Chatter avoidance in the milling of thin floors with bull-nose end mills: Model and stability diagrams

The milling of thin parts is a high added value operation where the machinist has to face the chatter problem. The study of the stability of these operations is a complex task due to the changing modal parameters as the part loses mass during the machining and the complex shape of the tools that are used. The present work proposes a methodology for chatter avoidance in the milling of flexible thin floors with a bull-nose end mill. First, a stability model for the milling of compliant systems in the tool axis direction with bull-nose end mills is presented. The contribution is the averaging method used to be able to use a linear model to predict the stability of the operation. Then, the procedure for the calculation of stability diagrams for the milling of thin floors is presented. The method is based on the estimation of the modal parameters of the part and the corresponding stability lobes during the machining. As in thin floor milling the depth of cut is already defined by the floor thickness previous to milling, the use of stability diagrams that relate the tool position along the tool-path with the spindle speed is proposed. Hence, the sequence of spindle speeds that the tool must have during the milling can be selected. Finally, this methodology has been validated by means of experimental tests.

"Going outside": discussing the connection between pedagogical practices with digital tools and outdoor learning in early childhood and primary education

Technology has an important role in children's lives and education. Based on several projects developed with ICT, both in Early Childhood Education (3-6 years old) and Primary Education (6-10 years old), since 1997, the authors argue that research and educational practices need to "go outside", addressing ways to connect technology with outdoor education. The experience with the projects and initiatives developed supported a conceptual framework, developed and discussed with several partners throughout the years and theoretically informed. Three main principles or axis have emerged: strengthening Children's Participation, promoting Critical Citizenship and establishing strong Connections to Pedagogy and Curriculum. In this paper, those axis will be presented and discussed in relation to the challenge posed by Outdoor Education to the way ICT in Early Childhood and Primary Education is understood, promoted and researched. The paper is exploratory, attempting to connect theoretical and conceptual contributions from Early Childhood Pedagogy with contributions from ICT in Education. The research-based knowledge available is still scarce, mostly based on studies developed with other purposes. The paper, therefore, focus the connections and interpellations between concepts established through the theoretical framework and draws on the almost 20 years of experience with large and small scale action-research projects of ICT in schools. The more recent one is already testing the conceptual framework by supporting children in non-formal contexts to explore vineyards and the cycle of wine production with several ICT tools. Approaching Outdoor Education as an arena where pedagogical and cultural dimensions influence decisions and practices, the paper tries to argue that the three axis are relevant in supporting a stronger connection between technology and the outdoor.

Practical Management of the Endangered African Wild Dog (Lycaon Pictus): Implementation of Research Methodologies, Management Tools and Validation of Non-Invasive Endocrinology Applications

The porpoise of this study was to implement research methodologies and assess the effectiveness and impact of management tools to promote best practices for the long term conservation of the endangered African wild dog (Lycaon pictus). Different methods were included in the project framework to investigate and expand the applicability of these methodologies to free-ranging African wild dogs in the southern African region: ethology, behavioural endocrinology and ecology field methodologies were tested and implemented. Additionally, research was performed to test the effectiveness and implication of a contraceptive implant (Suprenolin) as a management tool for the species of a subpopulation hosted in fenced areas. Attention was especially given to social structure and survival of treated packs. This research provides useful tools and advances the applicability of these methods for field studies, standardizing and improving research instruments in the field of conservation biology and behavioural endocrinology. Results reported here provide effective methodologies to expand the applicability of non-invasive endocrine assessment to previously prohibited fields, and validation of sampling methods for faecal hormone analysis. The final aim was to fill a knowledge gap on behaviours of the species and provide a common ground for future researchers to apply non-invasive methods to this species research and to test the effectiveness of the contraception on a managed metapopulation.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

New perspectives on A.I. in sentencing. Human decision-making between risk assessment tools and protection of humans rights.

The aim of this thesis is to investigate a field that until a few years ago was foreign to and distant from the penal system. The purpose of this undertaking is to account for the role that technology could plays in the Italian Criminal Law system. More specifically, this thesis attempts to scrutinize a very intricate phase of adjudication. After deciding on the type of an individual's liability, a judge must decide on the severity of the penalty. This type of decision implies a prognostic assessment that looks to the future. It is precisely in this field and in prognostic assessments that, as has already been anticipated in the United, instruments and processes are inserted in the pre-trial but also in the decision-making phase. In this contribution, we attempt to describe the current state of this field, trying, as a matter of method, to select the most relevant or most used tools. Using comparative and qualitative methods, the uses of some of these instruments in the supranational legal system are analyzed. Focusing attention on the Italian system, an attempt was made to investigate the nature of the element of an individual's ‘social dangerousness’ (pericolosità sociale) and capacity to commit offences, types of assessments that are fundamental in our system because they are part of various types of decisions, including the choice of the best sanctioning treatment. It was decided to turn our attention to this latter field because it is believed that the judge does not always have the time, the means and the ability to assess all the elements of a subject and identify the best 'individualizing' treatment in order to fully realize the function of Article 27, paragraph 3 of the Constitution.

