Electrical and Computer Engineering

WIMS Seminar

MEMS Opportunities in John Deere Sensor Applications

Ms. Deborah Lickness

Advanced Sensor Engineer/Physicist, John Deere
John Deere and MEMS are two words that most people would not expect to use in the same sentence. But the fact is, John Deere has been tracking MEMS activities for several years. Why? Because MEMS sensors offer the opportunity to increase shareholder and customer value. How? By offering (1) enabling technologies, (2) improvements in performance and reliability, or (3) cost improvement opportunities.

To understand the impact that MEMS could have on Deere equipment, an understanding of current Deere applications is needed. A sensor developed primarily for one of today’s “killer apps”, may not be able to survive in the environment of a Deere application. A goal of Deere involvement in MEMS organizations is to promote an understanding of Deere sensing needs. This understanding provides the opportunity to design MEMS sensors that meet the conventional markets, as well as the new potential market within the John Deere Enterprise.

The presentation will promote the understanding of Deere equipment usage with an emphasis on sensing applications in:
1. Agriculture Equipment
2. Deere Power Systems
3. Consumer & Commercial Equipment
4. Construction & Forestry Equipment
Deb Lickness is an Engineer from John Deere. She has a BS in Applied Physics from the University of Northern Iowa, and will graduate in May from the University of Iowa with a Masters in Business Administration.

Deb has been with John Deere for 7 years. During that time, she has worked as a Design Engineer in Production Sensors, as a Strategic Buyer in Supply Management, and is currently working as Engineer in Advanced Sensor Technology. The Advanced Sensor team is responsible for the research and development of next generation or enabling sensor technologies for use on John Deere Agricultural, Construction, Forestry, Commercial, and Consumer product equipment

Sponsored by

WIMS ERC Seminar Series