1,721,080 research outputs found
aiuto di A. Haller,Scultura lignea raffigurante Madonna addolorata (sec. XVI),Selva di Cadore, chiesa di Santa Fosca
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Robust tool condition monitoring in Ti6Al4V milling based on specific force coefficients and growing self-organizing maps
Full text linkTool condition monitoring (TCM) is a mean to optimize production systems trying to use cutting tool life at its best. Nevertheless, nowadays available TCM algorithms typically lack robustness in order to be consistently applied in industrial scenarios. In this paper, an unsupervised artificial intelligence technique, based on Growing Self-Organizing Maps (GSOM), is presented in synergy with real-time specific force coefficients (SFC) estimation through the regression of instantaneous cutting forces. The conceived approach allows robustly mapping the SFC, exploiting process parameters and similarity to manage the variability of their estimation due to unmodelled phenomena, like machine dynamics and tool run-out. The devised approach allowed detecting the tool end-of-life in cutting tests with variable lubrication, machine tool and cutting speed, through the adoption of a self-starting control chart running on real-time clustered data. The solution was validated through the comparison of the GSOM framework with respect to the optimized self-starting control chart applied without GSOM clustering. The GSOM reached a root mean squared percentage error (RMSPE) of 13.2% with respect to 56.1% obtained with the analogous control chart in a full-set optimization scenario. When optimised on tests for a unique machine tool and tested on another machine tool, GSOM scored an RMSPE of 34.5%, whereas the optimized control chart scored 64.5%
Hybrid heterogeneous prognosis of drill-bit lives through model-based spindle power analysis and direct tool inspection
Full text linkAbstract: In the context of Industry 5.0, manufacturing systems are driven by human-centered production processes, assigning high-level supervisory tasks to operators. This necessitates that machines can perform low-level decision-making actions. This paper presents a novel hybrid heterogeneous prognosis algorithm designed to autonomously inspect the cutting edges of drill-bits and to forecast their Remaining Useful Life along with the associated probability density function. The algorithm leverages specific force coefficients from spindle power and feed axis current measurements, as features correlated with tool wear, to detect tool brittle failures. Additionally, flank wear is automatically measured through a specifically conceived image processing algorithm, using thresholding, convolutional filters, and edge detection techniques. Direct tool wear measurements are analyzed by a hybrid prognosis algorithm, fusing particle filter and multi-layer perceptron, to predict drill-bits’ remaining useful lives. The proposed solution offers several advantages. It reduces the need for extensive experimental run-to-failure tests typically required for training standard machine learning algorithms. Instead, it allows for real-time adaptation, even in scenarios involving untested and varying cutting process conditions. Furthermore, it utilizes both indirect wear observations during cutting operations and direct wear observations during setup times (e.g. tool changes, workpiece changes), without interrupting the ongoing process. Exponent of Kronenberg’s models for specific force coefficients was found to be sensitive to tool wear. Prognosis could correctly predict the 67% of end-of-lives with an average prognosis horizon of 30%
Mechanistic force model for double-phased high-feed mills
Full text linkBeing able to predict cutting forces, torque and power in machining applications allows to check their influence on the quality of the product, to assess the feasibility of the process and to compare different operations for sustainability purposes. In this paper, the analytical development of a mechanistic model for cutting forces prediction for high-feed mills is carried out. High-feed cutters are featured by extremely low lead angles, leading to a gradual engagement of the cutter inside the workpiece. This fact prevents the mechanistic literature formulation to accurately compute the undeformed instantaneous chip section of each cutter, and thus to correctly predict the spindle torque and power. A closed analytical formulation for the mechanistic cutting force model, including an improved chip thickness formulation with variable entry and exit angles and double-phased cutter geometry, is presented. Experimental cutting tests using double-phased high-feed mills were carried out on with variable feed rate per tooth, cutting speed and axial depth of cut. The model was assessed by comparing the performances of the literature model and the developed high-feed one in the identification of specific force coefficients — SFC. The identified SFC resulted to belong to two statistically different populations. SFC 95% confidence intervals were found to be significantly narrower with respect to the literature ones. 95% confidence intervals were equal to (1085; 1426) MPa and (970; 2423) MPa for the proposed and literature model, respectively. The validity of the proposed model was assessed in terms of mean forces, mean spindle torque and mean spindle power prediction capabilities. The Root Mean Squared Prediction Error for the proposed model resulted to be remarkably lower (15 N, 0.33 Nm, 29 W) with respect to literature model (41 N, 1.25 Nm, 120 W)
Climatic factors influencing the isotope composition of Italian olive oils and geographic characterisation
No full textMill condition monitoring based on instantaneous identification of specific force coefficients under variable cutting conditions
Full text linkFollowing the necessity of increased performance and availability requirements for manufacturing systems, research is becoming more and more attracted by monitoring solutions for cutting tools. In this paper, a robust unsupervised strategy for milling tool wear monitoring under variable process parameters and lubrication conditions is presented. The proposed method is completely unsupervised, thus not requiring any kind of training procedure, and is validated on different machine tools. The solution is based upon the online estimation of specific force coefficients (SFC) from instantaneous cutting forces in high-feed milling of Ti6Al4V workpiece. This avoids the need for continuously variable feed per tooth during cutting tests, necessitated for the application of reference literature approach. For this purpose, a novel high-feed mill mechanistic model was conceived and developed. Five run-to-failures were performed in different lubrication conditions – cryogenic and traditional lubrication – with different cutting speeds (50 m/min, 70 m/min and 125 m/min) on two different machine tools. Principal Component Regression was introduced in order to deal with the variability of the estimated coefficients. Self-starting tabular cusum control charts were implemented and demonstrated high accuracy and reliability in the prediction of notch wear phenomena as well as chipping of tool cutting edges for all the cases considered. The solution detected an out-of-control conditions ranging from 166μm to 499μm of maximum flank wear for the analysed tests. The mean prediction error with respect to the 600μm threshold is of −45% with a peak of −72%, whereas reference literature algorithms reach −57% and −66%, respectively. A sensitivity analysis of control chart threshold was performed with reference to the maximum flank wear at the detection point. In a supervised scenario, the threshold can be increased to obtain a less conservative approach: for instance, a mean prediction error of −41% was reached by doubling the threshold
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Fictional brand design. Evolution, strategies, and an attempt to a history of visual identities in audiovisual narratives
No full textBrands tell stories to affirm their values, positioning, and identity. They tell stories that allow people to recognise themselves in them, to share them, to feel part of them. That occurs in the real world as in the world of fiction. The term fictional branding refers to the design and use of brands that do not refer to any existing service, product, company or organisation. They can include any brand referring to products or commercial services, political institutions, military organisations, and more. This paper aims to offer a comprehensive look at the topic through historical and theoretical research and analyse the state of the art of the practice. Specifically, we will deepen fictional brands’ role in building engaging, believable and memorable stories and narratives. In order to carry out a more precise and in-depth analysis, we defined a specific field of investigation: that of long-form narrative audiovisual works, thus including films, TV series and video games. The result of the research is currently merged into the Fictional Brands Archive. This website allows users to consult an extensive collection of fictional brands accompanied by information and visual material
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