Machine learning and convolutional neural networks for melanomas identification

Il machine learning negli ultimi anni ha acquisito una crescente popolarità nell’ambito della ricerca scientifica e delle sue applicazioni. Lo scopo di questa tesi è stato quello di studiare il machine learning nei suoi aspetti generali e applicarlo a problemi di computer vision. La tesi ha affrontato le difficoltà del dover spiegare dal punto di vista teorico gli algoritmi alla base delle reti neurali convoluzionali e ha successivamente trattato due problemi concreti di riconoscimento immagini: il dataset MNIST (immagini di cifre scritte a mano) e un dataset che sarà chiamato ”MELANOMA dataset” (immagini di melanomi e nevi sani). Utilizzando le tecniche spiegate nella sezione teorica si sono riusciti ad ottenere risultati soddifacenti per entrambi i dataset ottenendo una precisione del 98% per il MNIST e del 76.8% per il MELANOMA dataset

Moniprotokollapalveluiden luontiympäristö

Tieto- ja teleliikenneverkkojen konvergenssi on tuonut uusia vaatimuksia palvelukehitysympäristöille ja aiheuttanut haasteita ympäristöjen kehitykselle. Moderneilla palvelukehitysympäristöillä on pystyttävä tuottamaan nopeasti monimutkaisia ja samalla varmatoimisia palveluja. Lisäksi moniprotokollapalveluiden luontiympäristöjen on mukauduttava uusiin olosuhteisiin, jotta palveluntarjoajat pysyisivät kilpailukykyisinä. Tämän työn tarkoituksena oli etsiä menetelmiä ja apuvälineitä nopeaan ja luotettavaan konvergoivissa verkoissa tarjottavien palveluiden luontiin. Työssä tutustuttiin markkinoilla oleviin palvelukehitysympäristöihin ja esiteltiin Intellitel OSN:n palvelukehitysympäristö ja sen palvelunluontimalli, joka tukee palvelunkehitystä läpi koko palvelunluontiprosessin. Työn käytäntöosuudessa parannettiin Intellitelin palvelunluontimallia ja palvelukehitysympäristön tarjoamia työkaluja ja apuohjelmia. Työssä toteutettiin Intellitelin palvelukehitysympäristöllä vaiheittain palvelunluontimallin mukaisesti numeronmuunnospalvelu.

Utilização da lógica difusa no controle inteligente de sistemas eletro-hidráulicos

Electro-hydraulic servo-systems are widely employed in industrial applications such as robotic manipulators, active suspensions, precision machine tools and aerospace systems. They provide many advantages over electric motors, including high force to weight ratio, fast response time and compact size. However, precise control of electro-hydraulic systems, due to their inherent nonlinear characteristics, cannot be easily obtained with conventional linear controllers. Most flow control valves can also exhibit some hard nonlinearities such as deadzone due to valve spool overlap on the passage´s orifice of the fluid. This work describes the development of a nonlinear controller based on the feedback linearization method and including a fuzzy compensation scheme for an electro-hydraulic actuated system with unknown dead-band. Numerical results are presented in order to demonstrate the control system performance

Neural network detection of grinding burn from acoustic emission

An artificial neural network (ANN) approach is proposed for the detection of workpiece `burn', the undesirable change in metallurgical properties of the material produced by overly aggressive or otherwise inappropriate grinding. The grinding acoustic emission (AE) signals for 52100 bearing steel were collected and digested to extract feature vectors that appear to be suitable for ANN processing. Two feature vectors are represented: one concerning band power, kurtosis and skew; and the other autoregressive (AR) coefficients. The result (burn or no-burn) of the signals was identified on the basis of hardness and profile tests after grinding. The trained neural network works remarkably well for burn detection. Other signal-processing approaches are also discussed, and among them the constant false-alarm rate (CFAR) power law and the mean-value deviance (MVD) prove useful.

Avanços no desenvolvimento de um processo de acabamento para peças planas submetidas à cinemática de lapidação sobre um rebolo dressado com grau de recobrimento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento de uma nova sistemática para centragem e alinhamento da plataforma giratória R-9350 na mandriladora CNC PAMA SPEEDRAM 3000

In heavy machining industries, a critical point that must be taken into account is setup. Because the characteristics of machine tools and parts to be machined, usually pieces robust and large, the preparation of these parts must be made accurately for machining has a good result as planned. As a result of the difficulty raised in the setup machining of heavy parts, companies in this segment seek alternatives to reduce the unproductive time caused by setup and optimize machining processes. One way was found that these companies create operating instructions that describe and standardize the operation between its employees, as well as deploy a control machining times to measure the unproductive time caused by the setup. This work studied a new system for the realization of centering and alignment of Rotating Deck R-9350 in CNC Milling Machine PAMA Speedram 3000, in Liebherr Brazil company. The part Rotating Deck R-9350 is a critical part in which its machining in PAMA Milling Machine is made in three phases and their setup times are quite high and involve stopping the machine. It has been tested and proposed a solution to the realization of this part of the setup without the use of the machine, but of the measuring instrument three-dimensional Laser tracker, with which the machine continued to work, while he was in the centering and alignment of other parts. It was noted that the instrument technically attended the need and it was possible to perform this operation more accurately

Cutting tools worked by hand and machine.

Mode of access: Internet.

Thermal error evaluation and modelling of a CNC cylindrical grinding machine

With the relentless quest for improved performance driving ever tighter tolerances for manufacturing, machine tools are sometimes unable to meet the desired requirements. One option to improve the tolerances of machine tools is to compensate for their errors. Among all possible sources of machine tool error, thermally induced errors are, in general for newer machines, the most important. The present work demonstrates the evaluation and modelling of the behaviour of the thermal errors of a CNC cylindrical grinding machine during its warm-up period.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.