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Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718
Matija Hriberšek, Lucijano Berus, Franci Pušavec, Simon Klančnik, 2020, izvirni znanstveni članek

Opis: This paper explains liquefied nitrogen’s cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental data, the empirical model was designed by an adaptive neuro-fuzzy inference system (ANFIS) and optimized with the particle swarm optimization algorithm (PSO), with the aim to predict the cooling rate (temperature) of the used media. The research has shown that the velocity of the nozzle has a significant impact on its cooling ability, among other factors such as depth and distance. Conducted experimental results were used as a learning set for the ANFIS model’s construction and validated via k-fold cross-validation. Optimization of the ANFIS’s external input parameters was also performed with the particle swarm optimization algorithm. The best results achieved by the optimized ANFIS structure had test root mean squared error (test RMSE) = 0.2620, and test R$^2$ = 0.8585, proving the high modeling ability of the proposed method. The completed research contributes to knowledge of the field of defining liquefied nitrogen’s cooling ability, which has an impact on the surface characteristics of the machined parts.
Ključne besede: cryogenic machining, cooling impact, Inconel 718, machine learning, adaptive neuro-fuzzy inference system, particle swarm optimization
Objavljeno v DKUM: 14.07.2023; Ogledov: 203; Prenosov: 9
URL Povezava na celotno besedilo
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Comparative model analysis of two types of clamping elements in dynamic conditions
Petar Todorovic, Borut Buchmeister, Marko Djapan, Djordje Vukelić, Marko Milosevic, Branko Tadic, Milan Radenkovic, 2014, izvirni znanstveni članek

Opis: This paper studies the compliance of the fixture-workpiece system. Workpiece clamping case with two types of clamping elements is considered. The first type of clamping element is standard, with flat top, while the second one is specially designed, with round cutting insert. Analyzed was the case of workpiece clamping using small forces, whereby the deformations in the workpiece/clamping element interface are predominantly on the order of magnitude of roughness height. A comparative analysis of dynamic behaviour of both types of clamping elements is also presented. In comparison with its standard counterpart, the specially designed clamping element with round cutting insert has superior clamping performance regarding both tangential load capacity and compliance.
Ključne besede: clamping, clamping element design, product development, machining, cutting, compliance, fixture, roughness, analysis, dinamical behaviour
Objavljeno v DKUM: 11.07.2017; Ogledov: 981; Prenosov: 97
.pdf Celotno besedilo (1,39 MB)
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Določevanje značilnih tehnoloških in gospodarskih parametrov med postopkom odrezovanja
Uroš Župerl, Franc Čuš, 2004, izvirni znanstveni članek

Opis: V prispevku je predlagan nov ne-deterministični optimizacijski postopek za kompleksno optimizacijo rezalnih parametrov pri odrezavanju. Ta postopek uporablja umetne nevronske mreže (ANN) za reševanje problema optimiranja rezalnih pogojev. Predlagan pristop temelji na kriteriju maksimalne stopnje proizvodnje in vključuje štiri tehnološke omejitve. Z izbiro optimalnih rezalnih parametrov je možno doseči ugodno razmerje med nizkimi obdelovalnimi stroški in visoko produktivnostjo ob upoštevanju podanih omejitev procesa rezanja. Eksperimentalni rezultati kažejo, da je predlagani algoritem pri reševanju nelinearnih optimizacijskih problemov s postavljenimi omejitvami učinkovit in ga je možno vključiti v inteligentne obdelovalne sisteme. Najprej je formuliran problem določitve optimalnih parametrov odrezavanja, kot več-ciljni optimizacijski problem. Nato so predlagane nevronske mreže za predstavitev proizvajalčevih prioritetnih struktur. Za demonstracijo zmogljivosti predlaganega pristopa je detajlno obravnavan nazoren primer.
Ključne besede: odrezovanje, pogoji rezanja, struženje, optimiranje, nedeterministični postopki, machining, cutting parameters, turning, nondeterministic optimization
Objavljeno v DKUM: 10.07.2015; Ogledov: 1037; Prenosov: 50
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Databases for technological information systems
Franc Čuš, Bogomir Muršec, 2004, izvirni znanstveni članek

Opis: Organization of tool management for mixed production includes today, in particular, the computer-supported management and organization of the flow of tools and data on them. The system supports the entire flow of tools in a production process including the tool store management, commissioning, mounting, dismantling and pre-setting of tools. The system contains the management of the tool database with all vital data on tools and ensures adaption of production requirements for meeting the needs for tools. The integral model for the selection of optimal cutting conditions in the computer aided tool management system (TOMS) is proposed. The integration of technological databases and tool management systems is urgently necessary. The target function for the OPTIS programme, worked out by the programme package Microsoft Visual Basic, is selection of optimal cutting conditions from commercial databases with respect to the lowest costs of machining by taking into account the technological limitations of the metal removal process. The newly developed OPTIS programme selects optimal cutting conditions with respect to the tool maker, workpiece material, type of machining, cutting machine, smallest and greatest cutting conditions, tool, data on series, type of clamping and workpiece geometry.
Ključne besede: machining processes, tool system, manufacturing systems, technological information systems, databases, tool management, machining systems, cutting conditions
Objavljeno v DKUM: 01.06.2012; Ogledov: 3316; Prenosov: 93
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Neural-network-based numerical control for milling machine
Jože Balič, 2004, izvirni znanstveni članek

