Artificial intelligence meets laser technology: A review of recent advances

Abstract

Laser technology has revolutionized various industries, offering precision, efficiency, and versatility in applications ranging from manufacturing to medicine. However, the complex interplay between laser parameters, material properties, and process outcomes often presents challenges in achieving optimal results. Machine learning (ML) emerges as a powerful tool to address these complexities, providing data-driven insights and predictive capabilities. This paper explores the integration of ML techniques into laser processes, focusing on applications in manufacturing, materials processing, and biomedical engineering. It examines the benefits of ML, such as enhanced process control, improved quality, and reduced costs. Additionally, it discusses the challenges and considerations involved in implementing ML in laser applications, including data collection, feature engineering, model selection, and validation. Case studies are presented to illustrate the successful application of ML in diverse laser-based tasks, highlighting the potential of this technology to drive innovation and advancements in various fields. • Exploring ML's role in laser technology to boost precision and efficiency in manufacturing and science. • It highlights difficulties in improving laser processes due to the complex interplay of parameters, material properties, and outcomes. • The paper examines ML advantages (control, quality, cost) alongside data and model validation challenges. • Case studies highlight ML's success in laser tasks, demonstrating its potential for wide innovation.

Ähnliche Arbeiten