No abstract

Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is the quality assurance of the AM fabricated parts. While there are several ways of approaching this problem, how to develop informative process signatures to detect part anomalies for quality control is still an open question. The objective of this study is to build a new layer-wise process signature model to characterize the thermal-defect relationship. Based on melt pool images, we propose novel layer-wise key process signatures, which are calculated using multilinear principal component analysis (MPCA) and are directly correlated with the layer-wise quality of the part. The resultant layer-wise quality features can be used to predict the overall defect distribution of a fabricated layer during the build. The proposed model is validated through a case study based on a direct laser deposition experiment, where the layer-wise quality of the part is predicted on the fly. The accuracy of prediction is calculated using three measures (i.e., recall, precision, and F-score), showing reasonable success of the proposed methodology in predicting layer-wise quality. The proposed quality prediction methodology enables online process correction to eliminate anomalies and to ultimately improve the quality of the fabricated parts.

No 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 to predict the size and location of porosities based on in-situ process signatures, i.e. thermal history. Size as well as location of pores highly affect the resulted fatigue life where near-surface and large pores, compared to inner or small pores, significantly reduces the fatigue life. Therefore, building a model to predict the porosity size and location will pave the way toward building an in-situ prediction model for fatigue life which would drastically influence the additive manufacturing community. The proposed model consists of two phases: in Phase I, a model is established to predict the occurrence and location of small and large pores based on the thermal history; and subsequently, a fatigue model is trained in Phase II to predict the fatigue life based on porosity features predicted from Phase I. The model proposed in Phase I is validated using a thin wall fabricated by a direct laser deposition process and the Phase II model is validated based on fatigue life simulations. Both models provide promising results that can be further studied for functional outcomes.

The additive manufacturing (AM) domain contains novel fabrication techniques, which are defined as the process of joining the material together in a layer-by-layer manner, to make 3D objects. [1] AM enables fabricating complex geometries where most of them are not feasible in the domain of conventional manufacturing techniques. The significant design flexibility offered by AM techniques can save a noticeable amount of money and time if applied correctly. However, the broader adoption of AM processes has faced challenging issues, especially at satisfying quality standards and process repeatability. [2] The compromised structural performance resulted from processinduced defects is the main challenge against the continued adoption of AM in different industries. [3,4] Among different modes of mechanical failures, fatigue failure, that is, failure under cyclic loading, is the dominant failure mode in mission-critical applications. [5] This is due to the fact that fatigue is a local phenomenon; thus, it is more directly affected by the microstructural features. [5] 62% of aircraft structures have had failures due to fatigue, where only 14% of them were because of mechanical overload. [6] Meeting fatigue and durability requirements has proven to be a challenging task for AM parts. [3,4] Process and design parameters have shown a significant impact on the microstructure and defect properties of AM parts, and thus largely determine their fatigue life behavior. [3,5] In the absence of voids and inclusions, which typically serve as crack initiation sites, slip bands usually drive the crack initiation in material. [7] Slip length and slip planarity are the two most important factors in determining fatigue properties of titanium alloys. [8] Slip length can be controlled by microstructural features including α laths, phase boundaries and colony boundaries, while slip planarity is governed by oxygen and aluminum content as well as secondary precipitation (Ti3Al). [7] Since fatigue cracks tend to initiate at the longest existing crystallographic slip bands in the microstructure, reducing the maximum dislocation slip length is critical for enhancing the material resistance against fatigue crack initiation (i.e., HCF strength). Crystallographic orientations of the adjacent grains as well as the grain size and geometry act as a barrier for