Noise and vibration damping of Fe-Cr-x alloys

The aim of the present work is to study the noise and vibration damping capacity of ferromagnetic Fe-16%Cr base alloys (before and after heat treatment) with different Al and Mo contents. The noise damping was evaluated by the level of sound emission after an impact. The vibration damping was studied using a cantilever device. In addition to these tests, the magnetic structure of the materials was also investigated by Kerr effect. It was verified that the materials can decrease noise level in the frequency range of human earring. The vibration damping is influenced by heat treatment and chemical composition of the alloy. The improvement of vibration damping after heat treatment is ascribed to the decrease of internal stresses in materials and changes in magnetic domain structures.

Characterization Of Process Efficiency Improvement In Laser Additive Manufacturing

Laser additive manufacturing (LAM), known also as 3D printing, has gained a lot of interest in past recent years within various industries, such as medical and aerospace industries. LAM enables fabrication of complex 3D geometries by melting metal powder layer by layer with laser beam. Research in laser additive manufacturing has been focused in development of new materials and new applications in past 10 years. Since this technology is on cutting edge, efficiency of manufacturing process is in center role of research of this industry. Aim of this thesis is to characterize methods for process efficiency improvements in laser additive manufacturing. The aim is also to clarify the effect of process parameters to the stability of the process and in microstructure of manufactured pieces. Experimental tests of this thesis were made with various process parameters and their effect on build pieces has been studied, when additive manufacturing was performed with a modified research machine representing EOSINT M-series and with EOS EOSINT M280. Material used was stainless steel 17-4 PH. Also, some of the methods for process efficiency improvements were tested. Literature review of this thesis presents basics of laser additive manufacturing, methods for improve the process efficiency and laser beam – material- interaction. It was observed that there are only few public studies about process efficiency of laser additive manufacturing of stainless steel. According to literature, it is possible to improve process efficiency with higher power lasers and thicker layer thicknesses. The process efficiency improvement is possible if the effect of process parameter changes in manufactured pieces is known. According to experiments carried out in this thesis, it was concluded that process parameters have major role in single track formation in laser additive manufacturing. Rough estimation equations were created to describe the effect of input parameters to output parameters. The experimental results showed that the WDA (width-depth-area of cross-sections of single track) is correlating exponentially with energy density input. The energy density input is combination of the input parameters of laser power, laser beam spot diameter and scan speed. The use of skin-core technique enables improvement of process efficiency as the core of the part is manufactured with higher laser power and thicker layer thickness and the skin with lower laser power and thinner layer thickness in order to maintain high resolution. In this technique the interface between skin and core must have overlapping in order to achieve full dense parts. It was also noticed in this thesis that keyhole can be formed in LAM process. It was noticed that the threshold intensity value of 106 W/cm2 was exceeded during the tests. This means that in these tests the keyhole formation was possible.

Exit times for some processes with normally distributed noise

Denna avhandling handlar om metoder för att hitta begränsningar för det asymptotiska beteendet hos en förväntad uthoppstid från ett område omkring en xpunkt för processer som har normalfördelad störning. I huvudsak behandlas olika typer av autoregressiva processer. Fyra olika metoder används. En metod som använder principen för stora avvikelser samt en metod som jämför uthoppstiden med en återkomsttid ger övre begränsningar för den förväntade uthoppstiden. En martingalmetod och en metod för normalfördelade stokastiska variabler ger undre begränsningar. Metoderna har alla både förtjänster och nackdelar. Genom att kombinera de olika metoderna får man de bästa resultaten. Vi får fram gränsvärdet för det asymptotiska beteendet hos en uthoppstid för den multivariata autoregressiva processen, samt motsvarande gränsvärde för den univariata autoregressiva processen av ordning n.

