There will be five 55-minute tutorial sessions on September 24.

Tutorial #1

Introduction to Axiomatic Design Theory
Mary Kathryn Thompson

Department of Mechanical Engineering
Technical University of Denmark

Axiomatic Design (AD) Theory is one of the most comprehensive and well-established engineering design theories developed to date. This tutorial will provide a brief overview of AD, its history, and the motivation for its development. It will present the axiomatic design process, key concepts and vocabulary associated with AD, and the design axioms. The material is based primarily on Suh’s books The Principles of Design (1990) and Axiomatic Design: Advances and Applications (2001). No previous knowledge of AD is required or expected. Everyone is welcome.

Tutorial #2

Dealing with redundant designs
António Gonçalves-Coelho
Department of Mechanical Engineering
Universidade Nova de Lisboa

The term “redundancy” is usually employed to mean some needless repetition. In engineering design, however, redundancy seldom means worthlessness. Several cases of engineering redundancy promptly come to our minds, as for example the retractable hydraulic-powered landing gears of aircraft that are backed-up by hand-operated mechanical systems. Redundancy can also make “design objects” more suitable to the working conditions, thus enhancing their usability and/or reliability. Axiomatic Design is quite appropriate to deal with redundancy and this tutorial will show how to identify redundancy and how to develop redundant designs taking into account the independence axiom.

Tutorial #3

 The Linearity Theorem and its influence in design parameter selection
Efrén Moreno Benavides
Department of Aerospace Propulsion and Fluid Mechanics
Universidad Politécnica de Madrid
Joan B. Rodríguez

In engineering challenges, the laws of physics establish the relationships between functional requirements, constraints and design parameters, whereas Axiomatic Design establishes the conceptual formulation of the rules that will drive the engineer’s decisions towards a leader product. As Axiomatic Design is optimal to define the concept of “best” design, this tutorial explores how it is used in order to identify the appropriate set of design parameters among the ones available from the formal formulation of the design problem. In this context, the Linearity Theorem appears as a suitable rationale to select the best design parameters in accordance with the principles of Axiomatic Design.

Tutorial #4

Teaching and Learning Axiomatic Design in Several Contexts
Christopher A. Brown
Worcester Polytechnic Institute

Teaching Axiomatic Design (AD) is approached as a basic problem in AD.  The tutorial is intended primarily for people who will be teaching AD.   For people new to AD this tutorial also will be interesting as a case study in using AD to solve this particular kind of problem.  The tutorial assumes knowledge of AD at a basic level, and will be within reach of those who have followed tutorial #1 by Prof. Thompson.   This tutorial addresses the teaching of AD in short courses to industry, or to other teachers, and in semester long university courses at grad or undergrad levels.  It will also be useful for people teaching AD in the context of project advising, at several levels, including industry and pre-university education.  Participants in this tutorial will be introduced to some advanced concepts in AD and be shown how they can be beneficial to teaching.  The material is based on more than three decades of using, teaching and developing AD.  FR0: provide the participants the most fundamental and powerful design tool in all of engineering, Axiomatic Design.

Tutorial #5

Mathematical Elucidations of Axiomatic Design Thru Case Studies
Hilario (Larry) Oh
MIT Park Center for Complex Systems

We use four successful case studies to elucidate the mathematical basis of Axiomatic Design: (1) Design of Wheel Cover; (2) Reducing Emergency Overcrowding; (3) Establishing The Operating Range of Crawler Crane; and (4) Reducing Tolerance Stack-up in Assembly. The first Case Study illustrates the implementation of two tasks in AD: eliminate the bias and reduce the spread. It is then possible to derive expressions using the design variables (DV), the noise variables (NV) and the design parameters (DP), which allow showing that the Jacobian matrix is made up of two sensitivity sub-matrices, [∂FR/∂DV] and [∂FR/∂NV]. Case Study 2 illustrates the instance when [∂FR/∂DV] shows dependent FRs. The sensitivity matrices can contain variables that may be used as DPs. Case Study 3 shows how a proper choice of the DPs places the crane in a stable operating range. Case Study 4 shows how choosing the DPs reduce tolerance stack-up in assemblies.