Evaluation of the role of hatch-spacing variation in a lack-of-fusion defect prediction criterion for laser-based powder bed fusion processes

Abstract

Lack of fusion (LOF) defects impact adversely on the mechanical properties of additively manufactured components produced via laser-based powder bed fusion. Following a stress-relieving heat treatment, the tensile properties and hardness of Ti6Al4V components were found to be negatively impacted by the presence of LOF defects. This work considers a geometrical-based inequality for the prediction of LOF defects. We critically evaluate an LOF criterion using both the experimentally and analytically obtained melt pool geometries. Experimentally, we determined melt pool dimensions by analysing a single-layer, multi-track deposition with oversized hatch spacing in order to establish depth and width from non-overlapping melt pools. Analytically, Rosenthal-based predictions of melt pool size (width and depth) are applied. To investigate LOF defects, we used hatch spacing as the main parameter variation to investigate defects while keeping all other controllable parameters unchanged. An original LOF criterion from the literature was found to be an adequate predictor of LOF defects when experimentally obtained melt pool geometry was used. Critically, however, the analytical expressions for melt pool geometry were found to be in error and this caused the LOF criterion to fail in predicting LOF defects in all cases where defects were observed experimentally. However, an adaptation to the LOF prediction criterion is proposed whereby it is recommended that a correction factor $${R}_{c}^{2}=0.7$$ R c 2 = 0.7 (or $${R}_{c}=0.83$$ R c = 0.83 ) is used with the analytically derived melt pool geometry. Furthermore, this correction is extended into the laser power versus scanning speed operating space to give minimum (corrected) line energy for LOF avoidance in Ti6Al4V components.

A Review on Vibration Characteristics of Additively Manufactured Metal Alloys

“…[ 89 ] The primary purpose of utilizing the VED is to characterize the empirical observations obtained from LPBF experiments. [ 90,91 ] The formulation begins by considering the most crucial process parameters in the following manner…”

A Deep Learning Approach to Classify and Detect Defects in the Components Manufactured by Laser Directed Energy Deposition Process

“…The defects can be located either by destructive or non-destructive postprocessing techniques. In destructive, manufactured samples are cross-sectioned at certain locations, and then by using metallographic procedures the samples are prepared and the defects are captured using optical imaging [8]. In Non-Destructive Testing (NDT), the X-ray Computed Tomography (X-CT) of the sample is carried out to locate the defects within the manufactured components [9].…”

“…The study found that the testing method not only detected the defects but also helped in characterising the shape and size of defects. Harkin et al [8] used both NDT and the destructive characterisation method to capture the lack of fusion defects. In NDT, XCT scan, and destructive characterisation, the optical imaging method was used.…”

Mechanical properties of additively manufactured AlSi10Mg under quasi‐static and cyclic loading

“…On the other hand, if the energy is too high, keyhole porosity defects can occur. 33,34 Lack of fusion defects are identified by the unmelted powders and can be aligned perpendicular to (lack of fusion between layers) or parallel to the building direction (lack of fusion between scan tracks). According to Yang et al, 11 irregularly shaped defects with unmelted regions are sometimes aligned either perpendicular or parallel to the building direction due to a lack of fusion.…”

“…If the energy density is insufficient, lack of fusion defects may form. On the other hand, if the energy is too high, keyhole porosity defects can occur 33,34 …”