Efficient parameter optimization in additive manufacturing : A systematic study on AlSi10Mg in laser powder bed fusion

Abstract

Metal additive manufacturing (AM) has become increasingly important in the lightweight construction of complex components in the automotive and aerospace industries as well as medicine in recent years [1]. The alloys required for this must be both high-performant and safety-compliant. In order to accelerate the development of new materials for AM and save resources, an efficient material development methodology for qualifying new AM materials is essential. In this context, X-ray testing [2] opens numerous possibilities to replace a comprehensive metallurgical examination and micrograph analysis. Applying X-ray microfocus computed tomography, test specimens are analyzed reproducibly throughout their entire volume so that irregularities within an alloy sample can be reliably detected. In particular, a subsequent pore analysis of the AM material allows us to draw conclusions on the material properties, by classifying the pores and statistically evaluating their size and shape [3,4]. The derived information is compared to micrograph analysis from quality control and combined with complementary data on the samples’ mechanical properties. The proposed AM development process is exemplified in a systematic study on an AlSi₁₀Mg alloy in laser powder bed fusion under variation of the laser power and the scan velocity. The resulting AM-parameter resolved datasets [5] allow to draw conclusions on the optimum 3D printing parameters for a specific case at hand – iteratively optimizing the AM process and thus improving the material development efficiency