Production Plant Control With ML Prediction for Quality Compliance
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Solution Overview
Problem
Existing production processes in industrial plants, particularly those manufacturing metal products, suffer from non-compliance with technological specifications, leading to defects and inefficiencies due to manual and static control methods, lacking cross-plant optimization, and inadequate automation of quality evaluation.
Innovation Solution
A system and method that integrates independent plant automation systems across multiple components, utilizing a prediction model generated by machine learning to optimize production processes, incorporating sensor and actuator data for real-time adjustments, and a central data storage to facilitate cross-plant communication and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual and static control methods are used for production processes, then operational simplicity is maintained, but product quality compliance and manufacturing precision deteriorate
Solution Approach 1:
The patent replaces manual mechanical control with an automated control system that uses machine learning models and algorithms to dynamically adjust production parameters. The system substitutes human-operated mechanical controls with computer-based automation that processes sensor data and generates control commands, thereby improving manufacturing precision while managing complexity through software-based solutions.
Solution Approach 2:
The system dynamically changes production parameters based on real-time sensor data and machine learning predictions. Instead of using fixed static parameters, the control system continuously adjusts process parameters (temperature, pressure, speed, etc.) to optimize product quality compliance, transforming the control approach from static to adaptive parameter management.
2Productivity
If independent plant automation systems are used for each plant component, then system simplicity and ease of operation are maintained, but cross-plant optimization and information exchange deteriorate
Solution Approach 1:
The patent merges independent plant automation systems into an integrated control architecture where multiple plant components communicate through a centralized control system. The machine learning model aggregates data from various plant components and coordinates their operation, enabling cross-plant optimization while managing integration complexity through a unified control framework.
Solution Approach 2:
The control system is designed with universal functionality to manage multiple plant components through a single integrated platform. The machine learning model serves multiple purposes: predicting quality outcomes, optimizing process parameters, and coordinating operations across different plant components, thereby achieving cross-plant optimization without proportionally increasing system complexity.
3Adaptability or versatility
If static recipes are used for production processes, then operational simplicity is maintained, but adaptability to changing conditions and product quality deteriorate
Solution Approach 1:
The patent transforms static production recipes into dynamic control processes that adapt to changing conditions in real-time. The machine learning model continuously receives sensor data from the production process and dynamically adjusts control parameters accordingly, enabling the system to respond to variations in raw materials, equipment state, and environmental conditions while maintaining product quality.
Solution Approach 2:
The system implements feedback loops where sensor measurements from the production process are fed back to the machine learning model, which then adjusts control parameters to maintain optimal performance. This closed-loop control enables adaptability to changing conditions by continuously monitoring process state and making real-time adjustments, transforming static recipes into responsive adaptive control systems.
Data Source
Figure 1
AI summary
The invention relates to a system (1) and method for controlling a production plant (2) consisting of a plurality of plant parts (3), in particular a production plant (2) for producing industrial goods such as metal semi-finished products, comprising: a plant automation unit (4) for monitoring and open-loop and/or closed-loop control of the production process within the production plant (2); a production planning system (5) having information concerning the products to be produced in the production plant (2); a model generator (6) for generating at least one prediction model (7) for the product currently being produced in the production plant (2) and/or for the products to be produced in the future, the model generator (6) taking account of the results of the monitoring of the production plant (2) when generating the at least one prediction model (7); a production optimiser (8) for determining an optimised production process within the production plant (2) on the basis of the data from the plant automation unit (4), the production planning system (5) and the prediction model (7) generated by the model generator (6), the production optimiser (8) taking account of the production-related specifications of the individual plant parts (3); and a production plant control unit (9) for generating target specifications for the plant automation unit (4) on the basis of the optimised production process determined by the production optimiser (8).