Additive Manufacturing (AM) has revolutionized the manufacturing industry in several
directions. Laser powder bed fusion (LPBF), a powder bed fusion AM process, has been …

Fatigue life prediction is critical for ensuring the safe service and the structural integrity of
mechanical structures. Although data-driven approaches have been proven effective in …

Defects in additively manufactured materials are one of the leading sources of uncertainty in
mechanical fatigue. Fracture mechanics concepts are useful to evaluate their influence …

Fatigue life prediction is critical for the design of engineering components made from
defective materials. Not only do these imperfections show a detrimental effect on the fatigue …

Additive manufacturing (AM) is being increasingly applied in aerospace, healthcare,
automotive, and energy fields. Despite good design flexibility, short lead times, and high …

Introducing random defects is a type of the dominant causes of fatigue scatter of additive
manufacturing (AM) materials. The fracture mechanics-based models oversimplify the …

Determining the quality of Ti-6Al-4V parts fabricated by selective laser melting (SLM)
remains a challenge due to the high cost of SLM and the need for expertise in processes …

Additive manufacturing (AM) process-induced defects make the fatigue life prediction of AM-
built parts challenging. A machine learning (ML) framework based on sensitive features and …

Additive manufacturing (AM) has attracted many attentions because of its design freedom
and rapid manufacturing; however, it is still limited in actual application due to the existing …

In this work, a framework based on the machine learning (ML) approach and Spearman's
rank correlation analysis is introduced as an effective instrument to solve the influence of …