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Autonomous sensor nodes, enabled by the integration of energy harvesting principles, create the preconditions for a significant simplification and mass reduction in aircraft structural health monitoring. A promising energy harvesting approach to collect and convert the energy from random aircraft vibrations during operation, is to use piezoelectric energy harvesters. Due to the narrow operating frequency bandwidth of the considered harvesters, a frequency up-conversion mechanism, based on plucking the free end of the piezoelectric devices by using rotating plectra, and letting the transducers oscillate at their eigenfrequencies, is introduced in order to enable their utilization while operating in the considered random excitation environment. An experimental investigation of fused deposition modelling 3D printed plectra is therefore carried on to better understand the effects of the respective design parameters, i.e., their size, stiffness and material types, on the resulting performances of the piezoelectric energy harvesters in terms of the obtainable specific power outputs. The voltages generated by the harvesters, as well as their maximal free end deflections, are thus measured for different plectrum materials, different plucking frequencies and different geometries of the plectra. Design-of-experiments (DoE) methods are employed in the setup of the experiments, whereas advanced numerical algorithms are used for the analysis of the hence obtained results. A comprehensive insight into the effects of plectrum properties on the responses of the piezoelectric energy harvesters is therefore provided, which represents another step towards the design of autonomous energy harvester powered sensor nodes to be used in aircraft structural health monitoring applications.

Digital Image Correlation (DIC) experimental method is receiving a significant attention recently both in research and industry since it enables full-field, non-contact optical measurements of deformations and strains on almost any material and any geometry [1, 2]. In this study, DIC measurement system was used for determination and analysis of the values of strains and stresses in plate specimens with central hole loaded elastically in tension using a tensile testing machine. In order to determine the applicability of the method and the sizes of geometrical discontinuities i.e., stress concentrators that can be successfully characterized with available equipment, plate specimens made of various materials and with central holes of different sizes were studied. An important step in preparation of the specimens, which can have significant influence on results, is creation of the appropriate random speckle pattern on the specimen’s surface which is usually done by spraying paint [3]. In this work, speckle pattern was applied using airbrush system and influence of speckle pattern on measurement results was considered. Values of strains and stresses on individual specimens were determined using basic empirical equations, numerically with the FEA software and compared with those determined experimentally using the DIC system. Results of the study are reported.

In the process of designing a part or a component, computer simulations are extensively used as they test the intended function of a product. Complex load conditions need to be adequately represented in simulations to make reliable estimations of the behavior of a structure. The material of a structure influences its mechanical behavior and is defined through various material models. The choice of the material model will depend on the conditions in which the structure will be used. After choosing a material model, user must define model parameters or use libraries with available material data of common materials. As not all material parameters can easily be acquired from experiments, data-driven models are used to find relationships between desired material parameters and the available ones. Data-driven models or, more specifically, machine learning (ML) models enable detecting patterns and extracting new relationships between material parameters which could not be discovered using the classical empirical models [1]. ML models need to be built on a sufficient amount of data, yet a common problem concerning material characterization is small low-dimensional data sets [2]. To overcome this problem, important input variables need to be detected by using feature selection techniques [3]. Furthermore, material properties from existing material databases need to be collected and used in building ML models to increase the number of learning examples [2]. These models should enable the end-user to efficiently design a product without the need to carry out time-consuming experimental material testing.

To adequately design a structure or a part, the behavior of a material, more precisely, relation between its stresses and strains, must be known. Considering that the experimental determination of fatigue and cyclic material parameters is costly and long-lasting, as opposed to monotonic tensile tests, it is of great interest to use more easily obtainable monotonic properties to estimate cyclic and fatigue material behavior [1]. Building a predictive model from acquired data can be done using classical approaches, such as regression, or more recently, various available machine learning methods. The dataset which is used as an input for such models needs to be of appropriate size and have an adequate number of input variables, also called predictors, to avoid underfitting or overfitting a model. Higher ratio of number of samples to number of predictors makes the model less likely to be affected by possible errors in data and to generalize new cases well [2]. To increase the data volume, additional datasets can be acquired by performing experiments which take a great amount of time. Therefore, it is more economical to implement a feature selection (i.e. feature engineering) techniques instead, which enable the detection of redundant input variables followed by their removal which then consequently reduces model complexity [2]. In this study, using several chosen feature selection techniques, importance of each predictor in relation with the response is determined and a subset of the most relevant variables for predicting the cyclic and fatigue parameters is selected. Building models with newly acquired subset should reduce overfitting, improve interpretability and decrease the complexity of the model.

Rad ne sadrži sažetak.

U okviru ovog diplomskog rada opisano je stanje tehnike na području prikupljanja i pretvorbe niskorazinske energije iz okoliša s naglaskom na piezoelektrične pretvarače za primjene u nosivim tehnologijama. Analizirani su mogući načini proširenja raspona primjene piezoelektričnih pretvarača s fokusom na povećanju frekvencijskog raspona rada pretvarača mehaničkim trzanjem. U eksperimentalnom dijelu rada, ispitan je utjecaj krutosti trzalica namijenjenih za mehaničko trzanje na odziv piezoelektrične konzole u obliku generiranog napona i progiba konzole. Predstavljena su dva konceptualna rješenja rotora s trzalicama koji se razlikuju u broju trzalica postavljenih oko osi rotora. Nakon toga, koristeći koncept rotora s dvije trzalice, utvrđuje se matematička veza odziva piezoelektričnog pretvarača s različitim čimbenicima kao što su materijal trzalice, frekvencija mehaničkog trzanja i geometrijske karakteristike trzalice.

Konstruirano je prijenosno ručno vitlo koje se može primjenjivati za spuštanje i izvlačenje čamaca ili drugih tereta. U uvodu je objašnjen način funkcioniranja i prikazani su osnovni dijelovi vitla. Nakon toga napravljena je analiza tržišta te je prikazano nekoliko koncepcijskih rješenja koja bi odgovarala zadanom slučaju. Odabran je prijenosni omjer, dužina povlačenja i nosivost. Proračun konstrukcije podijeljen je na tri dijela i započinje proračunom mehanizma za prihvat tereta. Odabrano je uže, kuka i ručica za povlačenje tereta. Zatim se dimenzionira bubanj, vratilo, ležajevi i ostali pripadajući dijelovi. Na kraju su proračunati zupčanici i napravljena je kontrola naprezanja. Objašnjen je odabir svih standardnih dijelova, a nestandardni su dimenzionirani i prikazani. Model vitla izrađen je u programskom paketu CATIA V5 te je napravljen sklopni crtež vitla i radionički crtež odabranih dijelova.