Fabrication additive ou Impression 3D. Aperçus sur une variation terminographique

Materiaalia lisäävä valmistus eli 3D-tulostus on valmistusmenetelmä, jossa kappale tehdään 3D-mallin pohjalta materiaalikerroksia lisäämällä, käyttäen useita tekniikoita ja materiaaleja. Menetelmää sovelletaan useilla teollisuuden aloilla. Lisääviä valmistustekniikoita on kehitetty 1990-luvun alkupuolelta lähtien, ja ne monipuolistuvat jatkuvasti. Tässä pro gradu -tutkielmassa tutkitaan sovellusalan terminologian kehitystä vertailevilla menetelmillä ja luodaan kolmikielinen sanasto alan asiantuntijoille, joita edustaa Suomessa FIRPA ry. Sanaston kielet ovat englanti, ranska ja suomi. Terminologian tutkimus on perinteisesti keskittynyt sanastotyöhön ja käsiteanalyysiin, sen sijaan termihistorian tutkimus on ollut vähäisempää. Tässä työssä on tehty vertailevaa termitutkimusta sekä sanastotyön että termihistorian näkökulmista. Vertailutasoja ovat termien merkityksen muuttuminen, vertailu pivot-kielen suhteen ja kielikohtaisten ominaisuuksien tarkastelu termien muotoutumisessa. Tutkittavia asioita ovat sanastokäsitteiden väliset suhteet, synonyymien, varianttien ja uudissanojen moninaisuus, ja termien yleiskielistyminen. Samalla pohditaan muita termien muuttumiseen vaikuttavia syita. Tärkeimpänä lähteenä käytetään Wohlersin vuosiraportteja, jotka kuvaavat kattavasti koko teollisuudenalaa. Koska englannin pivot-vaikutus on voimakasta teknisillä aloilla, omankielisen terminologian kehittyminen vaatii tietoista terminologiatyötä ja aktiivista omankielisten termien käyttöä. Terminologian vakiintumista voidaan arvioida termivarianttien ja uudissanojen määristä, sekä termien yleiskielistymisestä. Terminologia muuttuu jatkuvasti toimialan kehittyessä ja vaatii säännöllistä päivittämistä. Termihistorian tunteminen tukee sanastotyön termivalintoja. Alan asiantuntijat ovat vastuussa omasta terminologiastaan, ja heidän aktiivisuutensa on tärkeää sen kehittämisessä. Toteutettu sanasto on tämän pro gradu -tutkielman liitteenä ja se julkaistaan myös FIRPA ry:n Internet-sivustolla. Suomenkielinen osio sanastosta on ensimmäinen laaja suomeksi julkaistu materiaalia lisäävän valmistuksen sanasto.

Energy and raw material consumption analysis of powder bed fusion: case study: CNC machining and laser additive manufacturing

Laser additive manufacturing (LAM), known also as 3D printing, is a powder bed fusion (PBF) type of additive manufacturing (AM) technology used to manufacture metal parts layer by layer by assist of laser beam. The development of the technology from building just prototype parts to functional parts is due to design flexibility. And also possibility to manufacture tailored and optimised components in terms of performance and strength to weight ratio of final parts. The study of energy and raw material consumption in LAM is essential as it might facilitate the adoption and usage of the technique in manufacturing industries. The objective this thesis was find the impact of LAM on environmental and economic aspects and to conduct life cycle inventory of CNC machining and LAM in terms of energy and raw material consumption at production phases. Literature overview in this thesis include sustainability issues in manufacturing industries with focus on environmental and economic aspects. Also life cycle assessment and its applicability in manufacturing industry were studied. UPLCI-CO2PE! Initiative was identified as mostly applied exiting methodology to conduct LCI analysis in discrete manufacturing process like LAM. Many of the reviewed literature had focused to PBF of polymeric material and only few had considered metallic materials. The studies that had included metallic materials had only measured input and output energy or materials of the process and compared to different AM systems without comparing to any competitive process. Neither did any include effect of process variation when building metallic parts with LAM. Experimental testing were carried out to make dissimilar samples with CNC machining and LAM in this thesis. Test samples were designed to include part complexity and weight reductions. PUMA 2500Y lathe machine was used in the CNC machining whereas a modified research machine representing EOSINT M-series was used for the LAM. The raw material used for making the test pieces were stainless steel 316L bar (CNC machined parts) and stainless steel 316L powder (LAM built parts). An analysis of power, time, and the energy consumed in each of the manufacturing processes on production phase showed that LAM utilises more energy than CNC machining. The high energy consumption was as result of duration of production. Energy consumption profiles in CNC machining showed fluctuations with high and low power ranges. LAM energy usage within specific mode (standby, heating, process, sawing) remained relatively constant through the production. CNC machining was limited in terms of manufacturing freedom as it was not possible to manufacture all the designed sample by machining. And the one which was possible was aided with large amount of material removed as waste. Planning phase in LAM was shorter than in CNC machining as the latter required many preparation steps. Specific energy consumption (SEC) were estimated in LAM based on the practical results and assumed platform utilisation. The estimated platform utilisation showed SEC could reduce when more parts were placed in one build than it was in with the empirical results in this thesis (six parts).

