Probabilistic-based random maximum defect estimation and defect-related fatigue life prediction for laser direct deposited 316L parts

Abstract

• A probabilistic-based small sample extremum estimation method was proposed for the random maximum defect of LDD parts. • The number of defects samplings was reduced with the combination of EVS and approximate defect distribution. • A defect-related fatigue life prediction model was established basing fatigue critical stress. • The high cycle fatigue life of parts was directly and reliably predicted with process-independent approach. Laser direct deposition (LDD) is a typical additive manufacture process for complex parts, while the highly complex thermal behavior during LDD results in the generation and randomness of parts’ internal defects. Among these internal defects, the maximum defect-induced fatigue cracks initiation is the most influential factor of fatigue life for the in-service performance. Apparently, how to estimate the random maximum defect size of surface polished parts is critical for predicting the fatigue life. Therefore, according to extreme value statistic (EVS) theory, an extremum probabilistic estimation method from small sample size was proposed for parts’ random maximum defect size from a sub-volume to the whole-volume. Subsequently, with the obtained maximum defect and being taken to be equivalent to cracks, a defect-related fatigue life prediction model was established based on the failure critical stress. Orthogonal experiment was carried out for obtaining the different maximum defect, and the hardness for each sample was measured as well. The results showed that: (1) The proposed method can reliably estimate the maximum defect size of LDD-316L parts under the small defect samples size and the error was within 10 %. (2) The established prediction model provided a process-independent method for directly estimating the LDD-316L parts’ fatigue life, with the accuracy being over 78 %. This research provides a novel methodology for estimating parts’ maximum defect size and fatigue life, and offers a theoretical basis for reliability and economy of parts during manufacturing and servicing process.