Dough Kneading State Detection Using Sensor Segmentation
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Solution Overview
Problem
Current systems fail to determine the optimal kneading time for dough with precision, leading to poor-quality final products due to under-mixing or over-mixing, as the kneading time and mechanical work applied are critical for developing rheological properties, and existing sensor systems struggle to accurately monitor the kneading cycle in real time.
Innovation Solution
A method involving a kneading machine with sensors to continuously measure representative quantities during the kneading cycle, using a kneading state model defined through a learning phase to determine the kneading state and stop criterion, allowing for precise monitoring and automation of the kneading process, including a learning phase with test cycles to define and adapt the kneading stop criterion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor systems are used to monitor kneading, then the system complexity is low, but the measurement precision of kneading state is insufficient
Solution Approach 1:
The monitoring system is segmented into multiple independent sensor modules, each measuring specific physical quantities (power, temperature, speed, torque). This segmentation allows precise measurement of individual parameters while keeping each sensor module simple and maintainable, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
A processing unit acts as an intermediary that receives data from multiple simple sensors and computes the kneading state through mathematical models. This intermediary transforms simple sensor readings into precise kneading state information, achieving high measurement precision without requiring complex individual sensors.
2Manufacturing precision
If kneading time is determined by operator experience, then the ease of operation is high, but the manufacturing precision of dough quality is poor
Solution Approach 1:
The system performs self-service by automatically determining the optimal kneading stop point through real-time monitoring of physical quantities and comparison with reference values. The machine autonomously controls the kneading process without requiring operator expertise, achieving consistent dough quality while simplifying operation through automation.
Solution Approach 2:
The system implements continuous feedback by monitoring kneading parameters in real-time and comparing them against reference values obtained during a learning phase. This feedback mechanism automatically adjusts the kneading process to maintain optimal conditions, ensuring consistent dough quality without requiring operator intervention or complex manual adjustments.
3Manufacturing precision
If real-time monitoring of multiple physical quantities is implemented, then the manufacturing precision of kneading state determination is improved, but the use of energy and device complexity increase
Solution Approach 1:
The system performs preliminary action during a learning phase where reference values for optimal kneading are established. This pre-acquired reference data enables accurate real-time monitoring without requiring continuous complex computations or additional sensors during actual production, reducing energy consumption while maintaining high precision in determining kneading completion.
Data Source
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AI summary
The present description relates to a method for determining the kneading state of a dough such as a cereal dough, the dough being configured to be kneaded in a kneading machine (1), the kneading machine (1) comprising a plurality of sensors (13, 15, 17) configured to measure quantities representative of the dough during a kneading cycle and at least one kneading tool, the method comprising the steps of: - collecting measurements of the quantities representative of the dough continuously during the kneading cycle; - determining a kneading state on the basis of a kneading state model defined according to the measurements of the representative quantities, the kneading state model being defined or adapted on the basis of a learning phase; - verifying the achievement of a kneading stop criterion based on the kneading state.