Achieving Cognitive Intelligence for Sustainable Advanced Manufacturing

Sustainable manufacturing is a global imperative, requiring the convergence of materials, manufacturing processes, and engineering design. Manufacturing plays a vital role in the global economy; however, it contributes detrimentally to 20% of carbon emissions. Laser-based advanced manufacturing, primarily additive manufacturing (AM), holds promise for sustainable production despite challenges like cost-effectiveness, energy efficiency, and process stability. The challenge lies in effectively studying critical parameters and incorporating new materials, sensing technologies, advanced modeling, and prediction methods into the manufacturing process’s waste-free and defect-free monitoring and optimization. This research focuses on developing research recommendations in three areas. First, (i) justifying the importance of developing Free and Open-Source Hardware (FOSH) laser-based AM infrastructure to facilitate research exploration, data-sharing, and open research. Second, (ii) exploring the concepts and theories towards integrated data-driven “perceptual intelligence,” model-driven “computational intelligence,” and integrated artificial intelligence methods to achieve cognitive capabilities with explainability and generalizability. Third, (iii) one case study on melt pool monitoring in metal-based AM showcases novel capabilities and limitations in real-time process monitoring using data-driven modeling. Regardless of the complexity and multidisciplinary nature of achieving cognitive intelligence in manufacturing processes, this endeavor will revolutionize advanced manufacturing process modeling and optimization, enabling more sustainable manufacturing processes.