Thesis
Investigation of additive manufacturing for producing electrical steel components and texture development via laser metal deposition
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2026
- Thesis identifier
- T17622
- Person Identifier (Local)
- 202056208
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- This thesis presents the first successful application of additive manufacturing (AM), specifically Laser Metal Deposition (LMD), to fabricate high-silicon electrical steel (Fe-6.5 wt% Si) components for electrical machine applications. The alloy is extremely difficult to process due to its brittleness and high cracking tendency, making conventional manufacturing routes unsuitable. To address these challenges, this research focuses on optimising the alloy’s metallurgical properties, microstructure, and crystallographic texture to enhance its magnetic performance through the innovative use of LMD. A systematic process window was developed by varying laser power (400–500 W), scanning speed (360–440 mm/min), and powder feed rate (2.0 g/min) under controlled argon shielding. Porosity analysis revealed values ranging from 0.1% to 1.3%, with sub-optimal parameters leading to lack-offusion pores at low laser power and high scan speed, and to keyhole porosity and cracking at excessive laser power. Optimised conditions consistently yielded dense builds with porosity below 0.2%, with some samples entirely free of cracks. Comprehensive microstructural analysis was performed using optical microscopy, scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and energy-dispersive X-ray spectroscopy (EDS). The study identified processing conditions that promote a strong <001> crystallographic texture while minimising the presence of undesirable <111> orientations—critical for optimising magnetic properties. The influence of laser power, scan speed, scan strategies, energy density, grain size, grain boundary characteristics, and kernel average misorientation (KAM) was examined, revealing how optimised LMD settings reduce defects and enhance microstructural quality. Mechanical properties were evaluated using Vickers hardness testing, which showed maximum values of 376–386 HV at higher energy densities, attributed to improved fusion, reduced porosity, and the formation of ordered phases (DO3, B2) that restrict dislocation movement. The results confirm that LMD can produce FeSi 6.5 wt% components with minimal defects, good hardness, and improved texture alignment. The developed process window enables consistent, repeatable fabrication of core materials and other soft magnetic components for electric motors. Additionally, the use of 316L austenitic steel as a foundation layer during LMD successfully resolved the persistent cracking and bonding issues typically associated with direct FeSi deposition. By optimising process parameters, the bonding quality was greatly improved, leading to enhanced magnetic properties through the promotion of a strong <001> crystallographic orientation. This alignment is crucial for maximising magnetic performance and overall efficiency in advanced applications. Further geometric modifications strengthened the <001> texture, minimising residual stresses and completely eliminating cracking, even without the need for a foundation layer. This breakthrough demonstrates the potential of multi-material deposition and geometric design optimisation in advancing the fabrication of magnetic components, paving the way for superior performance in high-tech applications. Overall, this research provides pioneering insights into optimising the crystallographic texture of electrical steel via LMD, enabling the fabrication of highperformance components for electrical machines. Future work should explore novel stator designs, such as Hilbert structures, and hybrid manufacturing approaches to fully harness the potential of AM technologies in this field.
- Advisor / supervisor
- Tamimi, Saeed
- Butler, David
- Javadi, Yashar
- Resource Type
- DOI
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PDF of thesis T17622 | 2026-03-20 | Public | Download |