This document is not field policy and has not been subject to field vote. It is a working document provided as a good-faith attempt to describe the current shared viewpoint of the faculty. Per field rules, the exam committee decides the scope of the exam and questions are at the discretion of the committee.
This page is a copy of the original Design Q Study Guide: https://docs.google.com/spreadsheets/d/1z4R4ow-KLBYwNJFzyfvhFCpb6ySimJbpXoB8JDW6Sns
You are encouraged to check both to ensure that the information provided is fully up to date.
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Q exam name: Design
Field(s) to which it applies: ME, AE
Core principles covered: This qualifying exam evaluates the student’s preparedness for Ph.D level research in the field of Engineering Design, including but not limited to the following sub-areas: design theory and methodologies, materials and manufacturing, design synthesis and evaluation, automated design, human-computer interaction for design, design of human-robot interaction systems, design for X (e.g. design for manufacturing, design for sustainability, design for resiliency).
Such research includes the following contexts: robotics research concerned with the design of robotic systems; Human-Robot and Human-Agent Interaction research, where either design or system evaluation is a core component; research on new methods for integrating mechanical components in user-centric applications; and research on new methods and tools for designing complex systems such as space systems, robotic systems, and additive manufacturing systems.
The exam evalutes the student’s mastery of core concepts related to Engineering Design; their ability to tackle a new design problem, analyse the design alternatives, and justify their design choices; and their ability to comprehend and present an Engineering Design related research paper.
The exam committee decides the scope of the exam and questions are at the discretion of the committee; however, this list is provided as a good-faith outline of topics covered as a way to frame student’s broad study.
Recommended textbooks for study:
Hazelrigg, G. A. (2012). Fundamentals of Decision Making for Engineering Design and Systems Engineering.
Kalpakjian & Schmid (2014). Manufacturing Engineering and Technology (7th Ed), Pearson
Lowry, R. (Online) Concepts & Applications of Inferential Statistics
Creswell (2013). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Edition
Additional reading options:
Field, Miles, & Field (2012). Discovering Statistics Using R, Sage.
Papalambros, P. Y., & Wilde, D. J. (2000). Principles of Optimal Design. Cambridge University Press.
Montgomery (2001). Design and Analysis of Experiments (5th Ed). Wiley
Lewis, K., Chen, W., Schmidt, L. (2006). Decision making in engineering design. ASME.
Classes required before Q exam: none
Classes deemed helpful but not always taken before Q exam: MAE 5950: System Architecture, MAE 6710: Human-Robot Interaction, MAE 2250, MAE 6950: Advanced Manufacturing
Typical Format:
The exam lasts an hour and includes the following components:
One week before the Q exam date:
At least two business days before the Q exam date, students are encouraged to submit a 1-2 page write up describing their design challenge and the questions they plan to address. The committee will then provide feedback to the candidate concerning the choice of design challenge and the way to approach certain questions and topics.
On the date of the Q exam:
- 15 minute presentation of a research paper in the field, including any of the sub-areaslisted above. This can be a research paper from a class, a paper related to the student’s research interests, or an original paper that the student authored. The student should expect questions from the exam committee during or after their presentation, so please plan for 10 minutes of presentation at most, to allow for 5 minutes of Q&A.
- 30 minute presentation of the design project responding to the approved challenge, including the application of two out of the high level topics (see below: Applied Statistics, Design Decision Making, and Manufacturing) of the student’s choice.
- 15 minute elaboration in response to additional questions posed by the faculty. This will include a question by the examiners about one choice of sub-topic (e.g., Gradient-Based Optimization) from the third high-level topic that was not chosen by the student.
Topics of Study
Listed are courses that cover the specific topics as well as chapters from specific sources. See recommended textbooks for the book sources.
Applied Statistics |
MAE 6710: Human-Robot Interaction | |||
Probability Theory | Lowry | Creswell | Field, Miles, & Field | Montgomery |
Probabilities | Ch 5 | Ch2 | ||
PDF, CDF | Ch 1-2, 6 | Ch2 | ||
Distributions, Moments, Descriptives, Z Scores | Ch 1-2, 6 | Ch 1-2 | ||
Conditional and marginal probabilities, law of total probability | ||||
Bayes’ Theorem | ||||
Experimental Design | ||||
Validity (face, internal, external, ecological), Reliability, and Sensitivity | Ch 8 | Ch 1.5 | ||
Design: Independent / Dependent Variables, Repeated Measures vs Between Subjects | Ch 8 | Ch 2 | ||
Control in Between / Within/ Before-After Designs | Ch 8 | |||
Bias in experimental design, counter measures: pre-test, counterbalancing | Ch 8 | Ch 1 | ||
Metric and scale design | Ch 8 | |||
Inferential Statistics | ||||
Correlations (Perason, Spearman, bootstrapping) | Ch 3 | Ch 6 | ||
Regression (Model, fit, parameters, least squares) | Ch 3 | Ch 7 | ||
Sample means comparison: T-test, independent and repeated measures | Ch 4-7, 9-12 | Ch 9 | Ch 2 | |
ANOVA: Theory and application | Ch 14-15 | Ch 10 | Ch 3 | |
Non-parametric test: Wilcoxon rank-sum / Kruskal-Wallis | Ch 11-12 |
Design Decision Making |
MAE 5959: System Architecture | ||
Utility Theory | Haxelrigg | Leis et al | Papalambros |
Lotteries | Ch 5 | Ch 3 | |
Axioms | Ch 5 | Ch 3 | |
Single-attribute utility functions | Ch 5 | Ch 3, 4 | |
Independence conditions | Ch 7 | Ch 12 | |
Multi-attribute utility functions | Ch 7 | Ch 12 | |
Value of information | Ch 5 | ||
Optimization | |||
Identification and classification of critical points in continuous functions | Ch 3 | Ch 1 | |
Constrained optimization: Kurush-Kuhn-Tucker conditions, Lagrange multipliers | Ch 3 | Ch 5, 7 | |
Linear Programming – Simplex | Ch 3 | Ch 5 | |
Gradient-based optimization | Ch 3 | Ch 4, 7 | |
Heuristic methods | Ch 3 | Ch 7 | |
Multi-objective optimization | Ch 3 | Ch 1 |
Materials and Manufacturing |
MAE 2250 Mechanical Synthesis, MAE 6950:Advanced Manufacturing |
Materials | Kalpakjian and Schnid |
Properties of Metals Ceramic, Polymers, Composites | Ch 1-9 |
Manufacturing | |
Subtractive Processes | Ch 21, Ch 27 |
Additive Processes | Review Papers
1. Additive manufacturing of metallic components – process, structure, and properties 2. Polymer design for 3D printing elastomers: recent advances in structure, properties, and printing. 3. Polymers for 3D printing and customized additive manufacturing |
Forming Processes | Ch 30-31 |
Joining Processes | Ch 33-34 |