AI Process Control for Plastics Machines With Material Variability
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Solution Overview
Problem
The increasing complexity of plastics processing, combined with a shortage of skilled labor and the use of recycled materials with fluctuating properties, poses challenges for achieving high-quality component production, as rule-based methods struggle to adapt effectively to these changes.
Innovation Solution
A computer-implemented method utilizing artificial intelligence (AI) to control plastics processing machines, involving simulation datasets, process images, and a Design of Experiments (DoE) matrix to optimize process parameters, coupled with a software communication robot (chatbot) for interactive operator feedback and machine learning-based optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based methods are used to control plastics processing machines, then the control logic is simple and easy to implement, but the system cannot effectively adapt to fluctuating material properties and complex production requirements
Solution Approach 1:
The system enables self-service by allowing the control system to automatically adapt to material fluctuations through AI-based analysis of process data and simulation results. The machine autonomously adjusts process parameters without requiring expert intervention, transforming the control system from a static rule-based approach to a self-learning, self-adjusting system that continuously optimizes production based on real-time conditions
Solution Approach 2:
The system applies preliminary action by pre-calculating optimal process parameters through simulations before actual production. The AI model uses historical data and material properties to predict the best processing conditions in advance, allowing the system to be pre-configured for specific materials and reducing the need for complex real-time adjustments during operation
2Manufacturing precision
If expert operators manually adjust process parameters to handle material fluctuations, then production quality can be maintained, but the system requires highly skilled labor which is in short supply
Solution Approach 1:
The system replaces the mechanical system of expert human judgment and manual adjustment with an AI-based digital control system. The AI model analyzes process data, material properties, and simulation results to automatically determine optimal parameters, substituting the need for expert human operators with an automated intelligent system that maintains high manufacturing precision while reducing skill requirements
Solution Approach 2:
The system implements continuous feedback by monitoring actual process parameters and component quality, comparing them against target values, and automatically adjusting process settings. The AI model learns from feedback loops, using real-time data from sensors and quality measurements to refine its predictions and maintain consistent manufacturing precision without requiring constant expert intervention
3Adaptability or versatility
If complex simulations and AI models are used to optimize processes, then adaptability to material changes improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs complex simulations and AI model training in advance, before actual production runs. By pre-calculating optimal processes for different material types and conditions, the system reduces the computational burden during real-time operation to simple lookups and minor adjustments, maintaining high adaptability while managing computational complexity through advance preparation
Solution Approach 2:
The system segments the control process into distinct phases: offline simulation and model training, online data collection and analysis, and real-time parameter adjustment. This segmentation allows computationally intensive tasks to be performed separately during maintenance or setup periods, while the production system itself remains relatively simple and responsive
Data Source
AI summary
A method for controlling processes on at least one plastics-processing machine. The method comprises the following steps:-performing a simulation so as to produce at least one component with generation of simulation datasets (SD) that relate to an outline of the component and/or material properties,-determining a process image so as to operate the machine at an idealized operating point based on the simulation datasets (SD),-generating a design of experiments (DoE) matrix,-iteratively simulating the DoE matrix with computing of remaining variations of process parameters while reducing the DoE matrix and obtaining a trained process model for the machine, using which a component is able to be produced on the machine,-verifying the remaining variations of process parameters through real tests (40), in which components are produced on the machine and assessed, so as to generate a process parameter dataset (PPD) for subsequent operation of the machine at an operating point (AP), by virtue of an operator communicating interactively with a software communication robot, in particular chatbot, and the method steps comprise at least two artificial intelligences that interact