Ultrasonic assisted high-speed machining (UAHSM) can be served as a thermomechanical surface sever plastic deformation (SSPD), because of the high-frequency impact load exerting to the sample together with thermomechanical loads due to shearing and plowing. Despite existing of few works which studied the impact of ultrasonic vibration on fatigue life assessment of difficult-to-cut material by experimental approach, they couldn’t provide an in-depth analysis to identify the underlying mechanisms of fatigue due time-consuming and costly fatigue life tests. Hence, elucidating the role of ultrasonic vibration in UAHSM on variation of fatigue life needs further studies. In order to do so, in the present work, a hybrid predictive approach based using ANFIS-based machine learning model and micromechanical Navaro-Rios (NR) fatigue crack propagation model has been introduced to directly correlates the UAHSM’s parameters to fatigue life. Here the former correlates feed rate, cutting velocity and vibration amplitude as process inputs, to surface integrity aspects (SIA) viz residual stress, roughness and grain size as output. Then, the modeled SIA are correlated to fatigue life using the former. The introduced hybrid model was then verified through series of UAHSM by examining the fatigue lives of milled Inconel 718 using four-point bending fatigue tests. Upon confirmation of the developed model, a comprehensive study was carried out to find how the process factors impact variation of SIA and subsequently fatigue. It was found from the results of developed models and confirmatory experiments that the role of ultrasonic vibration on improved fatigue life is mainly due to inducing compressive residual stress and more refined microstructure than the roughness.

中文翻译：

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超声波振动对高速铣削加工的 Inconel 718 疲劳寿命的影响：物理增强机器学习方法


超声波辅助高速加工 （UAHSM） 可以用作热机械表面严重塑性变形 （SSPD），因为施加到样品上的高频冲击载荷以及剪切和犁地产生的热机械载荷。尽管通过实验方法研究超声振动对难切削材料疲劳寿命评估影响的工作很少，但由于耗时且昂贵的疲劳寿命测试，他们无法提供深入的分析以确定疲劳的潜在机制。因此，阐明超声振动在 UAHSM 中对疲劳寿命变化的作用需要进一步研究。为此，在本工作中，引入了一种基于基于 ANFIS 的机器学习模型和微机械 Navaro-Rios （NR） 疲劳裂纹扩展模型的混合预测方法，将 UAHSM 的参数与疲劳寿命直接关联起来。在这里，前者将作为工艺输入的进给速度、切削速度和振动幅度与作为输出的表面完整性方面 （SIA） 相关联，即残余应力、粗糙度和晶粒尺寸。然后，使用前者将建模的 SIA 与疲劳寿命相关联。然后，通过使用四点弯曲疲劳测试检查铣削 Inconel 718 的疲劳寿命，通过一系列 UAHSM 验证了引入的混合模型。在确认开发的模型后，进行了一项全面的研究，以了解过程因素如何影响 SIA 的变化和随后的疲劳。从开发的模型和验证实验的结果中发现，超声波振动对延长疲劳寿命的作用主要是由于诱导压缩残余应力和比粗糙度更精细的微观结构。