Smart Additive Manufacturing of Advanced Materials Using Artificial Intelligence and Machine Learning

The field of additive manufacturing is undergoing a profound transformation as artificial intelligence (AI) and machine learning (ML) become integral to the design, monitoring, and fabrication of complex, high-performance components from advanced materials. Traditional additive manufacturing methods have enabled unprecedented geometric freedom and customization, but challenges such as process instability, microstructural variability, and inconsistent quality still limit their widespread adoption in critical sectors. Recent advances show that integrating AI and ML with additive manufacturing can enable adaptive control, real-time defect detection, and process optimization—fundamentally changing how advanced materials and lightweight, high-performance structures are developed. However, there remain key questions regarding the reliability, reproducibility, and scalability of these intelligent manufacturing systems, especially as they transition from laboratory settings to industrial environments.Recent studies highlight the promise of data-driven models, digital twins, and physics-informed machine learning in capturing intricate process–structure–property relationships and facilitating autonomous decision-making during manufacturing. AI/ML platforms are now used for in-situ monitoring, closed-loop process control, and predictive quality assurance—factors that are crucial for applications ranging from biomedical implants to aerospace components and energy systems. Despite this progress, further work is needed to overcome ongoing challenges such as residual stresses, porosity, and the lack of real-time, high-resolution monitoring at industrial scale. In addition, standardization, robust validation protocols, and integration of smart systems with diverse material classes remain open research questions.This Research Topic aims to advance the field of smart additive manufacturing by focusing on the convergence of advanced materials processing and AI/ML-enabled technologies. The goal is to develop and demonstrate frameworks that provide robust, adaptive, and autonomous solutions to address current limitations in quality, reproducibility, and efficiency. Central objectives include establishing process–structure–property linkages, deploying real-time monitoring and control, and creating scalable, sustainable manufacturing practices. By bringing together multidisciplinary perspectives from materials science, artificial intelligence, machine learning, process engineering, and application domains, this Research Topic seeks to accelerate the adoption of intelligent additive manufacturing across high-value industries and biomedical engineering.To generate new insights and address existing gaps, we invite contributions within the following scope: Original research and reviews targeting, but not limited to:• Development and processing of advanced materials for additive manufacturing, including lightweight structures and multifunctional systems• Integration of artificial intelligence and machine learning for real-time process monitoring, in-situ control, and defect prediction• Physics-informed and data-driven modeling of process–structure–property relationships• Digital twin approaches for additive manufacturing optimization and quality assurance• Closed-loop, autonomous manufacturing systems for reproducible and scalable production• Applications of smart additive manufacturing in aerospace, biomedical, energy, and next-generation engineering• Standardization, validation protocols, and industrial implementation of intelligent additive manufacturing frameworksAppendix:This Research Topic welcomes original research articles, comprehensive reviews, and case studies. Topic editors: Pradyumn Kumar Arya, Atefeh Golbang, Sagar Nikam, Mayur Sawant Source: https://www.frontiersin.org/research-topics/76716/smart-additive-manufacturing-of-advanced-materials-using-artificial-intelligence-and-machine-learning