Interactions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parameter space. Here, we apply neural networks for parameter optimisation and for predictive visualisation of expected outcomes in laser surface texturing with blind vias for tribology control applications. Critically, this method greatly reduces the amount of experimental laser machining data that is needed and associated development time, without negatively impacting accuracy or performance. The techniques presented here could be applied in a wide range of fields and have the potential to significantly reduce the time, and the costs associated with laser process optimisation.

在短脉冲激光材料加工过程中，光与物质之间的相互作用是高度非线性的，因此对激光参数（例如脉冲能量，重复频率和使用的脉冲数）非常敏感。由于这种复杂性，基于基本物理原理计算的模拟方法通常只能对这些参数之间的相互关系提供定性理解。诸如参数优化之类的替代方法通常需要在可用参数空间上进行系统的，因此耗时的实验探索。在这里，我们将神经网络应用于参数优化，并在摩擦控制应用的盲孔激光表面纹理化中对预期结果进行预测可视化。至关重要的是 这种方法大大减少了所需的实验激光加工数据的数量以及相关的开发时间，而不会对准确性或性能产生负面影响。此处介绍的技术可以应用于广泛的领域，并且有可能显着减少时间以及优化激光工艺的成本。