The track ‘Artificial Intelligence in Laser Processing’ at ICALEO 2024 explores the fusion of AI and laser manufacturing. Discover cutting-edge research on optimizing laser processes and enhancing manufacturing efficiency. Explore AI's diverse applications in parameter optimization, quality prediction, real-time process monitoring, and control. Join us to unlock the potential of AI-driven solutions revolutionizing laser materials processing, delivering unprecedented precision, productivity, and innovation.
Session Search
Day | Session | Title | Keywords |
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Monday | Process Monitoring and Control | Velocity-Based Closed-Loop Control in Fusion Laser Cutting for Multi-Directional and Curved Geometries |
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Monday | Process Monitoring and Control | Real-Time Analysis of Inline Sensor Data During USP-Laser Machining |
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Monday | Process Monitoring and Control | Optimized Closed-Loop Real-Time Speed Regulation With Loss-of Cut Active Monitoring in Laser Oxidation Cutting |
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Monday | Process Monitoring and Optimization | AI-Driven Optimization of Laser Cutting Parameters: Automating Experimentation for Enhanced Precision |
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Monday | Process Monitoring and Optimization | AI-Based Spatially Resolved Parameter Prediction in Laser Metal Deposition for Increased Process Stability |
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Monday | Process Monitoring and Optimization | Vision Transformer Based Cut Interruption Detection and Prediction of Laser Fusion Cutting from Monitored Melt Pool Images |
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Monday | Process Monitoring and Optimization | Analyzing Multi-Spectral Emission and Synchrotron Data to Evaluate the Quality of Laser Welds on Copper |
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Monday | Process Monitoring and Optimization | Artificial Intelligence in Camera-based Online Process Monitoring |
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Monday | Process Monitoring I | Photodiode-Based Focus Monitoring in Ultrashort-Pulsed Laser Structuring of Graphite Anodes for Lithium-Ion Batteries |
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Monday | Process Monitoring I | DNN-Based Real-Time Monitoring of Weld Shape during the Laser Welding Process |
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Monday | Process Monitoring I | Event-Based Vision in Laser Welding: An Approach for Process Monitoring |
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Monday | Process Monitoring I | Title - TBD | |
Tuesday | Model Based Prediction | Application of a Physics-Informed Hybrid Model with Additional Output Constraints for the Prediction of the Threshold of Deep-Penetration Laser Welding |
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Tuesday | Model Based Prediction | AI-Supported prediction of femtosecond laser micromachining parameters |
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Tuesday | Model Based Prediction | Generative adversarial network (GAN)-based Prediction of Thermal Residual Stress and Melt Pool Characteristics in Laser Processing of Carbon Steel |
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Tuesday | Model Based Prediction | Autonomous Micro Laser Processing with Artificial Intelligence |
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Tuesday | Poster Gallery | Full Penetration Detection in Fiber Laser Remote Cutting of Electrical Steel Sheets from In-Situ Monitored Images using Convolutional Neural Networks |
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Tuesday | Poster Gallery | Acoustic Process Monitoring During the Laser Beam Welding of Stainless-Steel Foils Using an Adjustable Ring Mode Laser Beam Source |
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