Mechanical pre-stressing treatments using shot peening are widely used in automobile, aeronautic and biomedical industries to improve mechanical parts and structures. These cold working processes use spherical media called shot, and introduce surface compressive residual stresses that are found to enhance the fatigue resistance of intermediate and high strength metals and alloys. They protect the structure from fracture as fatigue cracks propagates mostly from surfaces during operation. The gain in strength and fatigue life observed after such a treatment can be spectacular while offering the advantage of being relatively easy to perform technically. It is, therefore, not surprising that major companies working in very different areas have now turned their attention to such applications and attempt to control the operating conditions of the process in order to optimize coverage or to achieve targeted surface properties.
 
In order to make additional progress and bring the technology to the next level, there is need to understand how shot behaves inside the peening chamber, and how the operating parameters (shot density, velocity, chamber geometry) will affect the shot impact on pieces and parts. Although the measurements of steel sphere velocities and angles can be relatively straightforward, not much is known on the way shot behaves collectively. The situation becomes even worse for the case of ultrasonic shot peening in which the spheres are propelled by an ultrasonic vibrating wall (a sonotrode) bounce around in a blind peening chamber.
 
 
It is, therefore, widely believed, that a direct visualization of the shot from hard sphere simulations could provide an interesting added value to the problem posed, while also saving R&D money used for cumbersome measurements of sphere bouncing, chaining methods relating the operating conditions to materials properties, validity assessments, and post-treatment analysis. In fact, because most of these aspects can be hardly reconciled, most of the design of peening chambers and the choice of process parameters remain empirical, making it costly, time consuming and partially optimized, especially when complex parts are to be considered.