Kirigami-Inspired Flexible Temperature Sensor; Reading the Signals: The Impact of Training Protocols on Effective Sensor Interpretation

In complex industrial systems, the accurate interpretation of sensor data by human operators is crucial for preventing disasters. The consequences of misinterpreting these signals in high-stakes operations can be severe, as illustrated by incidents such as the Three Mile Island reactor in 1979, the Davis-Besse reactor in 2002, and various aviation incidents involving instrument misinterpretation in the 1970s and 1980s. These events underscore the critical challenge of ensuring that operators are properly prepared to interpret and respond to the data presented by an array of sensors. This research addresses the broad challenge of ensuring effective operator response to sensor data in complex systems, a problem exemplified by the specific difficulties encountered in nuclear and aviation operations, as well as the need for improved medical devices. For instance, the Three Mile Island incident highlighted the dangers of misinterpreting position sensor data, leading to a severe reactor malfunction. Aviation accidents have similarly demonstrated the risks associated with misinterpreting light-based instruments, contributing to spatial disorientation. The Davis-Besse incident illustrated the problem of failing to recognize temperature fluctuations, which signaled dangerous component degradation. Additionally, in healthcare, traditional medical devices often suffer from bulkiness, delays, and the potential to cause skin irritation, highlighting the need for improved sensor technology for patient monitoring and care. The technical research project focused on designing, fabricating, and testing a compact, flexible, biocompatible, and stretchable sensor to address the shortcomings of traditional medical devices. A key innovation explored was the incorporation of kirigami cuts into the substrate material to enhance the sensor's elasticity and flexibility. The project involved an iterative design process, including the analysis of various conductive ink designs and substrate materials, using both physical and analytical tests to optimize the sensor's performance. The final design utilized a combination of PDMS and carbon nanotubes for conductivity and incorporated kirigami cuts with rounded edges to maximize stretchability and minimize stress on the conductive ink. The STS research explored the socio-technical factors influencing the effectiveness of sensor data interpretation in high-risk industries. It examined the critical role of human operators in interpreting complex sensor data and how inadequate training or flawed human-machine interfaces can contribute to or exacerbate crises, as seen in the Three Mile Island, Davis-Besse, and aviation incidents. The research highlights how societal expectations, regulatory oversight, and economic factors shape operator training and system design. It emphasizes the need for training protocols that not only cover routine operations but also equip operators with critical thinking skills to manage unexpected events and interpret ambiguous sensor data. This research underscores the importance of effective sensor design and comprehensive operator training in mitigating risks associated with complex systems. The technical project successfully designed a flexible temperature sensor, demonstrating the potential of innovative materials and techniques to improve medical devices. The STS analysis provided critical insights into the systemic factors that influence sensor data interpretation and operator performance in high-risk environments. Future work should continue to explore advancements in sensor technology, human-machine interfaces, and training methodologies to enhance safety and reliability across various industries.

School of Engineering and Applied Science

Bachelor of Science in Mechanical Engineering

Technical Advisor: Baoxing Xu

STS Advisor: Kent Wayland

Technical Team Members: Annabella Caporaletti, Troy Dodd, Tahmid Mahi, Katrina Shaffer, Sam Thomas