Opis: We describe a device which uses a neural network to generate part-programs for milling, drilling and similar operations on machining centres, on the basis of 2D, 2.5D or 3D geometric models of prismatic parts, without operator intervention. The neural network consists of networks for prediction of milling strategy, for prediction of surface quality and for the optimisation of technological parameters in milling. We introduce the surface complexity index (SCI) for identifying surfaces which are very difficult to machine. The SCI takes the surface roughness and machining strategy into account. Teaching and testing of the NN is described. The device, which can be retrofitted to a CNC controller, can be trained from a set of typical parts and will then generate new NC part-programs. A case study of a tool used in the automotive supplier industry shows how a milling strategy is proposed, according to set constraints.
Ključne besede: intelligent CNC control, retrofit CNC, intelligent CAD/CAM, neural networks, machining centres
Objavljeno v DKUM: 01.06.2012; Ogledov: 1460; Prenosov: 89
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Neural network based manufacturability evaluation of free form machining
Marjan Korošec, Jože Balič, Janez Kopač, 2005, izvirni znanstveni članek

Opis: Most CAD/CAM and computer-aided process planning systems manipulate all geometrical features on the part equally. In the area of free form machining, lack of efficient methodology for assessing the degree of manufacturing pretentiousness of free form features is still noticeable. Developing this methodology inside CAD/CAM systems brings the following benefits to the tool shop praxis: it minimizes the number of set-ups and tool changes and at the same time ensures the right sequence of machining strategies in order to achieve the best possible surface quality in the machining area. Based on this assessment, the CAD/CAM process will also be greatly simplified. When there are an increased number of non-prismatic and non-cylindrical features, this problem is even more exaggerated, and its solution cannot be found in the framework of analytical mathematics. This paper reports a neuro-fuzzy model that uses the concept of "feature manufacturability" to identify and recognize the degree of "pretentiousness-difficulty of machining". The model is created by means of the construction of parametric fuzzy membership functions, based on neural networks learning process. This makes possible simultaneous evaluation of features complexity in a CAD model and manufacturing capability in an environment description model.
Ključne besede: index of machining complexity
Objavljeno v DKUM: 01.06.2012; Ogledov: 1368; Prenosov: 96
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Combined feedforward and feedback control of end milling system
Franc Čuš, Uroš Župerl, Jože Balič, 2011, izvirni znanstveni članek

Ključne besede: machining, force control, neural networks
Objavljeno v DKUM: 01.06.2012; Ogledov: 1330; Prenosov: 36
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Prediction of surface roughness using a feed-forward neural network
Jernej Šenveter, Simon Klančnik, Jože Balič, Franc Čuš, 2010, izvirni znanstveni članek

Opis: This article presents the development of a system for predicting surface roughness, using a feed-forward neural network. The primary goal was to develop a system in order to predict with complex reliability and defined accuracy. However, this system is designed in such a way that it is also possible to use it for various other workpieces. The described system uses a neural network which receives signals at the input level. The signals then travel through all hidden levels to the output level, where the responses to input signals are received. Data are used which affects the selection of surface roughness regarding the input to the neural network. Three different inputs in total are used for the neural network. Data which represents the inputs to the neural network are encoded, so that they occupy values between 0 and 1. Adequate cutting speed, feed, and depth of cut, are selected in order to achieve an adequate surface roughness of the workpiece, using the trained neural network. This contributes to the optimisation and economy of machining, which is very important during the production of an individual product and also for an individual company or organisation when transferring the final product to the contracting authority or final customer.
Ključne besede: machining, turning, surface roughness, neural network
Objavljeno v DKUM: 31.05.2012; Ogledov: 1826; Prenosov: 62
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Machining process optimization by colony based cooperative search technique
Uroš Župerl, Franc Čuš, 2008, izvirni znanstveni članek

Opis: Research of economics of multi-pass machining operations has significant practical importance. Non-traditional optimization techniques such genetic algorithms, neural networks and PSO optimization are increasingly used to solve optimization problems. This paper presents a new multi-objective optimization technique, based on ant colony optimization algorithm (ACO), to optimize the machining parameters in turning processes. Three conflicting objectives, production cost, operation time and cutting quality are simultaneously optimized. An objective function based on maximum profit in operation has been used. The proposed approach uses adaptive neuro-fuzzy inference system (ANFIS) system to represent the manufacturer objective function and an ant colony optimization algorithm (ACO) to obtain the optimal objective value. New evolutionary ACO is explained in detail. Also a comprehensive userfriendly software package has been developed to obtain the optimal cutting parameters using the proposed algorithm. An example has been presented to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using methods of other researchers and handbook recommendations. The results indicate that the proposed ant colony paradigm is effective compared to other techniques carried out by other researchers.
Ključne besede: machining, turning, optimization, cutting parameters
Objavljeno v DKUM: 31.05.2012; Ogledov: 1733; Prenosov: 41
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Approach to optimization of cutting conditions by using artificial neural networks
Franc Čuš, Uroš Župerl, 2006, izvirni znanstveni članek

Opis: Optimum selection of cutting conditions importantly contribute to the increase of productivity and the reduction of costs, therefore utmost attention is paid to this problem in this contribution. In this paper, a neural network-based approach to complex optimization of cutting parameters is proposed. It describes the multi-objective technique of optimization of cutting conditions by means of the neural networks taking into consideration the technological, economic and organizational limitations. To reach higher precision of the predicted results, a neural optimization algorithm is developed and presented to ensure simple, fast and efficient optimization of all important turning parameters. The approach is suitable for fast determination of optimum cutting parameters during machining, where there is not enough time for deep analysis. To demonstrate the procedure and performance of the neural network approach, an illustrative example is discussed in detail.
Ključne besede: optimization, cutting parameter optimization, genetic algorithm, cutting parameters, neural network algorithm, machining, metal cutting
Objavljeno v DKUM: 30.05.2012; Ogledov: 2344; Prenosov: 119
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