Additive Manufacturing of Functional, Energy, and Magnetic Materials: Poster Session
Sponsored by: TMS Functional Materials Division, TMS: Additive Manufacturing Committee, TMS: Magnetic Materials Committee
Program Organizers: Markus Chmielus, University of Pittsburgh; Sneha Prabha Narra, Carnegie Mellon University; Mohammad Elahinia, University of Toledo; Reginald Hamilton, Pennsylvania State University; Iver Anderson, Iowa State University Ames Laboratory
Wednesday 5:30 PM
March 17, 2021
Room: RM 2
Location: TMS2021 Virtual
Additive Manufacturing of Soft Magnets for Electrical Machines—Prospects and Challenges: Tej Nath Lamichhane1; Latha Sethuraman2; Adrian Dalagan1; Haobo Wang1; Jonathan Keller2; M. Parans Paranthaman1; 1Oak Ridge National Laboratory; 2National Renewable Energy Laboratory
With growing interest in electrification from clean energy technologies, such as wind power, and the use of pure electric powertrains in various applications, the demand for next-generation, high-performance magnetic materials has risen significantly. Electrical machine design for these applications is facing challenges in terms of meeting very demanding metrics for power densities and conversion efficiencies, thereby motivating the exploration of advanced materials and manufacturing for the next generation of lightweight ultra-efficient electric machines. In this work, the state of the art in additive manufacturability and performance characteristics of soft magnetic materials for electric machines will be summarized and presented. The prospects of soft magnetic materials selection in terms of price, printability, weight, and performance of the electrical machines will also be discussed. We will highlight the current status of AM of large electrical machines, additive manufacturing process selection guidelines, hybrid printing technologies, and the associated opportunities and challenges.
Effect of Processing Parameters on Thermal Cyclic Stability of Nitinol Alloys Manufactured by Selective Laser Melting: Jianing Zhu1; Evgenii Borisov2; Johan Bijleveld1; Eduard Farber2; Marcel Hermans1; Vera Popovich1; 1Delft University of Technology; 2Peter the Great Saint-Petersburg Polytechnic University
There is a growing demand in using additive manufacturing (AM) for production of smart components capable to respond to thermal and mechanical stimuli. In this study phase transformation behavior of equiatomic Nitinol alloy manufactured by selective laser melting (SLM) was investigated by differential scanning calorimetry (DSC) in the temperature range of -70 oC to 200 oC. Thermal cycling associated with functional fatigue was performed in order to characterize the phase transformation stability. Phase transformation temperatures of SLM Nitinol alloys were found to decrease with increasing number of cycles. The effect of SLM process parameters was further investigated to improve the phase transformation stability. The optimized samples featuring improved functional stability were found to be attributed to their fine-grained structure and pre-existing nano-scale precipitates. Hence, this work demonstrates that thermal cycling stability of SLM Nitinol alloys can be successfully tailored by changing AM processing parameters.
Modeling of Selective Laser Melting of NiTi Shape Memory Alloy: Laser Single Track and Melt Pool Dimension Prediction: Hossein Abedi1; Reza Javanbakht1; Mohammadreza Nematollahi1; Keyvan Safaei1; Ala Qattawi1; Mohammad Elahinia1; 1The University of Toledo
NiTi Shape memory alloys are used in a growing number of applications, with ongoing research on practical Additive Manufacturing (AM) processing focusing on arriving at optimized AM process parameters to develop functional NiTi devices with minimal post-processing. Mathematical modeling is a viable route to expedite the research on the effect of process parameters on the thermomechanical properties of the fabricated parts. In this work, a 3D thermal numerical model of a selective laser melting AM process for NiTi is developed in COMSOL. To this end, a single-track scanning of laser over the NiTi substrate has been modeled. The model is calibrated for the uncertain parameters. The calibration is performed through the experimental temperature measurements via a thermal camera for a specific set of laser process parameters. The model is applied to predict the melt pool dimensions by highlighting the areas where the temperature is above the melting temperature of NiTi.