Han Yan, Xin Ding, Dawei Huang, Xiaojun Yan, Xingyu Yang, Haohao Liu
{"title":"Strain distribution of a fir-tree tenon/mortise structure under combined high and low cycle fatigue loads","authors":"Han Yan, Xin Ding, Dawei Huang, Xiaojun Yan, Xingyu Yang, Haohao Liu","doi":"10.1111/ffe.14374","DOIUrl":"https://doi.org/10.1111/ffe.14374","url":null,"abstract":"<p>The fir-tree tenon/mortise structure in areo-engine suffers from the combined high and low cycle fatigue (CCF) loads during service. The structural integrity of the tenon/mortise structure is significantly affected by the local strain distribution, while the strain value under the CCF loads is difficult to acquire. In this study, a simulated specimen of tenon/mortise structure is designed, and the CCF test is carried out using a divided-path loading fixture to avoid interference between the high cycle fatigue (HCF) loads and the low cycle fatigue (LCF) loads. The high speed digital image correlation (DIC) method is adopted to acquire the real-time strain distribution during CCF test. According to the strain results, the critical position of the mortise and its strain variation are determined. The high cycle strain of the mortise is proportional to the vibration amplitude of the tenon. The experimental results can provide basis for strength and life assessment.</p>","PeriodicalId":12298,"journal":{"name":"Fatigue & Fracture of Engineering Materials & Structures","volume":"47 9","pages":"3474-3485"},"PeriodicalIF":3.1,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A direct analytical methodology for the assessment of ductile fracture in metals based on multiaxial tests","authors":"Gabriele Cortis, Marcello Piacenti, Filippo Nalli, Luca Cortese","doi":"10.1111/ffe.14378","DOIUrl":"https://doi.org/10.1111/ffe.14378","url":null,"abstract":"<p>The prediction accuracy of ductile damage models is subject to a sound calibration strategy, which normally involves the execution of complex multiaxial tests and requires dedicated facilities. In addition, finite element (FE) analysis is mandatory to retrieve the stress and strain states at the critical point, which cannot be directly measured from experiments. To overcome this complexity, a minimal set of simple multiaxial tests is selected, and an analytical-numerical approach is proposed to evaluate, without resorting to FE, both the stress evolution with plastic deformation and the fracture strain, under any different loading condition of each test. This is achieved from the sole knowledge of the material bilinear stress–strain relation and of the applied test displacement at fracture. The obtained results are compared with a traditional testing and calibration methodology, and the robustness of the approach is proved on a 17-4PH steel, an X65 steel, and a Ti6Al4V alloy.</p>","PeriodicalId":12298,"journal":{"name":"Fatigue & Fracture of Engineering Materials & Structures","volume":"47 9","pages":"3408-3424"},"PeriodicalIF":3.1,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Johannes Diller, Ludwig Siebert, Michael Winkler, Dorina Siebert, Jakob Blankenhagen, David Wenzler, Christina Radlbeck, Martin Mensinger
{"title":"An integrated approach for detecting and classifying pores and surface topology for fatigue assessment 316L manufactured by powder bed fusion of metals using a laser beam using \u0000\u0000 μCT and machine learning algorithms","authors":"Johannes Diller, Ludwig Siebert, Michael Winkler, Dorina Siebert, Jakob Blankenhagen, David Wenzler, Christina Radlbeck, Martin Mensinger","doi":"10.1111/ffe.14375","DOIUrl":"https://doi.org/10.1111/ffe.14375","url":null,"abstract":"<p>This research aims to detect and analyze critical internal and surface defects in metal components manufactured by powder bed fusion of metals using a laser beam (PBF-LB/M). The aim is to assess their impact on the fatigue behavior. Therefore, a combination of methods, including image processing of micro-computed tomography (\u0000<span></span><math>\u0000 <mi>μ</mi></math>CT) scans, fatigue testing, and machine learning, was applied. A workflow was established to contribute to the nondestructive assessment of component quality and mechanical properties. Additionally, this study illustrates the application of machine learning to address a classification problem, specifically the categorization of pores into gas pores and lack of fusion pores. Although it was shown that internal defects exhibited a reduced impact on fatigue behavior compared with surface defects, it was noted that surface defects exert a higher influence on fatigue behavior. A machine learning algorithm was developed to predict the fatigue life using surface defect features as input parameters.</p>","PeriodicalId":12298,"journal":{"name":"Fatigue & Fracture of Engineering Materials & Structures","volume":"47 9","pages":"3392-3407"},"PeriodicalIF":3.1,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}