Material Board Production Control With AI Quality Prediction
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Solution Overview
Problem
Current methods for producing material boards face challenges in efficiently controlling and optimizing quality parameters during production, leading to delays in quality assessment and increased risk of rejects due to reliance on manual adjustments and physical models that are complex and difficult to reproduce, especially when dealing with multiple variables.
Innovation Solution
A method utilizing an algorithm based on artificial intelligence that processes input parameters such as product, installation, and material parameters to predict quality values in real-time, reducing the need for laboratory cuts and allowing for rapid optimization of production settings, enabling quick changes between product types and minimizing rejects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical models and manual optimization methods are used to control production parameters, then quality assessment can be performed, but the process requires long dead time for laboratory analysis and increases the risk of rejects
Solution Approach 1:
The patent replaces physical measurement systems (laboratory cuts and manual analysis) with a cybernetic system using artificial neural networks. The neural network processes production parameters in real-time to predict quality outcomes, eliminating the need for physical laboratory testing and reducing dead time from hours/days to minutes.
Solution Approach 2:
The patent creates a virtual model (neural network) that replicates the quality assessment function without requiring physical copies of the material board. The neural network learns from historical data and produces quality predictions that replace the need for actual laboratory measurements.
2Manufacturing precision
If multiple production parameters are optimized experimentally based on experience, then quality can be improved, but the complexity of modeling increases exponentially with the number of parameters
Solution Approach 1:
The patent transforms the complex multi-parameter optimization problem into a different parameter space by training the neural network on historical data. Instead of manually modeling relationships between multiple parameters, the neural network learns optimal parameter combinations automatically, reducing modeling complexity while maintaining manufacturing precision.
Solution Approach 2:
The patent combines multiple data sources (production parameters, quality measurements, environmental conditions) into a composite neural network model. This integrated approach handles multiple parameters simultaneously without exponential complexity increase, as the neural network processes all inputs through parallel processing pathways.
3Measurement precision
If laboratory cuts are used to determine quality data, then accurate quality measurement can be obtained, but the process delays production and increases the risk of producing rejects
Solution Approach 1:
The patent replaces the mechanical laboratory cutting and testing process with a computational neural network system. The neural network receives production parameters electronically and instantly predicts quality metrics, eliminating the time-consuming physical testing process while maintaining measurement accuracy.
Solution Approach 2:
The neural network performs quality assessment in advance, before the material board actually leaves the production line. By predicting quality based on production parameters, the system enables real-time adjustments to prevent rejects before they occur, rather than detecting them after production.
Data Source
AI summary
A method for producing material boards in a production plant in which apparatuses form a material into a mat that is pressed to obtain the material board which has specific quality parameters. The production plant and/or the apparatuses are controlled in an open- or closed-loop manner by a controller, which preferably includes a programmable logic controller, and input parameters are received, processed and/or output by the controller. The input parameters are formed at least from settable product parameters for the material board to be produced, from settable and/or recorded plant parameters of the production plant and/or the apparatuses and/or from recorded material parameters. A quality value of at least one quality parameter of the material board to be produced is determined based on the input parameters by an algorithm based on artificial intelligence. The algorithm is trained or formed by a database which has at least one quality parameter and input parameters correlating with the quality parameter.