Microalloyed low carbon steels with high mechanical strength and elevated toughness obtained by controlled rolling have been widely used in several equipment of oil and gas industry, ensuring safety and reliability. However, residual stresses are inherent to all manufacturing processes and their knowledge is of great importance, considering that in presence of corrosive environments, the joint effect of stress with service loads can lead to structural failure. In this work, the influence of surface quality obtained by machining, shot peening, and bristle blasting was studied on the residual stresses of API 5L X80 steel submitted to static loads, with and without the presence of a corrosive medium. Samples were submitted to static loading performed by proof rings and the residual stresses were analyzed by X-ray diffraction using sin² ψ method. The effect of input conditions surface treatment, exposure medium, and time on residual stress was analyzed via artificial neural networks. Results indicated that surface treatment and the exposure medium have greater influence on residual stress states than loading time, suggesting that the corrosion process along with coarse roughness affects significantly residual stresses of API 5L X80 subjected to static loads. Despite the presenting coarse surface roughness, shot peening was an effective treatment to generate and also maintain stable compressive residual stresses along loading time. Moreover, artificial neural networks with supervised training predicted in an effective way experimental residual stresses for the studied steel even under different conditions of surface treatments, exposure medium, and loading time.

通过控制轧制获得的具有高机械强度和高韧性的微合金低碳钢已广泛用于石油和天然气工业的多种设备中，从而确保了安全性和可靠性。但是，残余应力是所有制造过程所固有的，考虑到腐蚀环境下，应力与服务载荷的共同作用会导致结构破坏，因此残余应力的知识非常重要。在这项工作中，研究了通过机加工，喷丸处理和硬毛喷砂处理获得的表面质量对在有或没有腐蚀性介质的情况下承受静态载荷的API 5L X80钢的残余应力的影响。样品经受由检定环施加的静态载荷，并使用Sin通过X射线衍射分析残余应力²ψ方法。通过人工神经网络分析了输入条件，表面处理，暴露介质和时间对残余应力的影响。结果表明，表面处理和暴露介质对残余应力状态的影响大于加载时间，这表明腐蚀过程以及粗糙的粗糙度会显着影响承受静态载荷的API 5L X80的残余应力。尽管呈现出粗糙的表面粗糙度，喷丸处理还是一种有效的处理方法，可以在加载过程中产生并保持稳定的压缩残余应力。此外，经过监督训练的人工神经网络可以有效地预测所研究钢材的实验残余应力，即使在不同的表面处理，暴露介质和加载时间条件下也是如此。