The masks have always been mentioned as an effective tool against environmental threats. They are considered as protective equipment to preserve the respiratory system against the non-desirable air droplets and aerosols such as the viral or pollution particles. The aerosols can be pollution existence in the air, or the infectious airborne viruses initiated from the sneezing, coughing of the infected people. The filtration efficiency of the different masks against these aerosols are not the same, as the particles have different sizes, shapes, and properties. Therefore, the challenge is to fabricate the filtration masks with higher efficiency to decrease the penetration percentage at the nastiest conditions. To achieve this concept, knowledge about the mechanisms of the penetration of the aerosols through the masks at different effective environmental conditions is necessary. In this paper, the literature about the different kinds of face masks and respiratory masks, common cases of their application, and the advantages and disadvantages of them in this regard have been reviewed. Moreover, the related mechanisms of the penetration of the aerosols through the masks are discussed. The environmental conditions affecting the penetration as well as the quality of the fabrication are studied. Finally, special attention was given to the numerical simulation related to the different existing mechanisms.

Fused filament fabrication (FFF), an additive manufacturing technique, unlocks alternative possibilities for the production of complex geometries. In this process, the layer-by-layer deposition mechanism and several heat sources make it a thermally driven process. As heat transfer plays a particular role and determines the temperature history of the merging filaments, the in-process monitoring of the temperature profile guarantees the optimization purposes and thus the improvement of interlayer adhesion. In this review, we document the role of heat transfer in bond formation. In addition, efforts have been carried out to evaluate the correlation of FFF parameters and heat transfer and their effect on part quality. The main objective of this review paper is to provide a comprehensive study on the in-process monitoring of the filament’s temperature profile by presenting and contributing a comparison through the literature.

Additive manufacturing (AM) has emerged as a commonly utilized technique in the manufacturing process of a wide range of materials. Recent advances in AM technology provide precise control over processing parameters, enabling the creation of complex geometries and enhancing the quality of the final product. Moreover, Machine Learning (ML) has become widely used to make systems work better by using materials and processes more intelligently and controlling their resulting properties. In industrial settings, implementing ML not only reduces the lead time of manufacturing processes but also enhances the quality and properties of produced parts through optimization of process parameters. Also, ML techniques have facilitated the advancement of cyber manufacturing in AM systems, thereby revolutionizing Industry 4.0. The current review explores the application of ML techniques across different aspects of AM including material and technology selection, optimization and control of process parameters, defect detection, and evaluation of properties results in the printed objects, as well as integration with Industry 4.0 paradigms. The progressive phases of utilizing ML in the context of AM, including data gathering, data preparation, feature engineering, model selection, training, and validation, have been discussed. Finally, certain challenges associated with the use of ML in the AM and some of the best-practice solutions have been presented.