Summary of Deepforge: Leveraging Ai For Microstructural Control in Metal Forming Via Model Predictive Control, by Jan Petrik and Markus Bambach
DeepForge: Leveraging AI for Microstructural Control in Metal Forming via Model Predictive Control
by Jan Petrik, Markus Bambach
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study introduces a novel method for microstructure control in closed die hot forging by combining Model Predictive Control (MPC) with a machine learning model called DeepForge. The latter uses a unique architecture that combines 1D convolutional neural networks and gated recurrent units to predict microstructure changes during forging, leveraging surface temperature measurements of the workpiece as input. The study also details the finite element simulation model used to generate the dataset, featuring a three-stroke forging process. Results demonstrate DeepForge’s ability to accurately predict microstructure with a mean absolute error of 0.4±0.3%. Furthermore, the paper explores MPC’s application in adjusting inter-stroke wait times, effectively mitigating temperature disturbances to achieve a target grain size of less than 35 microns within a specific 2D region of the workpiece. The study verifies its findings experimentally, showcasing significant progress towards improved control and quality in forging processes where temperature can be used as an additional degree of freedom. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to control what happens inside metal during hot forging by combining two powerful tools: Model Predictive Control (MPC) and a machine learning model called DeepForge. DeepForge uses special computer algorithms to predict how the metal will change based on its temperature, which is measured as it’s being shaped. The team also created a digital simulation of the process to train their model. They tested this approach by comparing their predictions with real-world results and found that they were very accurate, with only a small amount of error. This means that manufacturers can use this new method to make metal products that are stronger and more precise. |
Keywords
* Artificial intelligence * Machine learning * Temperature
