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ABSTRACTFatigue life assessment of metal additive manufacturing (AM) products has remained challenging due to the uncertainty of as–built defects, heterogeneity of the microstructure, and residual stress. In the past few years, many works have been conducted to develop models in order to predict fatigue life of metal AM samples by considering the existence of AM inherent defects. This review paper addresses the main issues regarding fatigue assessment of metal AM parts by considering the effect of defects and post processing strategies. Mechanisms that are contributing to the failure of metal AM samples are categorized and discussed in detail. Several modelling approaches exist in the case of fatigue life prediction. The common fatigue models that are compatible with AM properties are thoroughly explained by discussing the previous works and highlighting their major conclusions. In addition, the use of machine learning is identified as the future of metal AM fatigue life assessment due to their high performance. The main challenge of today's fatigue and fracture community was identified as the fatigue life estimation of complex geometries with the presence of different types of defects, anisotropic microstructure, and complex state of residual stress. This work proposes the available approaches to tackle this challenge.

Titanium aluminide alloys have gained attention for their lightweight and high-performance properties, particularly in aerospace and automotive applications. Traditional manufacturing methods such as casting and forging have limitations on part size and complexity, but additive manufacturing (AM), specifically electron beam melting (EBM), has overcome these challenges. However, the surface quality of AM parts is not ideal for sensitive applications, so post-processing techniques such as machining are used to improve it. The combination of AM and machining is seen as a promising solution. However, research on optimizing machining parameters and their impact on surface quality characteristics is lacking. Limited studies exist on additively manufactured TiAl alloys, necessitating further investigation into surface roughness during EBM TiAl machining and its relationship to cutting speed. As-built and heat-treated TiAl samples undergo machining at different feed rates and surface speeds. Profilometer analysis reveals worsened surface roughness in both heat-treated and non-heat-treated specimens at certain machining conditions, with higher speeds exacerbating edge cracks and material pull-outs. The hardness of the machined surfaces remains consistent within the range of 32–33.1 HRC at condition 3C (45 SFM and 0.1 mm/tooth). As-built hardness remains unchanged with increasing spindle and cutting head speeds. Conversely, heat-treated condition 3C surfaces demonstrate greater hardness than condition 1A (15 SFM, and 0.04 mm/tooth), indicating increased hardness with varying feed and surface speeds. This suggests crack formation in the as-built condition is considered to be influenced by factors beyond hardness, such as deformation-related grain refinement/strain hardening, while hardness and the existence of the α2 phase play a more significant role in heat-treated surfaces.

Abstract Background No reliable and comprehensive study has been published on the incidence and epidemiological profile of meningitis in Iran from 2008 to 2014, before pneumococcal conjugate vaccine (PCV) and pentavalent vaccine (DTPw-Hep B-Hib (PRP-T) vaccine (pentavac) (adsorbed)) introduction. The present study aimed to portray the epidemiological profile of meningitis in Iran from 2008 to 2014. Methods Data on meningitis cases aged from 1 day to 110 years were extracted from national notifiable diseases surveillance system from March 2008 to December 2014 in Iran. A total number of 48,006 cases of suspected meningitis were identified and 1468 cases of which met the criteria for diagnosis-confirmed meningitis. Of 1468 cases, 1352 patients were included in the study. Results The great number of cases reported from urban areas. Moreover, males were more predominant than females (58.51% vs. 33.81%) in total. The estimated annual incidence rate of meningitis varied from 0.28/100000 in 2008 to 0.09/100000 in 2014. Streptococcus pneumoniae, Haemophilus influenzae and Neisseria meningitidis were the most leading pathogens causing bacterial meningitis, accounted for 266(23.44%), 145(12.78%), 95(8.37%) of cases, respectively. Each of the three bacterial species showed a descending trend. The majority of infected subjects are children under five years. Conclusions Unlike the decreasing trend of meningitis and high percentage of cultures with negative results, according to World Health Organization recommendation PCV introduction into routine immunization is evident. Implementing an enhanced surveillance system to provide high quality data on epidemiological profile of meningitis in Iran is necessary.