As a special review article, several significant and applied results in 3D printing and additive manufacturing (AM) science and technology are reviewed and studied. Which, the reviewed research works were published in 2020. Then, we would have another review article for 2021 and 2022. The main purpose is to collect new and applied research results as a useful package for researchers. Nowadays, AM is an extremely discussed topic and subject in scientific and industrial societies, as well as a new vision of the unknown modern world. Also, the future of AM materials is toward fundamental changes. Which, AM would be an ongoing new industrial revolution in the digital world. With parallel methods and similar technologies, considerable developments have been made in 4D in recent years. AM as a tool is related to the 4th industrial revolution. So, AM and 3D printing are moving towards the fifth industrial revolution. In addition, a study on AM is vital for generating the next developments, which are beneficial for human beings and life. Thus, this article presents the brief, updated, and applied methods and results published in 2020.

Laser powder bed fusion (L-PBF) success in the industrial scenario strongly depends on the ability to manufacture components without defects and with high building rates, but also on the ability to effectively control the microstructure to gain the required properties in the final component. In this regard, the recently developed numerical simulation software of L-PBF technologies can represent an effective tool, since many of them provide solidification data (i.e. temperature gradient and cooling rate) useful for microstructure prediction. In this work, a numerical model was applied to simulate the processing of four single scan tracks of 316L stainless steel processed with different parameters. Temperature and cooling rate around the melt pool were extracted from the numerical model and used to estimate the microstructure cellular arm spacing and the microhardness. Experimental measurements were then compared with the estimated values revealing good agreement. The good agreement between experimental and estimated values shows the advantages of the proposed method for microstructure and microhardness prediction based on numerical modelling as a useful resource for process optimization according to the required final microstructural features.