Researchers and RTOs working on Artificial Intelligence (AI), Machine Learning (ML), Neural Networks (NN) for laser surface texturing and functionalization. Developments on the relationship between laser processing parameters, materials properties, surface strcutures, and the surface properties (functionalization).
Open to discuss the subject and foster partnerships !!!!!
Have a look at https://www.lasersystemseurope.com/feature/artificial-intelligence-optimises-laser-processing
https://www.trumpf.com/en_INT/newsroom/stories/brain-power-unleashed-ai-in-laser-material-processing/
Article Recent Progress and Scientific Challenges in Multi-material ...
Dear Alexandre Cunha,
We are currently developing a laser-based indentation method to determine material properties in high throughput. Amongst others, we used Neural Networks to correlate our measured characerisitc values with material properties, such as hardness and tensile strength. One of your findings (in collaboration with an institute) are presented here: (doi: 10.1063/1.5112634). Further, we want to use machine learning to identify characerisitics from our indentation profiles, which correlate with mechanical material properties.
Moreover, we are also doing research on laser-based surface functionalization, e.g. to improve subsequent forming operations (friction or residual stress) or to create of antibacterial surfaces. In some of our recent proposals, we added specific tasks to improve the functionalization by means of machine learning.
Let me know if you are interested. I am looking forward to hearing from you.
Best regards,
Tobias Valentino
Take a look at my papers related to the applications of Artificial Intelligence in laser material processing.
Article A review on applications of artificial intelligence in model...
Article Feasibility of artificial neural network on modeling laser-i...
Hi Alexandre,
Have a look at the following article:
Article Artificial neural network tools for predicting the functiona...
Dear Alexandre Cunha,
Have a look at the following articles:
Article Comparative evaluation of supervised machine learning algori...
Article Predicting the ultimate tensile strength of AISI 1045 steel ...
Our research currently utilizes artificial intelligence for laser based processing to understand the surface integrity of cutting tool materials, to improve tribological performance for improved machining performance and potential repair applications. Using defect detection and prediction to understand the relationship between laser parameters + workpiece materials properties on the laser induced surface integrity.
The following article is an example of this, it uses a hybrid method:
- Machine learning (region convolutional neural network) is used for defect detection and the result of this, is used to generate a dataset to train a defect prediction network (back propagation neural network).
- This result accurately estimates individual and multiple parameters on the laser induced surface integrity and underlying material science behavior, enabling the optimization of parameters in machining applications. Enabling a continuous learning system.
Article Surface defect detection and prediction in carbide cutting t...
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