Slender marine risers used in deepwater applications often experience vortex-induced vibration (VIV). Fatigue damage associated with VIV is of great concern to offshore engineers; however, it has proven difficult to predict this fatigue damage using existing semi-empirical tools. Similarly, approaches based on theoretical and computational fluid dynamics (CFD) generally rely on simplified assumptions on the fluid flow fields and response characteristics. To gain an understanding of VIV and associated fatigue damage, full-scale field monitoring campaigns as well as reduced-scale laboratory experiments are often carried out, wherein the riser response in the form of strains and/or accelerations is recorded using an array of a limited number of sensors distributed over the length of the riser. Simultaneously, current velocities at a proximate location are also recorded. Such measurements generally reveal complex characteristics of the dynamic response of a riser undergoing VIV, including the presence of multiple vibration harmonics, non-stationary behavior, and the existence of sustained or intermittent traveling wave patterns. Such complex features, often not accounted for in some semi-empirical and theoretical approaches, are critical to take into consideration for accurate fatigue damage estimation. In this study, several empirical methods are employed to first reconstruct the response of an instrumented riser and, then, estimate fatigue damage rates over the entire span of the riser based on a limited number of discrete measurements. The methods presented employ the measured data in different ways. One method, referred to as ``weighted waveform analysis'' relies on expressing the riser response as a summation of several weighted waveforms or riser modes; the mode shapes are ``assumed'' and time-varying weights for each mode are estimated directly from the measurements. The riser response over the entire span is reconstructed based on these assumed mode shapes and estimated modal weights. Other methods presented extract discrete mode shapes from the data directly. With the help of interpolation techniques, continuous mode shapes are formed, and the riser response is again reconstructed. Fatigue damage rates estimated based on the reconstructed strains obtained using the various empirical methods are cross-validated by comparing predictions against direct measurements available at the same locations (but not used in the analyses). Results show that the empirical methods developed here may be employed to accurately estimate fatigue damage rates associated with individual recorded segments of measurements. Finally, a procedure for prediction of long-term fatigue damage rates of an instrumented marine riser is presented that relies on combining (multiplying) the fatigue damage rates associated with short recorded segments for specific current profile types, with the relative likelihood of different incident current profiles, and integration over all current profiles. It should be noted that the empirical approaches to fatigue damage estimation presented in this study are based only on measured data; also, they explicitly account for different riser response characteristics and for site-specific current profiles developed from metocean studies. Importantly, too, such estimation procedures can easily accommodate additional data that become available in any ongoing field monitoring campaign to improve and update long-term fatigue damage prediction. / text

Ultrasonic welding is one of the leading technologies for joining multiple, thin sheets of dissimilar materials, such as copper and aluminum, for automotive lithium-ion batteries. The performance of ultrasonic welds, particularly the fatigue life, however, has not been well studied. In this work, a theoretical fatigue life model for ultrasonically welded joints was developed using continuum damage mechanics. In the model, the damage variable was defined as a function of the increase of the joint electrical resistance, resulting in an electrical resistance-based fatigue life prediction model. The fatigue model contains two constants to be determined with experimental data, depending on different fatigue loads and joint properties. As an application, the fatigue life model was validated for Al-Cu lithium-ion battery tab joints. Mechanical fatigue tests were performed under various stress loading conditions for welds made using different welding parameters. It is shown that the developed model can be used to predict the remaining life of the ultrasonically welded battery tab joints for electric and hybrid electric vehicles by monitoring the electrical resistance change. In addition, thermal and electrical fatigue tests were performed for Al-Cu battery tab welds using simulated operating conditions of electrical vehicles. These included temperature cycling between -40 and 90 °C and current cycling of 0 to 10 A. All the tests were conducted on individual weld joints. The results showed that the thermal and electrical loads imposed insignificant effect on the electrical resistance of the battery tab joints. / text

Fatigue failure is one of the most critical issues in industry nowadays as 60 to 90 percent of failures in metals are due to fatigue. Therefore, different methods and approaches are developed to estimate the fatigue life of metallic parts. In this research, a case-hardened steel splined shaft is studied to estimate the fatigue life that the shaft will withstand before failure. The purpose of the research is to develop an advanced fatigue prediction method for splines.A static experimental test was performed on the splined shaft for analyzing the load-strain behavior of the shaft and determining the suitable load cases of the study. A dynamic test of pure torsional load was carried out to collect experimental results for validating the generated fatigue methods and investigating the failure behavior of the shaft. Stress analysis was performed on the part for investigating critical areas and the effect of the different spline teeth designs on the resulting stress. Two finite element models were analyzed using two software, MSC Marc software with a geometry of straight spline teeth and Spline LDP with an involute spline teeth model. DIN 5466-1 spline standard’s analytical solution was used for verification purposes. Stress and strain-based approaches were used to estimate fatigue life. The most suitable method was evaluated against experimental test results.The research findings show that the most critical stress areas on the shaft are the spline root fillet and relief. When the part fails due to fatigue the crack initiates at the root fillet and propagates to the relief. It is also shown that involute teeth spline gives higher stress than straight teeth for the same load due to less contact area.The conclusion of the research could be summarized in: the stress-based method (Wöhler curve) is giving good accuracy and proved a reliable method. While among six different approaches used of strain-based methods, four-point correlation method is giving the best correlation to test results. Hence, it is recommended to use four-point correlation method for fatigue analysis for its accuracy and for considering both elastic and plastic behavior of the material.