Metal additive manufacturing of internal flow channels with powder bed fusion processes

This thesis studies the advantages, disadvantages and possibilities of additive manufacturing in making components with internal flow channels. These include hydraulic components, components with cooling channels and heat exchangers. Processes studied in this work are selective laser sintering and selective laser melting of metallic materials. The basic principles of processes and parameters involved in the process are presented and different possibilities of internal channel manufacturing and flow improvement are introduced

Effect of scanner acoustic background noise on strict resting-state fMRI

Functional MRI (fMRI) resting-state experiments are aimed at identifying brain networks that support basal brain function. Although most investigators consider a ‘resting-state’ fMRI experiment with no specific external stimulation, subjects are unavoidably under heavy acoustic noise produced by the equipment. In the present study, we evaluated the influence of auditory input on the resting-state networks (RSNs). Twenty-two healthy subjects were scanned using two similar echo-planar imaging sequences in the same 3T MRI scanner: a default pulse sequence and a reduced “silent” pulse sequence. Experimental sessions consisted of two consecutive 7-min runs with noise conditions (default or silent) counterbalanced across subjects. A self-organizing group independent component analysis was applied to fMRI data in order to recognize the RSNs. The insula, left middle frontal gyrus and right precentral and left inferior parietal lobules showed significant differences in the voxel-wise comparison between RSNs depending on noise condition. In the presence of low-level noise, these areas Granger-cause oscillations in RSNs with cognitive implications (dorsal attention and entorhinal), while during high noise acquisition, these connectivities are reduced or inverted. Applying low noise MR acquisitions in research may allow the detection of subtle differences of the RSNs, with implications in experimental planning for resting-state studies, data analysis, and ergonomic factors.

Characterization of design of a product for additive manufacturing

The purpose of conducting this thesis is to gather around information about additive manufacturing and to design a product to be additively manufactured. The specific manufacturing method dealt with in this thesis, is powder bed fusion of metals. Therefore when mentioning additive manufacturing in this thesis, it is referred to powder bed fusion of metals. The literature review focuses on the principle of powder bed fusion, the general process chain in additive manufacturing, design rules for additive manufacturing. Examples of success stories in additive manufacturing and reasons for selecting parts to be manufactured with additive manufacturing are also explained in literature review. This knowledge is demanded to understand the experimental part of the thesis. The experimental part of the thesis is divided into two parts. Part A concentrates on finding proper geometry for building self-supporting pipes and proper parameters for support structures of them. Part B of the experimental part concentrates on a case study of designing a product for additive manufacturing. As a result of experimental part A, the design process of self-supporting pipes, results of visual analysis and results of 3D scanning are presented. As a result of experimental part B the design process of the product is presented and compared to the original model.

