178,015 research outputs found
Software Package for Supplier Selection in Manufacturing Supply Networks
No full textTrattasi di pacchetto software sviluppato presso il Dipartimento di Ingegneria dei Materiali e della Produzione. Questo tipo di prodotto è stato inserito nella tipologia "Altro (ministeriale)" su suggerimento dei responsabili del CSI. Si riportano qui di seguito alcune delle principali pubblicazioni scientifiche facenti riferimento al pacchetto software in argomento.
D’Addona, D., Teti, R., 2010, Tool Delivery Prediction through Adaptive Neuro-Fuzzy Inferencing, Vimation Journal, Special Issue on Interactive Systems in Healthcare, ISSN 1866-4245: 65-72
D’Addona, D., Teti, R., 2010, Distributed Multi-Agent System for Tool Inventory Management in a Supply Network, 7th CIRP Int. Conference on Intelligent Computation in Manufacturing Engineering – CIRP ICME ‘10, 23-25 June, Capri, Italy
D’Addona, D., Teti, R., 2010, Diverse Risk/Cost Balancing Strategies for Flexible Tool Management in a Supply Network, Chapter 10 in Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management, Springer Series in Advanced Manufacturing, Editors L. Benyoucef, B. Grabot, Springer Publisher, ISBN 978-1-84996-118-9 (Print) 978-1-84996-119-6 (Online), ISSN: 1860-5168, DOI: 10.1007/978-1-84996-119-6_10: 271-313
D'Addona, D., Teti, R., 2006, Java-Based Multi-Agent System Design for Tool Management in a Supply Network, 5th CIRP Int. Sem. on Intelligent Computation in Manufacturing Engineering – CIRP ICME ‘06, Ischia, 25-28 July: 647-652
Teti, R., D’Addona, D., 2006, Emergent Synthesis in Supply Network Tool Management, J. of Advanced Engineering Informatics, Vol. 20, No. 3, Elsevier, ISSN 1474-0346: 233-246
D’Addona, D., Teti, R., 2005, Intelligent Tool Management in a Multiple Supplier Network, Annals of the CIRP, Vol. 54/1, ISBN 3-905 277-43-3, ISSN 007-8506 (CIRP Annals), ISSN 1660-2773 (CD Rom): 459-46
Wavelet transform feature extraction for chip form recognition during carbon steel turning
No full textCutting force sensor monitoring and wavelet decomposition signal processing were implemented for feature extraction and pattern recognition of chip form typology during turning of 1045 carbon steel. The wavelet packet transform was applied for the analysis of the detected cutting force signals by representing them in a time-frequency domain and providing for the extraction of wavelet packet statistical features. The latter were used to construct wavelet packet feature vectors, ranked according to the number of overlapping elements related to favourable or unfavourable chip forms that cause noise in the pattern recognition procedure (lower number, lower noise, higher rank). The eight highest ranked wavelet packet feature vectors were selected as inputs to a neural network decision-making system on chip form acceptability. Subsequently, a data refinement procedure was employed to improve the neural network performance in the chip form identification process
Computational Investigation of the Steel Quenching Process: Deformation Finite Element Analysis Toward Process Optimization
No full textGrain Size Evaluation in an Austenitic Stainless Steel by the means of Ultrasonic Non Destructive Testing
No full textFeasibility study of using microorganisms as lubricant component in cutting fluids
No full textThis work intends to lay the basis for a Biological Transformation in Manufacturing (BTM) industrial breakthrough aimed at developing new sustainable microbial-based cutting fluids for greener machining processes. The point of departure is that conventional cutting fluids for machining are either entirely based on mineral oils or, in the case of water-based cutting fluids, contain significant percentages of mineral oils. The world annual consumption of cutting fluid concentrate amounts to several billion litres, highlighting their importance for the manufacturing industry and their criticality in terms of environmental impact. Furthermore, the depletion of mineral oil reserves worldwide is already driving cutting fluid industries to search for new renewable raw materials. A contribution to the solutions of these problems can be provided by the development of microbial-based cutting fluids, incorporated in the machine tool, that contain microorganisms with significant lubricating properties in order to substitute mineral oil-based cutting fluids for use in metal alloy machining
Evoluzione della disciplina delle societa' per azioni ed evoluzione del mercato dei capitali
No full textLO SCRITTO ANALIZZA L'EVOLUZIONE DELLA DISCIPLINA DELLE SPA IN RELAZIONE ALLE ESIGENZE DEL MERCATO DEI CAPITAL
Advanced IT Methods of Signal Processing and Decision Making for Zero Defect Manufacturing in Machining
No full textAbstractIn the cutting zone of a machining process, several variables are influenced by process conditions: cutting force, vibrations, temperature, acoustic emission, power absorption. Some variables, useful for process monitoring, can be measured by sensors installed on the machine tool. However, when assessing a particular process variable, a single sensory source may not be able to meet all the requirements. A solution is sensor data fusion, the purpose of which is to combine sensory information from disparate sources so that the resulting intelligence is reinforced. Multi-sensor signal processing provides for the extraction and selection of signal features, relevant for the machining monitoring scope, that are assembled into sensor fusion pattern feature vectors functional for pattern recognition through knowledge based methods. Cognitive paradigms, such as artificial neural networks, can map input information fed by pattern feature vectors to output determinations for decision making on machining process conditions, including the adoption of corrective actions. Application cases of multi-sensor monitoring of machining process conditions investigated at the Fh-J_LEAPT Naples are reported with reference to: (a) workpiece residual stress assessment in turning of nickel base alloys; (b) tool wear state identification in machining of fiber reinforced composites; (c) chip form control in turning of C steel
Abbott, P., Teti, A., Sapsford, R. & Lomazzi, V. (2016) Arab Transformations Longitudinal Data base, Database, University of Aberdeen, Aberdeen; ORCID: /0000-0003-0751-4445/work/94083256; http://hdl.handle.net/2164/11617
No full textLongitudinal Data base for survey data including World Values Survey, Arab Barometer, AfroBarometer and Arab Transformations Project surveys between 1999 and 2016
Emergent methodology for solving tool inventory sizing problems in a complex production system
No full textBased on recently established correlations between emergent synthesis classes, a Class III synthesis problem concerning tool inventory management in a complex make-to-order manufacturing environment is addressed. Such environment is shown to be affected by significant non-random uncertainty involving tool delivery time fluctuations and unpredictable tool demand. The trade-off typical of the inventory sizing dilemma is introduced with reference to reusable tools, such as grinding wheels, and a satisfactory solution is achieved by means of a dynamic purpose assignment approach. This leads to a global behavior, expressed by a recurrently oscillating pattern, affecting the inventory level trend in the nearby of a peculiar attraction band: the oscillation amplitude mainly depends on the attractor’s bandwidth as well as on the peaks attained by the tool demand rate during the tool management period
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