Localized muscular loads have in many cases replaced whole body loads in the current mechanized industry. In highly automated automobile industries, the prevalence of upper extremity musculoskeletal disorders is a matter of continuing concern. Overhead work has especially been noted for its association with shoulder related musculoskeletal disorders. Research aimed at determining causal relationships between overhead work and risk of injury has increasingly used localized muscle fatigue as an indirect or surrogate measure. In this study, localized muscle fatigue was used as a primary measure for studying the effects of workload level while performing overhead work. Subjective (ratings of perceived discomfort) measures of fatigue were collected and their predictive potential was investigated. Effect of personality type was also examined to account for any inter-individual differences in fatigue perception. While researchers have studied specific task conditions in controlled environments, the specific relationship between various risk factors and underlying injury mechanisms is largely unknown. Two main problems faced by researchers are limited resources and the large scope of potential ergonomic analyses. This study attempted to circumvent some of these limitations by examining the time-course of fatigue and the predictive potential of subjective measures. The feasibility of using shorter experimental durations to make deductions for a 2-hour work period was explored. Reductions in experimental duration means decreased experimental time, expenses and resources. Thus, in turn, the researcher can utilize available resources to study more factors and a more general scenario. Specifically, subjective measures of shoulder fatigue were used to determine the possibility of reducing experimental duration for an intermittent overhead task. A laboratory-simulated intermittent overhead task was designed based on observations made at an automotive assembly unit. For this study, two treatment conditions were tested consisting of different combinations of two tool masses and two duty cycles. The choice of the treatment conditions was made to simulate different task difficulty levels of occupational tasks and their effects on shoulder fatigue. Each experiment was conducted for 2 hours (a common duration in industries with job rotation) for these selected treatment conditions. Subjective measures of fatigue were collected to assess shoulder fatigue and relative acceptability of the overhead work. Any observed trends in the subjective fatigue measure were determined and tested using statistical and mathematical models to determine how best to represent their salient characteristics. Derived qualitative and quantitative measures were also used to estimate the maximal acceptable task durations using certain formalized assessment techniques. Results of this research suggest possible reductions in the experimental duration. Short (8 to 26 minute) trials were found to be sufficient to predict performance measures for 2 hours. Results also indicated a strong influence of task difficulty level on the predictive performance of subjective measures though personality type did not show very consistent trends. Various unique analysis techniques used to look at the psychophysical data may prove useful for further investigation into predictive verification. A generalized mathematical model, a type of approach, was also developed to represent changes in the psychophysical measures over time. This research can find both industrial and research applications where resources are constrained and using psychophysical measures is feasible. In the following report, details on this work are presented, including a description of the factors that inspired this study, an outline of the relevant literature, methodology, results and their implications. / Master of Science

Monitoring of physical exercise is an important task to evaluate and adapt exercise to provide better exercise results. The Inno-X™ device, developed by Innowearable, is a device that can be used for such monitoring. It collects data using an accelerometer and sEMG sensor. To optimize Inno-X™, this Thesis investigates how raw data from the sensors can be used to classify physical exercises and fatigue levels using machine learning. The exercises that were monitored and evaluated were cycling and squats. The workflow includes data collection, preprocessing, feature extraction and finetuning of the models. Participants engage in standardized exercise protocols to ensure reliable data. Under preprocessing, the data is scaled and filtered followed by feature extraction where time-domain and frequency-domain attributes are analyzed. Three classifiers, Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM), are evaluated for their performance in fatigue detection and exercise classification. Results reveal reliable accuracy across all classifiers, with SVM demonstrating the most effective performance in fatigue detection, with an accuracy of 79.5%. The classification accuracy for the executed exercises surpassed 97% for all three employed models. In conclusion, this Thesis offers insights into the application of machine learning for exercise classification and fatigue prediction. The established data processing pipeline and the performance of the chosen classifiers indicate a potential application of these methods into real-world scenarios for precise exercise monitoring and fatigue management. / Detta examensarbete fokuserar främst på användningen av avancerademaskininlärningstekniker. Fokuspunkterna inkluderar klassificering av övningaroch förutsägelse av muskeltrötthet under träningspass, med hjälp av data somsamlats in från Inno-X-enheten av Innowearable AB.Studien innebär noggrann bearbetning, med insamling, förbehandling,extrahering av funktioner och klassificering. Deltagarna deltar i standardiseradeträningsprotokoll. Uppgifterna genomgår noggrann förbehandling, följt avfunktionsextraktion, där tidsdomän- och frekvensdomänattribut analyseras.Anmärkningsvärda funktioner som medeleffekt, total effekt, medelfrekvens,medelfrekvens och maxfrekvens bidrar till effektiviteten hosmaskininlärningsmodellerna.Tre klassificerare, Random Forest (RF), Support Vector Machine (SVM) och LongShort-Term Memory (LSTM), utvärderas för deras prestanda vidträningsklassificering och upptäckt av trötthet. Resultaten visar tillförlitlignoggrannhet för alla klassificerare, där SVM uppvisar den mest effektivaprestandan när det gäller att upptäcka trötthet för cykling och knäböj.Sammanfattningsvis ger denna avhandling insikt in i tillämpningen avmaskininlärning för träningsklassificering och förutsägelse av trötthet. Denetablerade pipelinen för databehandling och den rimliga prestandan hos de valdaklassificerarna indikerar en potentiell tillämpning av dessa metoder i verkligascenarier för exakt träningsövervakning och hantering av trötthet.

