How Many Engineers Does It Take To Make A Measurement

The emergence of nano-technology has driven the evolution of instrumentation tools and has revolutionized the measurement industry. The new technology also impacts engineering education with challenges to prepare the next generation of graduates to be competent and effective in this rapidly evolving field. This paper examines three current industry applications and explores their implications for curriculum development and delivery. The first application involves measuring the performance of prototypes to validate automobile design. The second concerns continuous status assessment of missiles and the third deals with instrumentation embedded within advanced production tools used in the semiconductor industry. Inexpensive embedded instrumentation empowers data generation requiring a fraction of the human resources and at an acquisition rate many orders of magnitude greater than was possible even a decade ago. The new measurement technology puts emphasis on timing, accuracy and stability, troubleshooting and formatting gigabytes (and more) in a reliable and unambiguous way. The paper offers an example showing how these changes can be incorporated into a typical curriculum without massive restructuring. Maintaining educational relevance Every technology-focused educational group goes to great lengths to maintain the currency and relevance of its programs. The most common methods are: Receive advice from an Industry Advisory Board. The process works well, especially if meetings are held more than once per semester and the industry members carry their message into the class-room as guest speakers and act as hosts for company visits. Through conferences, research and applications-focused partnerships. Opportunities for hands-on experience and student involvement through internships and projects follow. Provide a series of short courses for industry. The issues are always pragmatic and often very basic but there is no better way to learn about the practical skills requirements in the workforce. The success criteria are harsh. If a course does not add value, it will not survive no matter how enthusiastic its academic proponents may be. The authors use all three methods of interaction. One conclusion is that the world of measurement is changing rapidly. It would be easy to miss this outcome since it is not unique to any particular course or technology. However, it touches on every aspect of product design, manufacturing and support so the implications are important. To determine the scope and nature of the changes and trends in industry practice, a number of case studies were undertaken. They are drawn from the activities of a P ge 13672.2 combined industry-academic team that oversees the scope, content and outcomes of a series of industry short courses on instrumentation . The purpose of this paper is to present the outcomes of three use-cases, to infer skills and techniques that need further development and to show how the conclusions are being used to shape the content and priorities of a degree program. To answer the question posed in the title of the paper, measurement productivity has increased by many orders of magnitude over the past two decades. The effort to make a measurement can now be assessed in units of “nano-engineers”. There have been few dramatic breakthroughs but each new system generation incorporates more automated measurement and control features so that over a period of years the generic principles have been extended into totally new applications contexts. However, they may be most simply summarized as a natural consequence of the increasing complexity and requirements of electronic systems. The paper offers a snapshot of three typical industry trends and an academic response. Use case 1 – Automotive. The achievement of greater economy, reliability and safety in cars relies heavily on greater use of electronics to measure a wider range of functions and thus allow more sophisticated control and diagnostic algorithms to be implemented. Some are controversial such as the use of ‘black box’ data for accident analysis . However, there is also a more general question; how does the data from a real event like that shown in Figure 1 compare with the evaluations from controlled crash tests? Figure 1. Reality can be different from lab conditions This is an example of a general and rapidly developing instrumentation requirement. Almost all product design makes heavy use of simulation. This approach has given massive performance and quality improvements but the simulations still have to be validated, especially under worst-case conditions. A good example concerns under-hood temperatures. A casual inspection of any modern vehicle is enough to show that there is very little space available under the hood and temperatures can often rise to 150 C. Since reliability decreases rapidly as temperatures rise, it is important to have values for KCAL