2024 ASEE Annual Conference & Exposition

In the dominion of project-based learning, we embarked on a journey to create an innovative time prediction thermometer tailored for food systems. Throughout this endeavor, we explored a range of fundamental principles that have proven invaluable for our lifelong learning journey. As students, this project provided fertile ground for honing our problem-solving skills and immersing ourselves in the intricate world of engineering design.

Our journey began with identifying a culinary challenge, followed by brainstorming potential solutions, selecting the most efficient approach, and executing it meticulously. Beyond these experiences, this endeavor equipped us with a wealth of practical skills, including fabrication, design, analysis, and the art of technical writing. It served as a platform for us to refine our expertise in computer-aided design (CAD), research methodologies, and the dynamics of collaborative teamwork. Remarkably, the implications of our design extend beyond the confines of our educational journey, offering potential applications within the broader realm of engineering education

Presently, many amateur cooks lack the intuition and experience required to gauge the optimal cooking time, often resulting in suboptimal culinary outcomes that are either undercooked or overcooked. Our team made a deliberate choice to construct a temperature probe that would mitigate this issue by precisely measuring the internal temperature of food and forecasting its future temperature, which we have aptly named a "smart thermometer." In this paper, we elucidate the design criteria for ThermoChef++ (TC++), a budget-friendly smart thermometer, in comparison to existing time prediction models in literature and smart thermometers available in the consumer market. We elucidate the development of software and Arduino circuits in the realization of our project.

During the culinary process, TC++ continuously monitors real-time temperature data and employs regression analysis to construct thermal models that predict the future behavior of the food system. An additional feature of TC++ is a library containing standard cooking models for common foods, promoting heightened awareness of cooking safety. The current iteration of our product is tailored for novice cooks who rely on indirect temperature cues. However, an enhanced iteration of TC++ holds potential implications for the food manufacturing industry.