Injection Moulding Parameter Processing with CAE Feedback
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Solution Overview
Problem
Existing methods for determining molding parameters for injection molding presses rely heavily on operator experience and manual iteration, leading to inefficiencies and variability in results due to the inability of CAE simulations to account for all complex molding phenomena.
Innovation Solution
A method involving CAE simulations, data processing, and machine learning algorithms to automatically generate and optimize molding parameters by comparing virtual and actual molding data, creating a database for efficient retrieval and iteration, thereby reducing operator dependence and improving precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If CAE simulation is used to generate initial molding parameters, then preparation time is reduced, but manufacturing precision deteriorates due to inability to account for all complex molding phenomena
Solution Approach 1:
The system implements feedback by comparing CAE simulation results with actual molding test results, automatically adjusting and refining the molding parameters through iterative optimization. The computer automatically compares simulated data with actual test data, identifies discrepancies, and generates optimized parameters for subsequent molding operations, creating a closed-loop feedback system that continuously improves precision.
Solution Approach 2:
The invention replaces the manual mechanical process of parameter adjustment with automated computer-based processing. Instead of operators manually analyzing simulation results and adjusting parameters based on experience, the system uses automated data processing, comparison algorithms, and optimization software to generate precise molding parameters, substituting human manual work with computational automation.
2Manufacturing precision
If manual parameter setting based on operator experience is used, then manufacturing precision can be achieved, but productivity deteriorates due to large number of molding tests and long setting period
Solution Approach 1:
The system enables self-service by allowing the computer to automatically perform parameter optimization without continuous human intervention. The automated system independently compares simulation and test data, identifies optimal parameters, and generates molding instructions, reducing dependence on operator experience and enabling rapid parameter determination through autonomous computational processing.
Solution Approach 2:
The invention applies preliminary action by performing CAE simulations and automated parameter optimization before actual production molding. The system pre-determines optimal molding parameters through virtual simulation and automated refinement, so that when production begins, the parameters are already optimized, eliminating the need for time-consuming trial-and-error molding tests during production setup.
3Ease of operation
If arbitrary initial parameters are chosen for CAE simulation, then ease of operation is improved, but manufacturing precision deteriorates because initial parameters are far from optimal requiring many iterations
Solution Approach 1:
The system uses feedback to automatically refine arbitrary initial parameters through iterative comparison with actual test results. Even when starting with simple arbitrary parameters, the automated feedback loop continuously adjusts and optimizes them based on discrepancies between simulation and reality, ensuring rapid convergence to optimal parameters without requiring manual expertise in initial parameter selection.
Data Source
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AI summary
A method is described for processing moulding parameters (Pi+i) for an injection moulding machine (10) obtained by CAE. The CAE simulation generates simulation results (Ai), first machine parameters (Pi) are generated by electronically processing the simulation results (Ai), second machine parameters (Pi+i) are obtained, different from the first ones, from the execution of another moulding process for the same object; and in an electronic database (M) accessible by a user the first and second machine parameters are saved associating them in a common collection. In a further variation, the last method step is replaced by processing the first and second machine parameters with a software, and modifying the machine parameters calculated with a subsequent CAE simulation as a function of the processing produced by said software.