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@article{171520,
        author = {Jhanavi Trilok},
        title = {Literature Review: Recent Advances in Laser Welding Technologies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {488-500},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171520},
        abstract = {This literature review examines recent advancements in laser welding technologies, focusing on innovative methods for monitoring and controlling critical aspects of the welding process. The review covers various approaches, including machine learning techniques combined with spectrometer measurements for beam offset detection, acoustic emission monitoring paired with deep learning models for penetration assessment, and Optical Coherence Tomography (OCT) integrated with fuzzy control systems for inline weld depth evaluation. Each study emphasizes the importance of real-time detection and adjustment capabilities to enhance welding quality and efficiency. The findings reveal that these advanced methodologies significantly improve the precision and reliability of laser welding processes across different applications, contributing to reduced defects, lower scrap rates, and heightened process efficiency. By addressing traditional limitations in monitoring and control, these innovative solutions pave the way for more reliable and automated welding practices in modern manufacturing environments.},
        keywords = {Laser Welding, Beam Offset Detection, Machine Learning, Spectrometer Measurements, Acoustic Emission, Deep Learning, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Optical Coherence Tomography (OCT), Inline Measurement, Weld Depth Evaluation, Fuzzy Control Systems, Real-Time Monitoring, Weld Quality, Process Optimization, Manufacturing Efficiency.},
        month = {January},
        }