Targeted rework strategies for powder bed additive manufacture

Abstract

A major factor limiting the adoption of powder-bed-fusion additive manufacturing for production of parts is the control of build process defects and the effect these have upon the certification of parts for structural applications. In response to this, new methods for detecting defects and to monitor process performance are being developed. However, effective utilisation of such methods to rework parts in process has yet to be demonstrated. This study investigates the use of spatially resolved acoustic spectroscopy (SRAS) scan data to inform repair strategies within a commercial selective laser melting machine. New methodologies which allow for rework of the most common defects observed in selective laser melting (SLM) manufacturing are proposed and demonstrated. Three rework methodologies are applied to targeted surface breaking pores: a hatch pattern, a spiral pattern and a single shot exposure. The work presented shows that it is possible to correct surface breaking pores using targeted re-melting, reducing the depth of defects whilst minimising changes in local texture. For the hatch rework and spiral rework, a reduction in defect depth of 50 % and 31 % were observed, respectively, however, no improvement was seen after the single shot exposures. This work is part of a programme to develop a method by which defects can be detected and the part reworked in-process during SLM to enable defect specification targets to be met. Although further work in developing build-characterise-rework strategies for integrated and targeted defect correction is needed, the feasibility of the underlying method of identifying and selectively reworking to reduce a defect has now been demonstrated for the first time.

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

“…[5] Machine learning has been successfully applied in applications such as image processing, text classification, and speech recognition. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] An example of the utility of machine learning in the established quality control method of visual inspection is demonstrated by the use of a neural network to identify flaws in laser powder bed fusion 3D printing. [8] Examples of their use in both monitoring/feedback applications and predictive models include predicting property outcomes based on parameter settings, predicting global parameter settings for specific outcomes, identifying failures during printing, predicting bead geometry, adjusting geometry to prevent failures, and assessing part manufacturability.…”

Spatially Resolved Acoustic Spectroscopy Towards Online Inspection of Additive Manufacturing

“…Once several categories of defects were observed, based on the information presented in Table 1, the cause of defects could be identified and action could be taken to prevent them in subsequent layers. Armed with information such as the location, size and shape of the defects, a repair strategy could be implemented in a manner similar to that discussed by Hirsch et al [14] .…”

“…It has the capability to inform upon the microstructural texture of the part [11] , the ability to differentiate between surface and subsurface defects [12] and the potential to measure on rough surfaces [13] . Hirsch et al conducted a preliminary study into the feasibility of localised reworking of recognised defects, based on SRAS data [14] . The technique and instrumentation is described in greater depth in a previous study [15] .…”

Archives of Metallurgy and Materials

“…However, HIP process requires much time and cost additionally. In this regards, laser re-melting or re-scanning method have been applied to obtain high densification during SLM process [15][16][17] which was normally applied only on the surface of metal component to obtain fine surface finishing [18][19][20][21]. Laser re-melting during SLM process can increase the density by closing pores in each layers.…”