The lightest material (that meets all other constraints, of course) is that with the lowest value of (density divided by yield strength). The one with lowest price is that with the lowest value of (the same as above, multiplied by the price per unit mass) Graph stage using the Advanced facility  X-axis: Density / Yield strength  Y-axis: Price * Density / Yield strength Trade-off surface

© MFA and DC 2007 End of Unit 6