Smart Oven Humidity and Temperature Feedback for Doneness Control
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Solution Overview
Problem
Predefined cooking modes in cooking appliances often fail to provide optimal results due to variations in food size, shape, and initial temperature, as they do not account for specific humidity levels and cavity conditions, leading to inconsistent cooking outcomes.
Innovation Solution
A smart oven system equipped with food and cavity humidity sensors, temperature sensors, and a processor that recognizes food class, determines a target doneness score, and adjusts the heating process using a machine-learning assisted algorithm to predict and maintain desired humidity levels for precise cooking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predefined cooking modes are used, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system continuously monitors food humidity level, cavity humidity level, and cavity temperature during cooking, then uses this feedback to dynamically adjust heating power and cooking time. This closed-loop control enables the oven to adapt to actual cooking conditions while maintaining ease of operation through automated adjustments.
Solution Approach 2:
The cooking system automatically determines doneness based on monitored parameters and adjusts cooking parameters without user intervention. The processor autonomously controls the heating element based on humidity and temperature readings, making the system self-regulating while preserving user convenience.
2Device complexity
If predefined cooking modes are used, then device complexity is reduced, but reliability deteriorates
Solution Approach 1:
The system employs continuous monitoring of humidity and temperature with real-time feedback to the control processor, ensuring consistent cooking results across different food items. The feedback mechanism compensates for variations in food size, shape, and initial temperature, maintaining reliability without significantly increasing device complexity.
Solution Approach 2:
The system dynamically changes cooking parameters (heating power, cooking time, temperature) based on monitored humidity levels and doneness scores. This adaptive parameter adjustment ensures reliable cooking outcomes while working within the constraints of the existing device architecture.
3Manufacturing precision
If humidity sensing and dynamic control are implemented, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The humidity sensor serves multiple functions: monitoring food humidity level, monitoring cavity humidity level, and providing input for doneness determination. This multi-functionality reduces the need for separate sensing systems, minimizing the increase in device complexity while maintaining high cooking precision.
Solution Approach 2:
The processor acts as an intermediary that integrates data from multiple sensors (humidity sensors, temperature sensor) and translates this information into controlled heating adjustments. This centralized control approach manages complexity by consolidating decision-making logic in a single component rather than distributing it across multiple systems.
4Reliability
If real-time monitoring and dynamic adjustment are used, then reliability is improved, but use of energy increases
Solution Approach 1:
The system monitors humidity and temperature at periodic intervals during cooking rather than continuously, reducing energy consumption while maintaining reliable doneness determination. The periodic sampling provides sufficient data for accurate doneness scoring without the excessive energy use of continuous monitoring.
Solution Approach 2:
The system applies heating at varying power levels based on doneness requirements rather than maintaining constant high-power heating. By adjusting heating intensity to match actual cooking needs, the system achieves reliable cooking results while reducing overall energy consumption compared to fixed high-power modes.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time automated cooking cycles that ensure food is cooked to the desired doneness by accurately monitoring and adjusting humidity and temperature, improving cooking consistency and user experience.
Implementation Method 1
a heating system to cook the food item
Implementation Method 2
at least one food humidity sensor for acquiring a food humidity level
Implementation Method 3
at least one cavity humidity sensor for acquiring a cavity humidity level
Implementation Method 4
at least one cavity temperature sensor for acquiring a cavity temperature
Data Source
AI summary
A method for controlling a heating process may include recognizing a food class of a food item, defining a target doneness score for the food item based on at least one of the food class and a desired doneness level, receiving a food humidity level, receiving a cavity humidity level, receiving a cavity temperature, determining a current doneness score of the food item according to the food humidity level, the cavity humidity level and the cavity temperature, and utilizing the current doneness score and the target doneness score to control a heating system to cook the food item.