Category: Insight Shorts

Fragmentation is the breaking and shattering of brittle objects under the action of sudden forces. Examples include the shattering of a ceramic plate in the free fall, the crushing of mineral ore, or the grinding of coffee beans. There is something universal about fragmentation in terms of the crack pattern and fragment size. For many years, research has shown that fragmentation obeys power laws, reflecting its self‒similar nature [1]. Figure shows the scale-invariance and fractal nature of the fragmentation pattern, where the size of the triangular pieces scales up down with respect to the largest fragment. A recent article [2] by French physicist Emmanuel Villermaux at Aix-Marseille University proposed an entropy‒based formulation which interestingly corroborates this finding; irrespective of the size of the parent body, the graph relating fragment size and number follows the same trend.

The study of fragmentation initially originated in the mining and military sectors. Today, the application spans diverse areas of mechanical engineering, such as powder technology (manufacturing), satellite engineering (design) and shock physics (thermal). In the design of spacecraft protection systems, hypervelocity impact represents a critical aspect of planetary defense, as microgranular particles can strike spacecraft structures at velocities exceeding 4 km/s. In this context, K T Ramesh’s group at Johns Hopkins University has developed an adaptable two‒stage gas gun that can launch a particle cloud onto the target plate at. Further, material spallation and erosion are characterized using in‒situ technique like ultra‒high speed imaging and post‒mortem tool like profilometry [3]. This exciting work motivates exploration of debris density emanating from volcanic eruptions and supernovae. Alongside theoretical and experimental efforts, the advent of machine learning models and advances in computational methods have enabled reliable fragment size estimation using databases generated from numerous historical dynamic tests for various materials. A paper by Huan et al. demonstrates an accurate prediction of mean fragment size from a deep learning algorithmic framework that integrates convolutional neural networks, least square support vector machines and Newton‒Raphson based optimization [4]. Nonetheless, the emerging AI and ML technologies, combined with innovative experiments and state‒of‒the‒art instruments will facilitate deeper insight into the mechanics of incipient fragmentation.

REFERENCES

• B. Huan, X. Li, J. Wang, T. Hu, and Z. Tao, Sci. Rep. 15, 11515 (2025).
• G. Domokos, F. Kun, A. A. Sipos, and T. Szabó, Sci. Rep. 5, 9147 (2015).
• E. Villermaux, Phys. Rev. Lett. 135, 228201 (2025).
• J. Moreno, M. Shaeffer, S. Slingluff, Y. R. Rhim, D. Brown, and K. T. Ramesh, Int. J. Impact Eng. 203, 105366 (2025).