Business Scenario
AI-Enabled Thermal Monitoring for Smarter Kitchen Appliances
Ensures safer, more efficient cooking through real- time temperature control and predictive insights
30%
Improvement in Temperature Control Accuracy
Problem Statement
A leading Japanese appliances manufacturer sought to enhance the safety and intelligence of its IH cooking heater systems by integrating real-time food temperature sensing directly within the kitchen chimney. The goal was to capture accurate cooking surface data using an IR sensor array and display the information in real time. Traditional methods lacked integration, limiting visibility into cooking progress and creating potential risks of overheating. The customer required a partner to design, implement and validate a complete solution from data capture to visualization within a three-month development window.
Solution
We engineered a Python-based application running on Ubuntu, interfaced with the IR sensor through a custom connection board. The system architecture enabled the acquisition of 8x8 temperature matrices in real time, with visualization modules to display live cooking surface heat maps. Deliverables included not only the working program but also the full source code, connection hardware, and user instructions to ensure seamless integration with IH cooking heater units. This modular solution provided the customer with flexibility for future scalability and multi-sensor expansion.
Impact
The solution delivered precise and continuous temperature monitoring during the cooking process, reducing the risk of overheating and enabling smarter cooking experiences. By providing real-time visibility of temperature distributions, this could enhance both consumer safety and cooking efficiency. The rapid three-month development cycle ensured readiness for evaluation and potential market integration. This project also established a scalable platform for the customer to expand IR-based sensing across future smart kitchen appliances.
Services Rendered
- Services Rendered
- Problem definition and requirement analysis
- AI model development
- Sensor calibration, Test datasets
- Integration with IH cooking heater and chimney hardware
- MLOps implementation for sensor data monitoring and validation
- Testing & deployment
- Post-launch support including hardware integration guidance