De nos jours, il est crucial pour les industries de réduire leur consommation énergétique. Pour les industries du transport, cela peut se faire par le biais de l’allègement des pièces de structure. Dans ce contexte, les structures cellulaires représentent une des solutions les plus prometteuses. Grâce au développement de la fabrication additive, l’élaboration de telles géométries complexes n’est plus un frein à leur utilisation. Néanmoins, cette dernière restera limitée tant que les propriétés mécaniques – et plus particulièrement la résistance en fatigue pour les pièces aéronautiques – ne seront pas maîtrisées. Ce travail de thèse a pour objectif de déterminer les mécanismes qui gouvernent le comportement en fatigue de ces structures cellulaires. Pour cela, le travail s’est focalisé sur l’élement unitaire les constituant : la poutre. Des éprouvettes minces représentatives de la poutre ont été élaborées par Electron Beam Melting puis caractérisées à l’état brut de fabrication à l’aide de différentes techniques (tomographie aux rayons X, microscopie optique et électronique, …). Leurs propriétés statique et cyclique en traction ont ensuite été évaluées. L’état de surface et en particulier les défauts d’entaille ont été identifiés comme responsable de la perte de résistance. L’impact de ces défauts sur la résistance en fatigue a été prédit avec succès par le biais de diagrammes de Kitagawa. L’impact de l’orientation de fabrication a également été observé et prédit. Différents post-traitements ont ensuite été utilisés afin d’améliorer ces propriétés. Le polissage chimique et le grenaillage ultrasonique ont réduit de manière significative la criticité des défauts de surface ce qui a grandement amélioré les propriétés mécaniques des éprouvettes, jusqu’à se rapprocher de celles obtenues après usinage. Par ailleurs, la compression isostatique à chaud a provoqué la fermeture de l’entièreté des défauts internes ainsi qu’un grossissement de la microstructure. Ce dernier point permet une amélioration supplémentaire de la performance en fatigue une fois combiné avec un traitement de surface. Enfin, une méthode permettant de détecter automatiquement tous les défauts d’entailles et de déterminer leur criticité et leur influence sur la résistance en fatigue a été proposée et discutée. Elle a ensuite été appliquée avec succès aux échantillons attaqués chimiquement mais des modifications demeurent nécessaire pour l’appliquer à d’autres états de surface. / Nowadays, reducing the energy consumption is crucial for most of the industries. For transportation industries, it can be achieved through weight reduction. In this context, cellular structures turn out to be one of the most efficient solution. Thanks to the development of additive manufacturing, producing such complex geometries is no longer an issue. However, their use will remain limited as long as their fatigue performances are not known. This PhD work aimed at understanding the mechanisms that govern the fatigue behaviour of such cellular structures. It was first decided to focus on their unitary element, i.e. a single strut. Single struts samples were manufactured by Electron Beam Melting and then characterized in as-built conditions using different experimental techniques (X-ray tomography, optical and electron microscopy, etc.). Their static and cyclic tensile properties were then evaluated. The rough surface and in particular notch-like defects were found to be responsible for the knockdown of the mechanical properties. Regarding the fatigue resistance, their detrimental impact was predicted using Kitagawa diagrams. It also enabled to explain the impact of the build orientation. Different post-treatments were used in order to improve these mechanical properties. Chemical etching and ultrasonic shot peening (USP) significantly reduced the severity of surface defects of as-built thin struts and thus increased their mechanical properties. After USP, the fatigue properties of machined samples were almost matched. Hot Isostatic Pressing lead to the closure of all internal defects and to the coarsening of the microstructure. When combined with one of the surface treatments, the fatigue properties were further improved. Finally, a method enabling to systematically and automatically extract from the surface the most critical defects and quantitatively analyze their influence on fatigue life was proposed and discussed. It was successfully applied to chemical etched samples but improvements are mandatory for other surface conditions.