Adaptive Laser Beam Engineering with Coherent Beam Combining for Efficient Power Delivery

TL;DR

This paper presents a versatile coherent beam combining framework for adaptive laser beam shaping, enabling precise, high-efficiency power delivery with rapid reconfiguration for industrial and scientific applications.

Contribution

It introduces a comprehensive CBC-based method integrating dynamic and static beam shaping with deep learning optimization, enhancing scalability and performance over existing techniques.

Findings

Improved beam uniformity and power efficiency.

Enables rapid, reconfigurable beam shaping without mechanical parts.

Scalable framework suitable for industrial laser systems.

Abstract

High-power laser technologies are essential in precision manufacturing, defense, and scientific research, where accurate control of the beam profile is paramount. Although several beam-shaping methods exist, they often face implementation and scalability challenges. To address these limitations, we introduce a comprehensive and versatile framework for on-demand beam engineering through coherent beam combining (CBC) systems to precisely craft far-field intensity distributions. The proposed approach integrates limitless key capabilities: (i) dynamic beam shaping through sequential steering, (ii) structured static beam shaping allowing the direct formation of target-defined profiles, and (iii) high-speed dynamic beam sequencing without mechanical movement. Thus, the proposed approach could be a potential one-stop solution to meet wide manufacturing requirements. Rapid reconfiguration is achieved through optimized phase control of the CBC channels, supported by a deep-learning-inspired optimization algorithm. This unified CBC framework significantly improves beam uniformity, power delivery efficiency, and scalability compared to conventional techniques, thus establishing a robust platform for next-generation laser systems in industrial manufacturing, materials processing, and directed-energy systems.