Monitoring Directed Energy Deposition Processes in Additive Manufacturing

The purpose of this study is to find out how laser based Directed Energy Deposition processes can benefit from different types of monitoring. DED is a type of additive manufacturing process, where parts are manufactured in layers by using metallic powder or metallic wire. DED processes can be used to manufacture parts that are not possible to manufacture with conventional manufacturing processes, when adding new geometries to existing parts or when wanting to minimize the scrap material that would result from machining the part. The aim of this study is to find out why laser based DED-processes are monitored, how they are monitored and what devices are used for monitoring. This study has been done in the form of a literature review. During the manufacturing process, the DED-process is highly sensitive to different disturbances such as fluctuations in laser absorption, powder feed rate, temperature, humidity or the reflectivity of the melt pool. These fluctuations can cause fluctuations in the size of the melt pool or its temperature. The variations in the size of the melt pool have an effect on the thickness of individual layers, which have a direct impact on the final surface quality and dimensional accuracy of the parts. By collecting data from these fluctuations and adjusting the laser power in real-time, the size of the melt pool and its temperature can be kept within a specified range that leads to significant improvements in the manufacturing quality. The main areas of monitoring can be divided into the monitoring of the powder feed rate, the temperature of the melt pool, the height of the melt pool and the geometry of the melt pool. Monitoring the powder feed rate is important when depositing different material compositions. Monitoring the temperature of the melt pool can give information about the microstructure and mechanical properties of the part. Monitoring the height and the geometry of the melt pool is an important factor in achieving the desired dimensional accuracy of the part. By combining multiple different monitoring devices, the amount of fluctuations that can be controlled will be increased. In addition, by combining additive manufacturing with machining, the benefits of both processes could be utilized.

Psychopathy and self-monitoring : additive and interactive effects on self-presentation tactics /

The purpose of this study was to determine the relative contributions of psychopathy and self-monitoring to the prediction of self-presentation tactics (behaviours that individuals use to manipulate their self-image). Psychopathy is composed of two main factors: Factor 1, which includes manipulativeness and shallow affect, and Factor 2, which includes irresponsibility and anti-social behaviours. Self-monitoring is a personality trait that distinguishes between those who adapt their behaviour to fit different social situations (high self-monitors) and those who behave as they feel regardless of social expectations (low selfmonitors). It was hypothesized that self-monitoring would moderate the relationship between psychopathy and self-presentation tactics. One hundred and forty-nine university students completed the Self-Monitoring Scale (Snyder, 1974), the Self-Report Psychopathy Scale - Version III (Paulhus et aI., in press), the Self-Presentation Tactics scale (Lee, S., et aI., 1999), the HEXACO-PI (a measure ofthe six major factors of personality; Lee, K., & Ashton, 2004), and six scenarios that were created as a supplementary measure of the selfpresentation tactics. Results of the hierarchical multiple regression analyses showed that self-monitoring did moderate the relationship between psychopathy and three of the selfpresentation tactics: apologies, disclaimers, and exemplification. Further, significant interactions were observed between Factor 1 and self-monitoring on apologies and the defensive tactics subscale, between Factor 2 and self-monitoring on self-handicapping, and between Factor 1 and Factor 2 on exemplification. Contrary to expectations, the main effect of self-monitoring was significant for the prediction of nine tactics, while psychopathy was significant for the prediction of seven tactics. This indicates that the role of these two personality traits in the explanation of self-presentation tactics tends to be additive in nature rather than interactive. In addition. Factor 2 alone did not account for a significant amount of variance in any of the tactics, while Factor 1 significantly predicted nine tactics. Results are discussed with regard to implications and possible directions for future research.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Sequential Machine learning Approaches for Portfolio Management

