{"title":"超声波振动对高速铣削加工的 Inconel 718 的疲劳寿命的影响:物理增强型机器学习方法","authors":"Reza Teimouri, Marcin Grabowski","doi":"10.1016/j.ymssp.2024.112115","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"53 1","pages":""},"PeriodicalIF":7.9000,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effect of ultrasonic vibration on fatigue life of Inconel 718 machined by high-speed milling: Physics-enhanced machine learning approach\",\"authors\":\"Reza Teimouri, Marcin Grabowski\",\"doi\":\"10.1016/j.ymssp.2024.112115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":51124,\"journal\":{\"name\":\"Mechanical Systems and Signal Processing\",\"volume\":\"53 1\",\"pages\":\"\"},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2024-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mechanical Systems and Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1016/j.ymssp.2024.112115\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanical Systems and Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.ymssp.2024.112115","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
摘要
超声波辅助高速加工(UAHSM)可作为一种热机械表面断裂塑性变形(SSPD),因为它对试样施加了高频冲击载荷以及剪切和犁耕产生的热机械载荷。尽管已有少数研究通过实验方法研究了超声波振动对难切割材料疲劳寿命评估的影响,但由于疲劳寿命测试耗时且成本高昂,这些研究无法提供深入分析以确定疲劳的内在机制。因此,阐明超声振动在 UAHSM 中对疲劳寿命变化的作用还需要进一步研究。为此,本研究采用基于 ANFIS 的机器学习模型和微机械纳瓦罗-里奥斯(NR)疲劳裂纹扩展模型的混合预测方法,直接将 UAHSM 的参数与疲劳寿命相关联。前者将进给率、切削速度和振动振幅作为工艺输入,将表面完整性(SIA),即残余应力、粗糙度和晶粒度作为输出。然后,利用前者将建模的 SIA 与疲劳寿命相关联。然后,通过一系列 UAHSM,利用四点弯曲疲劳试验对铣削过的 Inconel 718 的疲劳寿命进行检验,从而验证了所引入的混合模型。在确认所开发的模型后,又进行了一项综合研究,以了解工艺因素如何影响 SIA 的变化以及随后的疲劳。从开发的模型和确认实验的结果中发现,超声波振动对提高疲劳寿命的作用主要是诱导压缩残余应力和更精细的微观结构,而不是粗糙度。
Effect of ultrasonic vibration on fatigue life of Inconel 718 machined by high-speed milling: Physics-enhanced machine learning approach
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.
期刊介绍:
Journal Name: Mechanical Systems and Signal Processing (MSSP)
Interdisciplinary Focus:
Mechanical, Aerospace, and Civil Engineering
Purpose:Reporting scientific advancements of the highest quality
Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems