Digital Twin Production Modeling With ML Feedback Optimization
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Solution Overview
Problem
Conventional simulation packages lack the ability to perform enhanced analytics, such as machine learning, and cannot automatically incorporate actual process parameters, limiting their capability to optimize production processes effectively.
Innovation Solution
A machine learning system integrating a production planning system, a simulation system with a digital model, and an analytics system using an API to iteratively optimize production processes by analyzing real-time data and adjusting input parameters, enabling automated optimization of production processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional simulation packages are used, then simulation capability is provided, but machine learning and enhanced analytics cannot be performed
Solution Approach 1:
The patent combines conventional simulation packages with machine learning algorithms into an integrated system. The simulation package executes simulations while the machine learning component performs enhanced analytics on the simulation outputs, allowing both simulation and advanced analytics capabilities to coexist in a unified platform without requiring separate systems.
Solution Approach 2:
The patent introduces an intermediary layer that connects the simulation package with machine learning algorithms. This intermediary enables the simulation outputs to be automatically fed into machine learning models for enhanced analytics, bridging the gap between traditional simulation and advanced analytics without direct integration complexity.
2Extent of automation
If conventional simulation packages are used, then simulation execution is possible, but automated iterative optimization with machine learning cannot be achieved
Solution Approach 1:
The patent implements a feedback loop where machine learning algorithms analyze simulation outputs and automatically generate optimized input parameters, which are then fed back into the simulation package for further iterations. This automated feedback mechanism eliminates manual input requirements and enables continuous iterative optimization without human intervention.
Solution Approach 2:
The patent performs preliminary actions by pre-configuring the integration between simulation packages and machine learning algorithms, establishing automated data flow and parameter transmission mechanisms in advance. This preliminary setup enables subsequent automated iterative optimization to proceed without manual configuration during execution.
3Manufacturing precision
If conventional simulation packages are used, then theoretical simulation results can be obtained, but actual process parameter optimization is limited
Solution Approach 1:
The patent utilizes parameter changes by allowing the machine learning algorithms to dynamically adjust simulation input parameters based on actual process data and optimization objectives. This enables the system to explore different parameter configurations and identify optimal settings that reflect real-world manufacturing conditions, thereby improving process optimization accuracy.
Data Source
AI summary
A machine learning system and method for optimizing a production process. For instance, the method includes several steps as follows: selecting different values for a plurality of input parameters of a digital model of the production process for simulation; running the digital model using the different values for the plurality of input parameters and at least some of real-time data of the production process; determining a plurality of output parameters of the digital model; analyzing the plurality of output parameters; learning an optimized plurality of input parameters corresponding to the plurality of output parameters; and programming the production process to use the optimized plurality of input parameters to run the production process.