Laser-based manufacturing and materials processing technology has proven to be a well-established and cutting-edge technology with remarkable growth across a wide range of industries. A few examples of processing methods include laser additive manufacturing, laser cladding, laser cleaning, laser hardening, laser glazing, laser alloying, laser color marking, laser corrosion removal, laser surface texturing, laser shaping, laser drilling, laser milling, laser welding, laser brazing, laser polishing, laser surface modification, etc. Current research is being carried out to investigate various facets of laser-based manufacturing and materials processing for metals, alloys, composites, polymers, and glass. These aspects include, but are not limited to, fabrication and laser processing experimental setups, laser-materials interaction, microstructural changes, materials characterization, surface analysis, corrosion & tribology, process optimization, and numerical analysis and simulations.
Laser materials processing is accompanied by challenges such as the high reflectivity of materials, incompatibility issues with certain materials, defect formation, phase instability, and burnout problems with plastics and composite processing. The process is extremely fast, and precise control of heat input with real-time adjustments requires process monitoring and control. A multifaceted strategy is necessary to address these issues, including advancements in laser technology, process optimization, and a deeper understanding of material interactions. The integration of artificial intelligence also enhances the autonomous functioning and intelligent decision-making capabilities of laser systems. Moreover, merging laser technology with Industry 4.0 concepts enables automation, data-driven decision-making, AI-enhanced in-situ Monitoring and control, and smart production. In manufacturing processes, laser monitoring systems offer real-time feedback for quality assurance and process control. Data-driven approaches, including those utilizing digital twins, IoT, cloud computing, AI/ML techniques, and data analytics, can aid in asset twin monitoring.
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