Industrial Control Configuration for Quality Optimization Under Constraints
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Solution Overview
Problem
Existing industrial control systems struggle to improve product quality beyond historical best without violating constraints, especially under changing environments with noisy data, relying on random experiments or domain experts, and existing techniques like Bayesian optimization fail with multiple process parameters.
Innovation Solution
A deep learning-based method using a multilayer perceptron (MLP) model to learn relationships among operating parameters, quality indicators, and constraints, applying gradient descent techniques to predict optimal controllable parameter values, ensuring gradual changes and adherence to constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing techniques like Bayesian optimization are used, then initial settings can be found through random experiments, but they fail to work with multiple process parameters and cannot improve quality beyond historical data
Solution Approach 1:
The patent transforms the quality maximization problem into a cost minimization problem by inverting the quality function. This parameter transformation enables the use of gradient-based optimization techniques that can efficiently handle multiple process parameters, overcoming the limitation of existing techniques that fail with multi-parameter optimization.
Solution Approach 2:
The patent replaces random experimentation and manual domain expert approaches with a data-driven gradient-based optimization system. By using historical data to train a predictive model and applying gradient descent algorithms, the system automatically identifies optimal parameter configurations without relying on random searches or expert knowledge.
2Manufacturing precision
If gradient-based techniques are used to improve product quality beyond historical data, then optimization efficiency improves, but they do not perform efficiently when requirements exceed historical data ranges
Solution Approach 1:
The patent performs preliminary action by training a predictive model on historical data before the optimization process. This pre-trained model establishes the relationship between process parameters and quality metrics, enabling the gradient-based optimization to reliably guide parameter adjustments even when targeting quality levels beyond the range of historical data.
Solution Approach 2:
The patent introduces a predictive model as an intermediary between the process parameters and quality outcomes. This intermediary component, trained on historical data, allows the optimization algorithm to reliably predict quality improvements beyond the historical range without directly observing those outcomes, thus extending the reliable operating range.
3Ease of operation
If manual experimentation and domain expert knowledge are used, then initial process settings can be established, but the process cannot consistently produce golden batches under changing environments
Solution Approach 1:
The patent implements self-service by creating an automated optimization system that uses historical data to continuously identify optimal parameter settings. The system automatically adapts to changing environmental conditions by re-running the optimization process with updated data, eliminating the need for continuous manual intervention and maintaining consistent quality without relying on domain experts.
Solution Approach 2:
The patent incorporates feedback mechanisms by using historical batch data that includes actual quality outcomes. The optimization process learns from past performance and continuously refines parameter recommendations, creating a closed-loop system that adapts to changing conditions and maintains quality consistency without manual intervention.
4Manufacturing precision
If random experiments are performed to find initial settings, then some baseline quality can be achieved, but the process cannot systematically improve beyond historical best
Solution Approach 1:
The patent replaces random experimentation with a systematic gradient-based optimization approach. By using the trained predictive model to guide parameter adjustments in the direction of steepest ascent for quality improvement, the system efficiently and systematically identifies optimal settings without relying on random chance, dramatically improving productivity in the quality improvement process.
Data Source
AI summary
The embodiments of present disclosure herein address unresolved problem of getting optimum quality of product while changing operating conditions and raw material frequently in an industrial manufacturing process. The disclosure herein generally relates to a deep learning based approach for a multi-objective constrained optimization. Embodiments provide a method and system for recommending optimum configurations in industrial control systems for improving quality of product. The system is configured to automate improvement over existing golden batch with a data driven approach and replace need of random experimentation with very minimal systematic experiments. The system ensures that no constraint violations are made, and the system remains stable even when data is noisy. Further, the system recommends values of parameters so that improvement in quality is achieved. The changes in parameter value should be gradual even if the historical data received from feedback is noisy.