Drying Device Using Lock-In Analysis for Uniform Temperature Control
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Solution Overview
Problem
Existing drying devices face challenges in enhancing drying efficiency, particularly in ensuring uniform drying and optimizing drying conditions based on real-time temperature and airflow dynamics.
Innovation Solution
The proposed drying device incorporates an infrared camera for acquiring temperature distribution information and a controller that performs lock-in analysis to correct noise, using pre-generated temperature and flow velocity distribution models via machine learning to adjust drying conditions in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hot air is blown to dry the surface of an article, then drying function is achieved, but drying efficiency is insufficient and uneven drying occurs
Solution Approach 1:
The system uses an infrared camera to detect temperature distribution on the article surface in real-time, and the controller adjusts hot air supply based on detected temperature variations. This closed-loop feedback mechanism enables dynamic optimization of drying conditions to achieve both high efficiency and uniformity
Solution Approach 2:
The drying system transitions from static hot air supply to dynamic adjustment based on real-time temperature distribution detection. The controller continuously modifies hot air supply parameters according to detected temperature variations, enabling adaptive optimization of drying efficiency and uniformity
2Manufacturing precision
If temperature distribution monitoring is implemented, then drying uniformity can be improved, but measurement precision is insufficient due to noise
Solution Approach 1:
A noise removal model acts as an intermediary between the raw temperature distribution data from the infrared camera and the control decisions. This intermediate processing step filters out noise while preserving genuine temperature variations, enabling accurate measurement for control purposes
Solution Approach 2:
The system replaces direct reliance on noisy infrared camera measurements with a machine learning-based noise removal model. This substitution transforms the measurement process from direct detection to intelligent inference, significantly improving measurement precision
3Productivity
If real-time temperature distribution control is implemented, then drying efficiency improves, but device complexity increases
Solution Approach 1:
The system employs machine learning models (noise removal model and temperature distribution estimation model) that automatically process temperature data and generate control decisions without human intervention. This self-service capability handles the complexity of real-time control algorithmically, reducing the need for complex manual control systems
4Measurement precision
If noise removal processing is applied to temperature distribution data, then measurement accuracy improves, but loss of information may occur
Solution Approach 1:
The noise removal model transforms the temperature distribution data by changing its statistical parameters, filtering out noise components while preserving genuine temperature variation patterns. This parameter transformation approach improves measurement precision without significant information loss
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
This approach significantly enhances drying efficiency by suppressing uneven drying, allowing for precise control of drying conditions, and enabling accurate estimation of drying completion time, thus reducing overall drying time and improving process efficiency.
Implementation Method 1
an infrared camera that acquires temperature distribution information that is information related to a temperature distribution inside the drying chamber
Implementation Method 2
a drying chamber (10) that includes a hot air supply port (15) that blows the hot air
Implementation Method 3
dries a surface of an article by blowing hot air
Data Source
AI summary
A drying device according to an embodiment dries a surface of an article by blowing hot air, and includes a drying chamber including a hot air supply port that blows the hot air, an infrared camera acquiring temperature distribution information that is information related to a temperature distribution inside the drying chamber, and a controller controlling a drying condition inside the drying chamber. The controller acquires corrected temperature distribution information by performing lock-in analysis of the temperature distribution information to remove noise from the temperature distribution information, and controls the drying condition based on the corrected temperature distribution information and based on a temperature distribution model that is a model related to the temperature distribution inside the drying chamber and is pre-generated using machine learning.