Steel represents the predominant construction material due to its versatile properties, which can be extensively modified through variations in composition and production processes. High-strength, low-alloy (HSLA) boron steels are particularly notable for their superior mechanical properties, achieved through precise microstructure control. Microalloying involves minute additions of elements such as Al, Ti, Nb, or V, forming nitrides, carbonitrides, and carbides. These elements exhibit a complex interplay, competing for nitrogen, with titanium showing the highest affinity, followed by boron and aluminum. This competition affects the formation and distribution of nitrides, which in turn impacts the steel microstructure and mechanical properties. Aluminum nitride (AlN) plays a crucial role in grain size control due to its strong pinning effect [1]. Titanium protects boron from forming BN and can facilitate the nucleation of acicular ferrite, thus enhancing toughness [2], but is also keen to result in a bimodal grain growth distribution and an abnormal grain growth phenomenon [3]. Boron, in small quantities of only several wt.-ppm., serves as an economical hardenability promoter [4]. The beneficial "boron effect" needs a comprehensive understanding of the interaction of boron atoms with aluminum and titanium, particularly regarding the competition over nitrogen [5]. Accurate thermodynamic descriptions of these subsystems are crucial for effective thermokinetic precipitation simulations, which predict trends validated through experimental characterization. This leads to a deeper understanding of the interplay of BN with other nitrides such as TiN or AlN, the quantification of dissolved boron in austenite, and the steel microstructure. We present a CALPHAD-based [6] thermokinetic simulation approach using the MatCalc [7] toolbox to investigate the matrix phase composition and properties across a wide range of alloy chemistries. By simulating large variations in chemical composition, we systematically explore the effects of alloying on phase stability, transformation kinetics, and mechanical properties. The accuracy of this method relies on well-calibrated thermodynamic databases and robust kinetic modeling, enabling reliable predictions of phase evolution under realistic processing conditions. This approach significantly reduces development time and minimizes the need for extensive experimental trials in alloy design. Furthermore, it offers the potential for real-time adaptation of process parameters during production, paving the way for more efficient and responsive manufacturing workflows. References 1. Wilson FG, Gladman T (1988) Aluminium Nitride in Steel. International Materials Reviews 33(1): 221–286. doi: 10.1179/imr.1988.33.1.221 2. Baker TN (2019) Titanium Microalloyed Steels. Ironmaking & Steelmaking 46(1): 1–55. doi: 10.1080/03019233.2018.1446496 3. Najafkhani F, Kheiri S, Pourbahari B et al. Recent Advances in the Kinetics of Normal/Abnormal Grain Growth: A Review. 1644-9665 21(1): 1–20. doi: 10.1007/s43452-021-00185-8 4. Sharma M, Ortlepp I, Bleck W (2019) Boron in Heat‐Treatable Steels: A Review. steel research international 90(11): 1900133. doi: 10.1002/srin.201900133 5. Führer M, Zamberger S, Seubert C et al. (2025) Experimental Investigation of the Interplay Between Al-, B-, and Ti-Nitrides in Microalloyed Steel and Thermodynamic Analysis. Metals 15(7): 705. doi: 10.3390/met15070705 6. Kaufman L, Bernstein H (1970) Computer calculation of phase diagrams. With special reference to refractory metals. Academic Press Inc, New York 7. (2024) Matcalc - Solid State and Kinetics Precipitation. https://www.matcalc.at/index.php/databases/open-databases. Accessed 29 Jul 2024