David R. Schneider graduated from Rensselaer Polytechnic Institute in Chemical Engineering in 1999, attended Columbia University Film M.F.A. Program in 2001, and earned his Masters and PhD from Cornell University in Mechanical Engineering with a concentration in Controls & Dynamics in 2007. David has taught at both Cornell and Columbia University. His most prominent research is his creation of the G*TA (G-Star-T-A) task allocation algorithm and his work as Program Manager of the Cornell RoboFlag program, with notable applications including AFRL UAV controls and NASA/NOAA unmanned boat designs. With a strong focus on education, David’s endeavors have included the creation of the Intel-Cornell Cup, Innovative Embedded Design National Competition; leading Cornell University Sustainable Design (CUSD); and the broader impacts video game creation for the NSF Expeditions in Computing Grant on Computational Sustainability. David has led the efforts to make Cornell the first university to officially partner with Make: and is a leader in the Higher Education Maker Alliance working with the White House Office of Science and Technology Policy. He has also led with Make: the re-creation of the national entrepreneurial competition “Pitch Your Prototype” and is a leading faculty member behind the American Society of Engineering Education, Community Engaged Division Film Festival national competition. David was also a screenwriter for Walt Disney Attractions Television Production.
Decision matrices are one of the most commonly used engineering tools. They are used to help rationalize why one option should be chosen over another, and you can find some form of them in just about every business, industry, and government. Decision matrices may not always be identified as such but can be used as part of a trade study, competitive analysis, or options review. As prevalent as these matrices are, they are also one of the most misused tools out there.
In this course, you begin by developing performance metrics. These performance metrics will allow you to objectively determine the value of any potential solution to a challenge. You will then develop a decision matrix around these metrics by applying justifiable weights and tuning the metrics to account for the needs and priorities of specific customers. By learning how to create a superior decision matrix with these well-defined performance metrics, you can achieve tremendous influence on a project even if you do not have official authority.
The quality function deployment (QFD) is one of the most effective methods for relating performance metrics that a customer cares about to technical criteria and engineering parameters and ultimately, the design targets a team needs to build their solution. You will learn that the QFD expresses this relationship in a way that allows you to compare your concepts to your competitors’ and to understand the trade-offs between engineering parameters and their influence on performance criteria. This equips you to argue effectively that your design targets will lead your team to a winning solution.
In this course, you will go through a detailed, step-by-step process to build a QFD for your own project. You will examine the interrelationship between different engineering characteristics. You will use all this information, along with factors such as cost and technical difficulty, to establish strong design targets and get an estimate of your final system’s performance.
Interfaces are one of the most important parts of design and design implementation. However, they are often one of the most challenging aspects to identify and manage, and one of the most common points of failure of any system. As a result, there has been a multitude of software developed to aid in managing this process. However, without a strong understanding of the interfaces and how the subsystem teams work together, the use of the software packages is futile. They are only as good as the information put into them.
In this course, you will explore a number of different tools including sequence diagrams and interface matrices to help tease out and formalize your interfaces and interface specifications. This formalization step will help your team to discuss the impact and the dependencies of these interfaces. You will then produce the details and record them as interface specifications so that your team can design and create a well-integrated credible system.
Everyone worries about risk. How do we identify risks? Is this issue more risky than another? Or even worse, "Sorry, but this project sounds too risky. We can't approve it." Wouldn't it be better if you could show an objective understanding of risks, how to plan to address them, and be able to justify the decisions behind those plans?
In this course, you will learn how to assess risk with failure modes and effect analysis. You will evaluate different losses of functionality that your system could experience, and determine the possible effects and related causes. You will then develop objective ways of measuring the severity and likelihood of each of these causes, ultimately to develop a quantifiable measure of system risk. You will produce this analysis in a way that not only allows you to make decisions on how to handle these risks, but also justify your actions to others. This course equips you to recognize risk and reduce it.