Big Data Analytics

Additive manufacturing (AM) has advanced considerably as a disruptive technology that addresses the increasing demand for multi-functional, multi-material, and geometrically complex components and has begun to reshape areas of both product development and production methods. However, significant challenges related to incompatible material properties, defective parts made from AM processes, and inconsistent use of product build quality still exist. The ability to achieve highly precise AM processes is heavily reliant on monitoring, controlling, and utilizing multiple process variables effectively. In recent years, more advanced methods, including machine learning (ML), big data analytics, and design for additive manufacturing (DfAM), have become more available to address these limitations. Although these methods have been studied significantly, their use in aerospace-specific settings has been limited. In this review, study first provides an in-depth discussion on recent trends in DfAM. The review also considers simulation and modeling tools as enablers to obtain improved geometric fidelity in AM and to increase predictability of AM processes. Other trends include improved automation through the IoT, as well as knowledge-based process planning, also for multi-part cases of production. Lastly, review summarize many of the on-going issues and some future directions for algorithm-driven AM in aerospace and relate to industry 4.0 trends that emphasize intelligent automation, adaptive process control and lifecycle management, which will improve efficiency, reliability, and scale-up of AM technologies in aerospace and other advanced engineering applications.