Monitoring, Modeling, and Statistical Analysis in Metal Additive Manufacturing: A Review

Abstract

Despite the significant advances made involving the additive manufacturing (AM) of metals, including those related to both materials and processes, challenges remain in regard to the rapid qualification and insertion of such materials into applications. In general, understanding the process–microstructure–property interrelationships is essential. To successfully understand these interrelationships on a process-by-process basis and exploit such knowledge in practice, leveraging monitoring, modeling, and statistical analysis is necessary. Monitoring allows for the identification and measurement of parameters and features associated with important physical processes that may vary spatially and temporally during the AM processes that will influence part properties, including spatial variations within a single part and part-to-part variability, and, ultimately, quality. Modeling allows for the prediction of physical processes, material states, and properties of future builds by creating material state abstractions that can then be tested or evolved virtually. Statistical analysis permits the data from monitoring to inform modeling, and vice versa, under the added consideration that physical measurements and mathematical abstractions contain uncertainties. Throughout this review, the feedstock, energy source, melt pool, defects, compositional distribution, microstructure, texture, residual stresses, and mechanical properties are examined from the points of view of monitoring, modeling, and statistical analysis. As with most active research subjects, there remain both possibilities and limitations, and these will be considered and discussed as appropriate.