Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys.

A deep learning framework is developed to predict the process-induced surface roughness of AlSi10Mg aluminum alloy fabricated using laser powder bed fusion (LPBF). The framework involves the fabrication of round bar AlSi10Mg specimens, surface topography measurement using 3D laser scanning profilometry, extraction, coupling, and streamlining of roughness and LPBF processing data, feature engineering to select the relevant feature set and the development, validation, and evaluation of a deep neural network model. A mix of core and contour-border scanning strategies are employed to fabricate four sets of specimens with different surface roughness conditions. The effects of different scanning strategies, linear energy density (LED), and specimen location on the build plate on the resulting surface roughness are discussed. The inputs to the deep neural network model are the AM process parameters (i.e., laser power, scanning speed, layer thickness, specimen location on the build plate, and the x,y grid location for surface topography measurements), and the output is the surface profile height measurements. The proposed deep learning framework successfully predicts the surface topography and related surface roughness parameters for all printed specimens. The predicted surface roughness ( S a ) measurements are well within 5% of experimental error for the majority of the cases. Moreover, the intensity and location of the surface peaks and valleys as well as their shapes are well predicted, as demonstrated by comparing roughness line scan results with corresponding experimental data. The successful implementation of the current framework encourages further applications of such machine learning-based methods toward AM material development and process optimization.

A review of crack growth models for near-neutral pH stress corrosion cracking on oil and gas pipelines.

This paper presents a review of four existing growth models for near-neutral pH stress corrosion cracking (NNpHSCC) defects on buried oil and gas pipelines: Chen et al.'s model, two models developed at the Southwest Research Institute (SwRI) and Xing et al.'s model. All four models consider corrosion fatigue enhanced by hydrogen embrittlement as the main growth mechanism for NNpHSCC. The predictive accuracy of these growth models is investigated based on 39 crack growth rates obtained from full-scale tests conducted at the CanmetMATERIALS of Natural Resources Canada of pipe specimens that are in contact with NNpH soils and subjected to cyclic internal pressures. The comparison of the observed and predicted crack growth rates indicates that the hydrogen-enhanced decohesion (HEDE) component of Xing et al.'s model leads to on average reasonably accurate predictions with the corresponding mean and coefficient of variation (COV) of the observed-to-predicted ratios being 1.06 and 61.2%, respectively. The predictive accuracy of the other three models are markedly poorer. The analysis results suggest that further research is needed to improve existing growth models or develop new growth models to facilitate the pipeline integrity management practice with respect to NNpHSCC.

Optimizing the Chinese Electricity Mix for CO2 Emission Reduction: An Input-Output Linear Programming Model with Endogenous Capital.

This study develops an input-output linear programming (IO-LP) model to identify a cost-effective strategy to reduce the economy-wide carbon dioxide (CO2) emissions in China from 2020 to 2050 through a shift in the electricity generation mix. In particular, the fixed capital formation of electricity technologies (FCFE) is endogenized so that the capital-related CO2 emissions of various generation technologies can be captured in the model. The modeling results show that low-carbon electricity, e.g., hydro, nuclear, wind, and solar, is associated with lower operation-related CO2 emissions but higher capital-related CO2 emissions compared to coal-fired electricity. A scenario analysis further reveals that a shift in the electricity generation mix could reduce the accumulated economy-wide CO2 emissions in China by 20% compared to the business-as-usual (BAU) level and could help peak China's CO2 emissions by 2030. The emission reduction is mainly due to a drop in operation-related CO2 emissions of electricity, contributing to a decrease in accumulated economy-wide emissions by 21.4%. The infrastructure expansion of electricity, on the other hand, causes a rise in the accumulated emissions by 1.4%. The proposed model serves as an effective tool to identify the optimal technology choice in the electricity system with the consideration of both direct and indirect CO2 emissions in the economy.