Showing papers by "Tarasankar Debroy published in 2022"

Superior printed parts using history and augmented machine learning

Abstract: Abstract Machine learning algorithms are a natural fit for printing fully dense superior metallic parts since 3D printing embodies digital technology like no other manufacturing process. Since traditional machine learning needs a large volume of reliable historical data to optimize many printing variables, the algorithm is augmented with human intelligence derived from the rich knowledge base of metallurgy and physics-based models. The augmentation improves the computational efficiency and makes the problem tractable by enabling the algorithm to use a small set of data. We provide a verifiable quantitative index for achieving fully dense superior parts, facilitate material selection, uncover the hierarchy of important variables that affect the density, and present easy-to-use visual process maps. These findings can improve the quality consistency of 3D printed parts that now limit their greater industrial adaptation. The approach used here can be applied to solve other problems of 3D printing and beyond.

Rapid Calculation of Part Scale Residual Stress - Powder Bed Fusion of Stainless Steel, and Aluminum, Titanium, Nickel Alloys

Abstract: A novel analytical model is developed to compute the part scale through-thickness longitudinal residual stress distributions and applied for laser powder bed fusion of four commonly used powder alloys. An important input for the analytical modeling calculations is the peak residual stress for a deposited layer, which is estimated using a unique functional relationship and presented as a function of important process conditions for laser powder bed fusion of different powder alloys. The analytically calculated results of longitudinal residual stress distributions through the part and baseplate thickness are tested rigorously with the corresponding numerically computed and experimentally measured results in the literature for laser powder bed fusion of small and large parts involving the deposition of several thousands of layers. It is shown further that the analytical model can serve as a fast and practical design tool to estimate the through-thickness longitudinal residual stress distribution, which is along the length of the part, for part scale laser powder bed fusion using inexpensive computational resources and with appreciable accuracy. The raw/processed data required to reproduce these findings cannot be shared at this time due to technical or time limitations. • An analytical model for residual stresses in 3D printed parts is extensively tested. • The model is validated with experimental data and numerically computed results. • Residual stresses in large 3D printed parts of 5 alloy compositions were examined. • Powder-specific process maps of peak tensile residual stresses are presented.

Rapid calculation of part scale residual stresses in powder bed additive manufacturing

Abstract: Numerical modelling of thermo-mechanical residual stresses for laser powder bed fusion is complex and computationally intensive. A novel analytical model is presented here that can compute the residual stress distributions through a printed part and the baseplate quickly and reliably using phenomenological modelling. The peak residual stress for each deposited layer, needed in the model, is computed using scaling analysis. The computed residual stress distributions are tested with the corresponding independent experimentally measured and numerically computed results. The analytically calculated residual stress distributions are shown to be in good agreement with the corresponding independent results. The analytical model is shown to be 10,000 times faster than the numerical models.

Vapor Pressure versus Temperature Relations of Common Elements

Abstract: The vapor pressure values of common elements are available in the literature over a limited temperature range and the accuracy and reliability of the reported data are not generally available. We evaluate the reliability and uncertainty of the available vapor pressure versus temperature data of fifty common pure elements and recommend vapor pressure versus temperature relations. By synthesizing the vapor pressure values from measurements reported in the literature with the values computed using the Clausius Clapeyron relation beyond the boiling point, we extend the vapor pressure range from 10−8 atm to 10 atm. We use a genetic algorithm to optimize the fitting of the vapor pressure data as a function of temperature over the extended vapor pressure range for each element. The recommended vapor pressure values are compared with the corresponding literature values to examine the reliability of the recommended values.