Production Plant Control With Prediction Models for Product Quality
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Solution Overview
Problem
Existing production systems for industrial goods, particularly metallic semi-finished products, struggle with non-compliance of technological target specifications, leading to defects and inefficiencies due to manual evaluation and lack of automated coupling between production parameters and quality assessments.
Innovation Solution
A system comprising a plant automation unit, production planning system, model generator, and production optimizer that generates prediction models and optimizes production processes across multiple plant parts, using sensors, actuators, and machine learning to ensure compliance with target criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual evaluation and static target specifications are used, then device complexity is reduced, but manufacturing precision and product quality deteriorate
Solution Approach 1:
The system implements automated feedback loops where production parameters are continuously monitored, evaluated against target specifications, and used to dynamically adjust control settings. This closed-loop control ensures manufacturing precision is maintained through real-time corrections based on actual production data.
Solution Approach 2:
The patent transforms static target specifications into dynamic, adaptable parameters. The system continuously learns from production data and automatically adjusts target values and control parameters to optimize product quality, replacing fixed manual settings with adaptive automated control.
2Manufacturing precision
If automated monitoring and prediction models are implemented, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces intermediate layers including prediction models, evaluation modules, and optimization algorithms that mediate between raw sensor data and control actions. These intermediaries process and interpret data, transforming complex information into actionable control decisions that improve precision without overwhelming the control system.
Solution Approach 2:
The control system is divided into modular functional components: data acquisition modules, prediction model generators, evaluation units, and control execution modules. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high manufacturing precision.
3Productivity
If dynamic optimization is implemented, then productivity is improved, but loss of time for model generation and optimization increases
Solution Approach 1:
The system performs preliminary actions by pre-generating prediction models and optimizing target specifications before actual production occurs. Historical data is used to create initial models and determine optimal parameters in advance, so that during production, the system can quickly adjust without time-consuming real-time calculations.
Solution Approach 2:
The patent utilizes parameter changes by adjusting model complexity and optimization depth based on production conditions. During high-speed production, simplified models with fewer parameters are used for quick adjustments, while during setup or low-volume production, more comprehensive models are generated for optimal precision.
Data Source
AI summary
A system for controlling a production plant includes a plant automation unit for monitoring and control of the production process within the production plant. A production planning system has information concerning the products to be produced. A model generator generates at least one prediction model for products produced in the production plant. The model generator takes into account the results of the monitoring of the production plant when generating the at least one prediction model. A production optimizer determines an optimized production process within the production plant on the basis of data from the plant automation unit, the production planning system, and the prediction model generated by the model generator. The production optimizer takes into account the production-related specifications of the individual plant parts. A production plant control unit generates target specifications for the plant automation unit on the basis of the optimized production process determined by the production optimizer.