An Integrated Computational Materials Engineering Predictive Platform for Fatigue Prediction and Qualification of Metallic Parts Built With Additive Manufacturing
نویسندگان
چکیده
Abstract Additive manufacturing (AM) processes create material directly into a functional shape. Often the properties vary with part geometry, orientation, and build layout. Today, trial-and-error methods are used to generate property data under controlled conditions that may not map entire range of geometries over part. Described here is development modeling tool enabling prediction performance parts built AM, rigorous consideration microstructural governing nucleation propagation fatigue cracks. This tool, called DigitalClone® for additive (DCAM), an Integrated Computational Materials Engineering (ICME) includes models crack initiation damage progression high-fidelity process microstructure approaches. The predictive model has three main modules: modeling, modeling. In this paper, detailed description theoretical basis each module provided. Experimental validations (microstructure, porosity, fatigue) using multiple characterization experimental coupon testing five different AM materials discussed. physics-based computational encompassed within DCAM provides efficient capability fully explore design space across materials, leading components represent optimal combination performance, reliability, durability.
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ژورنال
عنوان ژورنال: Journal of tribology
سال: 2021
ISSN: ['0742-4787', '1528-8897']
DOI: https://doi.org/10.1115/1.4050941