Genetic Algorithm Parameter Optimization for Heat Treatment
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Solution Overview
Problem
Batch-type heat treatment apparatuses face challenges in regulating temperature parameters efficiently, requiring repeated and labor-intensive PID parameter regulation.
Innovation Solution
A heat treatment apparatus with a receiving unit, simulation performing unit, evaluation function value calculating unit, and parameter updating unit, utilizing a genetic algorithm to generate new parameters for optimal temperature control, based on evaluation indexes such as sum of errors, temperature differences, and differential absolute values of power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If batch-type heat treatment apparatus is used to perform film formation processing, then processing efficiency is improved, but temperature regulation capability deteriorates
Solution Approach 1:
The system performs self-regulation by automatically calculating evaluation function values, comparing them against target values, and adjusting PID parameters without requiring manual intervention. The controller autonomously iterates through multiple calculations to achieve optimal temperature control, making the system self-sufficient in parameter optimization.
Solution Approach 2:
The system implements feedback control by calculating evaluation function values based on simulation results, comparing these values against target values, and using the comparison results to adjust PID parameters. This closed-loop feedback mechanism enables automatic optimization of temperature regulation while maintaining batch processing efficiency.
2Measurement precision
If repeated PID parameter regulation is performed to achieve optimal temperature control, then temperature control precision is improved, but time consumption and labor increase
Solution Approach 1:
The system performs preliminary calculations by pre-defining multiple sets of PID parameters and their corresponding evaluation function calculations. By preparing these parameter sets in advance and systematically evaluating them, the system avoids ad-hoc repeated adjustments, reducing both time consumption and manual labor while achieving optimal temperature control precision.
Solution Approach 2:
The system introduces an intermediary evaluation function as a mediator between the PID parameters and the temperature control outcome. This evaluation function quantifies the relationship between parameters and control precision, enabling automated comparison and selection of optimal parameters without requiring repeated manual trial-and-adjustment cycles.
3Manufacturing precision
If multiple simulations are conducted to optimize parameters, then parameter optimization accuracy is improved, but computational load and labor increase
Solution Approach 1:
The system segments the parameter optimization process into discrete, manageable steps by dividing PID parameters into separate groups (proportional gain, integral time, differential gain) and evaluating their combinations systematically. This segmentation allows the complex optimization problem to be broken down into smaller calculation units, improving accuracy while making the computational load more manageable and structured.
Data Source
AI summary
The controller receives information including a plurality of evaluation indexes, a weight of each evaluation index, the number of times for calculating a value of an evaluation function, and initial parameter values, and performs a simulation based on the received information. Then, the controller calculates a value of an evaluation function based on a result of the simulation, and determines whether the calculated value of the evaluation function is minimum, to update parameters when it is determined that the value of the evaluation function is minimum. In the calculation of a value of the evaluation function, a value of the evaluation function is calculated again based on the number of times for calculating a value of an evaluation function. The controller generates new parameters by a genetic algorithm when a value of an evaluation function is calculated again.