Control of the surface texture of steel strip during the galvanizing and temper rolling processes is essential to satisfy customer requirements and is conventionally measured post-production using a stylus. In-production laser reflection measurement is less consistent than physical measurement but enables real time adjustment of processing parameters to optimize product surface characteristics. We propose the use of machine learning to improve accuracy of the transformation from inline laser reflection measurements to a prediction of surface properties. In addition to accuracy, model evaluation speed is important for fast feedback control. The ROCKET model is one of the fastest state of the art models, however it can be sped up by utilizing a GPU. Our contribution is to implement the model in PyTorch for fast GPU kernel transforms and provide a soft version of the Proportion of Positive Values (PPV) nonlinear pooling function, allowing gradient flow. We perform timing and performance experiments comparing the implementations
翻译:在振动和调温滚动过程中对钢条表面纹理的控制对于满足客户的要求至关重要,并且是使用一个管状器对生产后进行常规测量的关键。生产中的激光反射测量不及物理测量,但能够实时调整加工参数,以优化产品表面特性。我们提议利用机器学习来提高从内线激光反射测量到预测表面特性的转化的准确性。除了准确性外,模型评价速度对于快速反馈控制非常重要。ROCKET模型是最新工艺模型中最快的状态之一,但可以通过使用GPU来加速。我们的贡献是实施快速GPU内核变PyTorrch模型,并提供正值非线性值比例的软版本,允许梯度流动。我们进行时间和性能实验,比较执行情况。我们比较了执行过程。我们的贡献是,在PyToch中采用快速GPUP内核变换的模型,并提供正值非线性值比例的软化版本。