Browsing by Author "Riensche, Alex"
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- Feedforward control of thermal history in laser powder bed fusion: Toward physics-based optimization of processing parametersRiensche, Alex; Bevans, Benjamin D.; Smoqi, Ziyad; Yavari, Reza; Krishnan, Ajay; Gilligan, Josie; Piercy, Nicholas; Cole, Kevin; Rao, Prahalada (Elsevier, 2022-12)We developed and applied a model-driven feedforward control approach to mitigate thermal-induced flaw formation in laser powder bed fusion (LPBF) additive manufacturing process. The key idea was to avert heat buildup in a LPBF part before it is printed by adapting process parameters layer-by-layer based on insights from a physics-based thermal simulation model. The motivation being to replace cumbersome empirical build-and-test parameter optimization with a physics-guided strategy. The approach consisted of three steps: prediction, analysis, and correction. First, the temperature distribution of a part was predicted rapidly using a graph theory-based computational thermal model. Second, the model-derived thermal trends were analyzed to isolate layers of potential heat buildup. Third, heat buildup in affected layers was corrected before printing by adjusting process parameters optimized through iterative simulations. The effectiveness of the approach was demonstrated experimentally on two separate build plates. In the first build plate, termed fxed processing, ten different nickel alloy 718 parts were produced under constant processing conditions. On a second identical build plate, called con-trolled processing, the laser power and dwell time for each part was adjusted before printing based on thermal simulations to avoid heat buildup. To validate the thermal model predictions, the surface tem-perature of each part was tracked with a calibrated infrared thermal camera. Post-process the parts were examined with non-destructive and destructive materials characterization techniques. Compared to fixed processing, parts produced under controlled processing showed superior geometric accuracy and resolu-tion, finer grain size, increased microhardness, and reduced surface roughness.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
- Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturingBevans, Benjamin; Barrett, Christopher; Spears, Thomas; Gaikwad, Aniruddha; Riensche, Alex; Smoqi, Ziyad; Halliday, Harold (Scott); Rao, Prahalada (Taylor & Francis, 2023)We developed and applied a novel approach for shape agnostic detection of multiscale flaws in laser powder bed fusion (LPBF) additive manufacturing using heterogenous in-situ sensor data. Flaws in LPBF range from porosity at the micro-scale (< 100 mu m), layer related inconsistencies at the meso-scale (100 mu m to 1 mm) and geometry-related flaws at the macroscale (> 1 mm). Existing data-driven models are primarily focused on detecting a specific type of LPBF flaw using signals from one type of sensor. Such approaches, which are trained on data from simple cuboid and cylindrical-shaped coupons, have met limited success when used for detecting multiscale flaws in complex LPBF parts. The objective of this work is to develop a heterogenous sensor data fusion approach capable of detecting multiscale flaws across different LPBF part geometries and build conditions. Accordingly, data from an infrared camera, spatter imaging camera, and optical powder bed imaging camera were acquired across separate builds with differing part geometries and orientations (Inconel 718). Spectral graph-based process signatures were extracted from this heterogeneous thermo-optical sensor data and used as inputs to simple machine learning models. The approach detected porosity, layer-level distortion, and geometry-related flaws with statistical fidelity exceeding 93% (F-score).