Purpose This study aims to provide a comprehensive overview of the current state of the art in powder bed fusion (PBF) techniques for additive manufacturing of multiple materials. It reviews the emerging technologies in PBF multimaterial printing and summarizes the latest simulation approaches for modeling them. The topic of “multimaterial PBF techniques” is still very new, undeveloped, and of interest to academia and industry on many levels. Design/methodology/approach This is a review paper. The study approach was to carefully search for and investigate notable works and peer-reviewed publications concerning multimaterial three-dimensional printing using PBF techniques. The current methodologies, as well as their advantages and disadvantages, are cross-compared through a systematic review. Findings The results show that the development of multimaterial PBF techniques is still in its infancy as many fundamental “research” questions have yet to be addressed before production. Experimentation has many limitations and is costly; therefore, modeling and simulation can be very helpful and is, of course, possible; however, it is heavily dependent on the material data and computational power, so it needs further development in future studies. Originality/value This work investigates the multimaterial PBF techniques and discusses the novel printing methods with practical examples. Our literature survey revealed that the number of accounts on the predictive modeling of stresses and optimizing laser scan strategies in multimaterial PBF is low with a (very) limited range of applications. To facilitate future developments in this direction, the key information of the simulation efforts and the state-of-the-art computational models of multimaterial PBF are provided.
Any production is based on materials. Material properties are of utmost importance, both for productivity as well as for application and reliability of the final product. A sound prediction of materials properties thus is highly important. For metallic materials, such a prediction requires tracking of microstructure and properties evolution along the entire component process chain. In almost all nature and engineering scientific disciplines the computer simulation reaches the status of an individual scientific method. Material science and engineering joins this trend, which permits computational material and process design increasingly. The Integrative Computational Materials and Process Engineering (ICMPE) approach combines multiscale modelling and through process simulation in one comprehensive concept. This paper addresses the knowledge driven design of materials and processes for forgings. The establishment of a virtual platform for materials processing comprises an integrative numerical description of processes and of the microstructure evolution along the entire production chain. Furthermore, the development of ab initio methods promises predictability of properties based on fundamentals of chemistry and crystallography. Microalloying and Nanostructuring by low temperature phase transformation have been successfully applied for various forging steels in order to improve component performance or to ease processing. Microalloying and Nanostructuring contribute to cost savings due to optimized or substituted heat treatments, tailor the balance of strength and toughness or improve the cyclic. A new materials design approach is to provide damage tolerant matrices and by this to increase the service lifetime. This paper deals with the numerically based design of new forging steels by microstructure refinement, precipitation control and optimized processing routes.
This paper presents an efficient mesoscale simulation of a Laser Powder Bed Fusion (LPBF) process using the Smoothed Particle Hydrodynamics (SPH) method. The efficiency lies in reducing the computational effort via spatial adaptivity, for which a dynamic particle refinement pattern with an optimized neighbor-search algorithm is used. The melt pool dynamics is modeled by resolving the thermal, mechanical, and material fields in a single laser track application. After validating the solver by two benchmark tests where analytical and experimental data are available, we simulate a single-track LPBF process by adopting SPH in multi resolutions. The LPBF simulation results show that the proposed adaptive refinement with and without an optimized neighbor-search approach saves almost 50% and 35% of the SPH calculation time, respectively. This achievement enables several opportunities for parametric studies and running high-resolution models with less computational effort.
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