Microstructure modeling of a sintered Al–4Si–0.6Mg alloy extruded at semi-solid temperature ranges

The microstructure evolution of sintered and extruded samples of Al–4Si–0.6Mg powder alloys at various semi-solid temperature ranges of 560 °C, 580 °C, and 600 °C, holding times of 600, 1200, and 1800 s, and strain rates of 0.1, 0.2, and 0.3 s−1 was studied. From the stress–strain curves and metallographic studies, Arrhenius grain growth model and Avrami dynamic recrystallization model have been formulated by means of linear regression. Parameters such as peak strain, critical strain, recrystallization fraction, and material constants have been found using the above equations. The experimental and calculated values of various material parameters agree with each other, indicating the accuracy of the developed model. Finite element method-based simulations were performed using DEFORM 2D software, and the average grain size obtained from experiments and simulations was validated by means of average grain size. The relative density of the compacted specimens as well as the extruded specimens was also simulated. The simulation results showed that large grains appeared at high temperatures and at the bottom of the specimen.

Reconstruction of Microscopic Thermal Fields from Oversampled Infrared Images in Laser-Based Powder Bed Fusion

This article elucidates the need to consider the inherent spatial transfer function (blur), of any thermographic instrument used to measure thermal fields. Infrared thermographic data were acquired from a modified, commercial, laser-based powder bed fusion printer. A validated methodology was used to correct for spatial transfer function errors in the measured thermal fields. The methodology was found to make a difference of 40% to the measured signal levels and a 174 °C difference to the calculated effective temperature. The spatial gradients in the processed thermal fields were found to increase significantly. These corrections make a significant difference to the accuracy of validation data for process and microstructure modeling. We demonstrate the need for consideration of image blur when quantifying the thermal fields in laser-based powder bed fusion in this work.

An Integrated Computational Materials Engineering Predictive Platform for Fatigue Prediction and Qualification of Metallic Parts Built with Additive Manufacturing

Additive manufacturing (AM) processes create material directly into a functional shape. Often the material properties vary with part geometry, orientation, and build layout. Today, trial-and-error methods are used to generate material property data under controlled conditions that may not map to the entire range of geometries over a part. Described here is the development of a modeling tool enabling prediction of the performance of parts built with AM, with rigorous consideration of the microstructural properties governing the nucleation and propagation of fatigue cracks. This tool, called DigitalClone® for Additive Manufacturing (DC-AM), is an Integrated Computational Materials Engineering (ICME) tool that includes models of crack initiation and damage progression with the high-fidelity process and microstructure modeling approaches. The predictive model has three main modules: process modeling, microstructure modeling, and fatigue modeling. In this paper, a detailed description and theoretical basis of each module is provided. Experimental validations (microstructure, porosity, and fatigue) of the tool using multiple material characterization and experimental coupon testing for five different AM materials are discussed. The physics-based computational modeling encompassed within DC-AM provides an efficient capability to more fully explore the design space across geometries and materials, leading to components that represent the optimal combination of performance, reliability, and durability.