Abstract
Layering deposition methodology in metal additive manufacturing (AM) and the influence of different processing parameters, such as energy source level and deposition speed, which can change the melt pool condition, are known to be the important influencing factors on properties of components fabricated via AM. The effect of melt pool conditions and geometry on properties and quality of fabricated AM components has been widely studied through experimental and simulation techniques. There is a need for better understanding the influence of solidified melt pool topography on characteristics of next deposition layer that can be applied to complex surfaces, especially those with sparse topographical features, such as those that occur in AM deposition layers. Topography of deposited layers in metal additive manufacturing is a significant aspect on the bonding condition between the layers and defect generation mechanism. Characterization of the topography features in AM deposition layers offers a new perspective into investigation of defect generation mechanisms and quality evaluation of AM components. In this work, a feature-based topography study is proposed for the assessment of process parameters’ influence on AM deposition layers topography and defect generation mechanism. Titanium alloy (Ti6Al4V) samples deposited on steel substrate, by direct energy deposition (DED) AM technique at different process conditions, were used for the assessment. Topography datasets and analysis of shape and size differences pertaining to the relevant topographic features have been performed. Different AM process parameters were investigated on metallic AM samples manufactured via direct energy deposition (DED) and the potential defect generation mechanism was discussed. The assessment of the topography features was used for correlation study with previously published in-situ monitoring and quality evaluation results, where useful information was obtained through characterization of signature topographic formations and their relation to the in-situ acoustic process monitoring, as the indicators of the manufacturing process behavior and performance.
Subject
General Agricultural and Biological Sciences
Cited by
3 articles.
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