Volume 2B: Advanced Manufacturing 2019
DOI: 10.1115/imece2019-10323
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In-Situ Fatigue Prediction of Direct Laser Deposition Parts Based on Thermal Profile

Abstract: Additive manufacturing (AM) is a novel fabrication technique which enables production of very complex designs that are not feasible through conventional manufacturing techniques. However, one major barrier against broader adoption of additive manufacturing processes is concerned with the quality of the final products, which can be measured as presence of internal defects, such as pores and cracks, affecting the mechanical properties of the fabricated parts. In this paper, a data-driven methodology is proposed … Show more

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Cited by 5 publications
(7 citation statements)
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“…2,8,12 When the residual stresses in an AM part are limited and the microstructure is nearly homogeneous-for instance, by postfabrication heat treatments or preheating the build plate-defect characteristics (ie, size, shape, location, and distribution) are the main factor affecting the fatigue performance. [20][21][22][23] Thus, being able to predict the fatigue resistance of AM parts from defect characteristics can address the issue of uncertainty. This can significantly decrease the time and cost for the development of new products via AM.…”
Section: Introductionmentioning
confidence: 99%
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“…2,8,12 When the residual stresses in an AM part are limited and the microstructure is nearly homogeneous-for instance, by postfabrication heat treatments or preheating the build plate-defect characteristics (ie, size, shape, location, and distribution) are the main factor affecting the fatigue performance. [20][21][22][23] Thus, being able to predict the fatigue resistance of AM parts from defect characteristics can address the issue of uncertainty. This can significantly decrease the time and cost for the development of new products via AM.…”
Section: Introductionmentioning
confidence: 99%
“…30,31 Crack-growth-based modelling appears to be a promising method for analyzing the fatigue life of AM materials since the cracks already exist as the process-induced voids in the parts fabricated by the current state of AM. 5,21,[32][33][34] However, a systematic application of damage-tolerance principles and defect-based modelling of fatigue-life for materials fabricated by AM is still missing. 17 This study presents fatigue-life estimation of 17-4 precipitation hardening (PH) stainless steel (SS) fabricated via a laser powder bed fusion (LPBF) system using a crack-growth approach.…”
Section: Introductionmentioning
confidence: 99%
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“…The obtained results motivated further research of application of ANNs and other machine learning methods, 34 including propositions of a two-phases methodology for in-situ prediction of fatigue life. 35 This study gave way to a potential roadmap to establish a data-driven evaluation platform that would use a large number of experimental data, arising from tests on miniature specimens, 36 in order to reduce production costs. A recent study 37 has shown that ANNs trained by support vector machine (SVM) method were able to achieve coefficients of determination between the predicted and experimental fatigue lives of Ti-6Al-4 V alloy as high as 0.99.…”
Section: Introductionmentioning
confidence: 99%