Reflow Oven Zone Temperature Setting Using Machine Learning Feedback
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Solution Overview
Problem
The existing methods for setting temperatures in reflow ovens for printed circuit board (PCB) production rely heavily on engineer experience, requiring extensive data collection and calculations, which is time-consuming and affects production efficiency.
Innovation Solution
A method using an electronic device with a machine learning model to predict initial setting temperatures for reflow oven zones, based on historical data, to determine target feature data and adjust temperatures for optimal production conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temperature setting relies on engineer experience and manual data collection, then temperature control accuracy can be achieved, but production efficiency deteriorates due to time-consuming adjustments
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal temperature settings for each furnace zone before production begins. The temperature control device stores historical temperature data and uses it to determine appropriate temperature settings in advance, eliminating the need for manual data collection and iterative adjustments during production.
Solution Approach 2:
A temperature control device acts as an intermediary between the furnace zones and operators. This device automatically determines temperature settings based on stored historical data and current production requirements, replacing manual engineer judgment and reducing the time needed for temperature adjustments while maintaining accuracy.
2Reliability
If multiple reference data collections and calculations are performed to determine temperature settings, then production quality can be ensured, but time consumption increases affecting overall production efficiency
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal temperature settings for each furnace zone before production begins. The temperature control device stores historical temperature data and uses it to determine appropriate temperature settings in advance, eliminating the need for manual data collection and iterative adjustments during production.
Solution Approach 2:
The system incorporates feedback mechanisms where the temperature control device continuously monitors furnace temperature data and compares it against stored historical data and production requirements. This feedback loop enables automatic adjustment of temperature settings to ensure production quality without requiring manual intervention and extensive time for calculations.
Data Source
AI summary
A method for determining temperature of reflow oven is provided. In the method, the electronic device receives an initial setting temperature of each of at least one zone of the reflow oven and obtains target feature data of each of the at least one zone of the reflow oven by predicting the initial setting temperature through a predetermined machine learning model. The electronic device further obtains actual data of each of the at least one zone of the reflow oven corresponding to the initial setting temperature in response that the initial setting temperature meets the production requirements and determines a target setting temperature of each of the at least one zone of the reflow oven based on the preset conditions, the initial setting temperature, and the actual data.