Cette thèse envisage un ensemble de méthodes permettant aux algorithmes d'apprentissage statistique de mieux traiter la nature séquentielle des problèmes de gestion de portefeuilles financiers. Nous débutons par une considération du problème général de la composition d'algorithmes d'apprentissage devant gérer des tâches séquentielles, en particulier celui de la mise-à-jour efficace des ensembles d'apprentissage dans un cadre de validation séquentielle. Nous énumérons les desiderata que des primitives de composition doivent satisfaire, et faisons ressortir la difficulté de les atteindre de façon rigoureuse et efficace. Nous poursuivons en présentant un ensemble d'algorithmes qui atteignent ces objectifs et présentons une étude de cas d'un système complexe de prise de décision financière utilisant ces techniques. Nous décrivons ensuite une méthode générale permettant de transformer un problème de décision séquentielle non-Markovien en un problème d'apprentissage supervisé en employant un algorithme de recherche basé sur les K meilleurs chemins. Nous traitons d'une application en gestion de portefeuille où nous entraînons un algorithme d'apprentissage à optimiser directement un ratio de Sharpe (ou autre critère non-additif incorporant une aversion au risque). Nous illustrons l'approche par une étude expérimentale approfondie, proposant une architecture de réseaux de neurones spécialisée à la gestion de portefeuille et la comparant à plusieurs alternatives. Finalement, nous introduisons une représentation fonctionnelle de séries chronologiques permettant à des prévisions d'être effectuées sur un horizon variable, tout en utilisant un ensemble informationnel révélé de manière progressive. L'approche est basée sur l'utilisation des processus Gaussiens, lesquels fournissent une matrice de covariance complète entre tous les points pour lesquels une prévision est demandée. Cette information est utilisée à bon escient par un algorithme qui transige activement des écarts de cours (price spreads) entre des contrats à terme sur commodités. L'approche proposée produit, hors échantillon, un rendement ajusté pour le risque significatif, après frais de transactions, sur un portefeuille de 30 actifs.

Analyse par apprentissage automatique des réponses fMRI du cortex auditif à des modulations spectro-temporelles

L'application de classifieurs linéaires à l'analyse des données d'imagerie cérébrale (fMRI) a mené à plusieurs percées intéressantes au cours des dernières années. Ces classifieurs combinent linéairement les réponses des voxels pour détecter et catégoriser différents états du cerveau. Ils sont plus agnostics que les méthodes d'analyses conventionnelles qui traitent systématiquement les patterns faibles et distribués comme du bruit. Dans le présent projet, nous utilisons ces classifieurs pour valider une hypothèse portant sur l'encodage des sons dans le cerveau humain. Plus précisément, nous cherchons à localiser des neurones, dans le cortex auditif primaire, qui détecteraient les modulations spectrales et temporelles présentes dans les sons. Nous utilisons les enregistrements fMRI de sujets soumis à 49 modulations spectro-temporelles différentes. L'analyse fMRI au moyen de classifieurs linéaires n'est pas standard, jusqu'à maintenant, dans ce domaine. De plus, à long terme, nous avons aussi pour objectif le développement de nouveaux algorithmes d'apprentissage automatique spécialisés pour les données fMRI. Pour ces raisons, une bonne partie des expériences vise surtout à étudier le comportement des classifieurs. Nous nous intéressons principalement à 3 classifieurs linéaires standards, soient l'algorithme machine à vecteurs de support (linéaire), l'algorithme régression logistique (régularisée) et le modèle bayésien gaussien naïf (variances partagées).

Toward a rational-choice foundation of non-additive theories

A classical argument of de Finetti holds that Rationality implies Subjective Expected Utility (SEU). In contrast, the Knightian distinction between Risk and Ambiguity suggests that a rational decision maker would obey the SEU paradigm when the information available is in some sense good, and would depart from it when the information available is not good. Unlike de Finetti's, however, this view does not rely on a formal argument. In this paper, we study the set of all information structures that might be availabe to a decision maker, and show that they are of two types: those compatible with SEU theory and those for which SEU theory must fail. We also show that the former correspond to "good" information, while the latter correspond to information that is not good. Thus, our results provide a formalization of the distinction between Risk and Ambiguity. As a consequence of our main theorem (Theorem 2, Section 8), behavior not-conforming to SEU theory is bound to emerge in the presence of Ambiguity. We give two examples of situations of Ambiguity. One concerns the uncertainty on the class of measure zero events, the other is a variation on Ellberg's three-color urn experiment. We also briefly link our results to two other strands of literature: the study of ambiguous events and the problem of unforeseen contingencies. We conclude the paper by re-considering de Finetti's argument in light of our findings.

Sur la validation des modèles de séries chronologiques spatio-temporelles multivariées

Dans ce mémoire, nous avons utilisé le logiciel R pour la programmation.