Porosities are major defects in additively manufactured metal parts. The relationships between process parameters and formation and evolution of porosities are sparsely studied. &nbsp;Moreover, a quantitative investigation focused on the relationship between spatial distribution of pores and process parameters is yet not reported. Based on the statistical methods provided in this study, more reliable and robust process parameter maps are constructed to reflect the effects of porosities, a significant type of defect in metal additive manufacturing, on part’s performance. It is know that mechanical properties of additively manufactured parts are sensitive to the presence of porosities form during the manufacturing process. As mentioned, the impact of porosities on the mechanical performance has been investigated extensively with respect to different parameters, such as pore volume fraction, shape, and size. However, statistical investigations focusing on the relationships between the spatial distribution of pores and process parameters; and consequently, the performance of the manufactured parts are scattered and basically absent. Also, these sparse investigations usually suffer from an ambiguous definition of terminologies. For instance, the required criteria to consider a point pattern as complete spatial randomness (CSR) are generally not clarified. Moreover, no numerical formalism is yet developed to show how much the observed spatial results are statistically significant. To address these shortcomings, the statistical definition of CSR in a point pattern and the procedure to quantitatively determine the deviation of a pattern from CSR were discussed. The explained statistical approach was used to investigate the effect of scanning speed parameter on the spatial distribution of gas porosities in laser powder bed fusion manufactured stainless steel parts. Furthermore, and to highlight the impact of the spatial distribution of porosities on mechanical properties, fatigue performances of parts with clustered and randomly distributed porosities were simulated by finite element analysis. It is shown that by reducing the scanning speed, the spatial distribution of gas porosities deviated more from CSR, and correspondingly fatigue performance deteriorated. The fatigue simulations serve as an exmaple that spatial distribution of a weak phase, in this case porosities created in additive manufacturing process, are quite significant in determining the service life of the 3D printed part and consequently, the spatial distribution of porosities should be account for in designing the manufacturing process, the functionality of the part and service aspects of the part. It is important to note that according to reports in the open literature, fatiuge endurance&nbsp;of additively manufactured parts is generally less than their counterparts manufactured by traditional methods.&nbsp;&nbsp;&nbsp;

Statistical Description of Spatial Distribution of Porosities in Metal Additive Manufacturing

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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