Precision optimization of femtosecond laser micromachining for endovascular neural interfaces based on RSM and machine learning

Le résumé fourni par la source

Abstract Three-dimensional laser micro-structuring of nitinol alloy tubes presents a promising technique for cost-effective production of vascular stents and random forest endovascular neural interfaces. The dual-functionality of this approach enables both vascular scaffold fabrication through precision laser ablation and conductive circuit patterning for cerebral electrode development. Given the sub-millimeter dimensional tolerances and biocompatibility requirements for vascular implants, advanced laser processing presents unique challenges in thermal management and surface integrity preservation. Ultrafast laser systems, particularly femtosecond pulses, have emerged as the preferred modality due to their non-thermal interaction mechanisms and sub-micron ablation precision. This study establishes a hybrid optimization framework combining response surface methodology with machine learning to systematically investigate the process-parameter relationship for 3D laser micromachining of nitinol alloys. Our experimental design quantifies the interdependencies between the key parameters in processing 0.1 mm thick sheet and tubular specimens. The integrated modeling approach demonstrates the optimized parameter matrices for enhanced dimensional accuracy while maintaining surface cleanness.

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