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1.
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: 31.05.2012; Ogledov: 1071; Prenosov: 41
URL Povezava na celotno besedilo

2.
Computer-based workpiece detection on CNC milling machine tools using optical camera and neural networks
Simon Klančnik, Jernej Šenveter, 2010, izvirni znanstveni članek

Opis: In this paper, system for optical determining the workpiece origin on the CNC machine is presented. Similar high sophisticated systems are commercially available but in most cases they are very expensive and so their purchase is economically unjustified. The purpose of our research is to develop an inexpensive system for non-contact determination of the workpiece origin, which is also sufficiently precise for practical use. The system is implemented on a three-axis CNC milling machine Lakos 150 G, which is primarily designed for good machinability materials. Calibration procedure using feed-forward neural networks was developed. With this method the calibration procedure is simplified and the mathematical derivation of camera model is avoided. Learned neural network represents the camera calibration model. After neural network learning is complete, we can begin using the system for determining the workpiece origin. This developed system was through a number of tests proved to be reliable and suitable for use in practice. In the paper, working of system is illustrated with a practical example, which confirms the effectiveness of the implemented system in actual use on machine.
Ključne besede: neural networks, image processing, milling, workpiece detection
Objavljeno: 01.06.2012; Ogledov: 1120; Prenosov: 24
URL Povezava na celotno besedilo

3.
Razvoj metode za inteligentno napovedovanje tehnoloških parametrov upogibanja pločevine v dveh stopnjah
Jernej Šenveter, 2013, doktorska disertacija

Opis: V doktorskem delu je znanstveno opisano doslej slabo poznano in raziskano upogibanje v dveh stopnjah za kot 90 ali več v večstopenjskih progresivnih orodjih. Omenjeno upogibanje je proces, pri katerem v prvi fazi pločevino pred-deformiramo do pred-upogibnega kota ter nato v drugi stopnji upogibanja pločevino upognemo do končnega kota upogiba. Osrednji del naloge zavzema razvoj ter implementacija metode za inteligentno napovedovanje tehnoloških parametrov. Za uspešno uporabe inteligentne metode, smo preučili postopke upogibanja ter podrobneje raziskali in analizirali upogibanje v dveh stopnjah. Pomemben vidik za zadovoljivo uporabo metode je tudi razvito in izdelano testno orodje za upogibanje pločevine. Na testnem orodju so bili opravljeni preizkusi, ki smo jih izvedli z različnimi vrstami pločevin. Pri spremljanju procesa upogibanja pločevine, smo merili oziroma beležili naslednje parametre: pred-upogibni kot, zamik matrice pri drugem upogibu, debelino pločevine, mejo plastičnosti, natezno trdnost ter končni kot upogiba. Po temeljiti analizi ter overitvi izmerjenih parametrov smo z uporabo umetne inteligence-nevronske mreže implementirali inteligentne metode za napovedovanje končnega kota upogiba, zamika matrice pri drugem upogibu ter pred-upogibnega kota pri postopku upogibanja v dveh stopnjah. Inteligentna metoda povečuje kakovost uporabe postopka in zmanjšuje število preizkusov. Na osnovi metode za napovedovanje tehnoloških parametrov je moč postopek enostavno uporabiti v večstopenjskih progresivnih orodjih za preoblikovanje pločevine. Razvita inteligentna metoda napovedovanja končnega kota upogiba, zamika matrice pri drugem upogibu ter pred-upogibnega kota uporabnikom omogoča učinkovito ekonomsko in tehnično enostavno uporabo postopka.
Ključne besede: upogibanje, upogibanje v dveh stopnjah, inteligentni sistem, napovedovanje tehnoloških parametrov, elastično izravnavanje pločevine
Objavljeno: 08.01.2014; Ogledov: 1071; Prenosov: 136
.pdf Celotno besedilo (7,88 MB)

4.
Prediction of technological parameters of sheet metal bending in two stages using feed-forward neural network
Jernej Šenveter, Jože Balič, Mirko Ficko, Simon Klančnik, 2016, izvirni znanstveni članek

Opis: This paper describes sheet metal bending in two stages as well as predicting and testing of the final bend angle by means of a feed-forward neural network. The primary objective was to research the technological parameters of bending sheet metal in two stages and to develop an intelligent method that would enable the predicting of those technological parameters. The process of bending sheet metal in two stages is presented by demonstrating the various technological parameters and the test tool used to carry out tests and measurements. The results of the tests and measurements were of decisive guidance in the evaluation of individual technological parameters. Developed method for prediction of the final bend angle is based on a feed-forward neural network that receives signals at the input level. These signals then travel through the hidden level to the output level, where the responses to input signals are received. The input to the neural network is composed of data that affect the selection of the final bend angle. Only five different inputs are used for the total neural network. By choosing the desired final bend angle by means of the trained neural network, bending sheet metal in two stages is optimised and made more efficient.
Ključne besede: bending in two stages, intelligent system, neural network, prediction of the final bend angle
Objavljeno: 12.07.2017; Ogledov: 332; Prenosov: 214
.pdf Celotno besedilo (900,30 KB)
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