Intelligent cutting tool condition monitoring in millingUroš Župerl
, Franc Čuš
, Jože Balič
, 2011, izvirni znanstveni članek
Opis: Purpose: of this paper is to present a tool condition monitoring (TCM) system that can detect tool breakage in real time by using a combination of neural decision system, ANFIS tool wear estimator and machining error compensation module. Design/methodology/approach: The principal presumption was that the force signals contain the most useful information for determining the tool condition. Therefore, ANFIS method is used to extract the features of tool states from cutting force signals. The trained ANFIS model of tool wear is then merged with a neural network for identifying tool wear condition (fresh, worn). Findings: The overall machining error is predicted with very high accuracy by using the deflection module and a large percentage of it is eliminated through the proposed error compensation process. Research limitations/implications: This study also briefly presents a compensation method in milling in order to take into account tool deflection during cutting condition optimization or tool-path generation. The results indicate that surface errors due to tool deflections can be reduced by 65-78%. Practical implications: The fundamental limitation of research was to develop a single-sensor monitoring system, reliable as commercially available system, but much cheaper than multi-sensor approach. Originality/value: A neural network is used in TCM as a decision making system to discriminate different malfunction states from measured signals.
Ključne besede: tool condition monitoring, TCM, wear, tool deflection, ANFIS, neural network, end-milling
Objavljeno: 01.06.2012; Ogledov: 880; Prenosov: 20
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MODELI MEHKE LOGIKE IN NEVRONSKE MREŽE ZA ANALIZO GEOTEHNIČNIH KONSTRUKCIJPrimož Jelušič
, 2013, doktorska disertacija
Opis: V doktorski disertaciji smo razvili nove modele za analizo voziščne konstrukcije, podporne konstrukcije in podzemne konstrukcije. Modele smo izdelali z adaptivnimi nevronskimi mrežami in mehkim identifikacijskim sistemom (adaptive network based fuzzy inference system, ANFIS). ANFIS metoda v splošnem omogoča izdelavo geotehničnih modelov, ki imajo večjo sposobnost napovedi kot konvencionalne analitične metode. ANFIS modele smo izdelali na podlagi geomehanskih računskih modelov in optimizacijskih modelov. Optimizacijske modele smo izdelali z nelinearnim programiranjem (nonlinear programming, NLP). Natančnost napovedi modelov je odvisna od nelinearnosti obravnavanega problema. Ugotovili smo, da je v ANFIS modelih bistvenega pomena razvrstitev nevronov. Za ta namen smo razvili ANFIS modele z različno topologijo nevronov in uporabili tisto, ki je imela najmanjšo odstopanje glede na množico testnih podatkov.
V doktorski disertaciji razviti ANFIS modeli voziščne konstrukcije omogočajo napovedovanje horizontalne specifične deformacije na dnu asfaltne plasti in vertikalne specifične deformacije na podlagi. Razviti modeli za podporno konstrukcijo s pasivnimi sidri omogočajo napovedovanje faktorja varnosti in optimalnega naklona pasivnih sider. Dobljeni ANFIS modeli za podzemne konstrukcije omogočajo napovedovanje optimalnih izdelavnih stroškov podzemnega skladišča plina in optimalne zasnove kaverne.
Ključne besede: ANFIS, NLP, voziščna konstrukcija, podporna konstrukcija s pasivnimi sidri, podzemno skladišče plina
Objavljeno: 17.10.2013; Ogledov: 1583; Prenosov: 167
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Soil-nail wall stability analysis using ANFISPrimož Jelušič
, Bojan Žlender
, 2013, izvirni znanstveni članek
Opis: The safety-factor optimization for a soil-nail wall is presented. The optimization is performed using the non-linear programming (NLP) approach. For this purpose, the NLP optimization model OPTINC was developed. The safety factor and the optimal inclination of the soil nails from the horizontal direction depend on the design of the soil-nail wall. Based on these results the ANFIS-INC model was developed for the prediction of the optimal inclination of the soil nail for any design of soil-nail wall. Additionally, an ANFIS-SF model was developed to predict the safety factor for different inclinations of the wall, the slope angle of the terrain, the length of the nails, and the hole diameter. It was found that the inclination of the soil nail should be adjusted to the inclination of wall, the length of nail, the slope angle of the terrain and the hole diameter. With increasing inclination of the wall, the length of the soil nail and the hole diameter, the safety factor is increasing. On the other hand, the safety factor is decreasing with the increasing slope angle of the terrain. The use of nonlinear programming and an Adaptive Network based Fuzzy Inference System allows a comprehensive analysis of the geotechnical problems.
Ključne besede: soil-nail, wall stability, optimization, NLP, ANFIS
Objavljeno: 10.07.2015; Ogledov: 800; Prenosov: 33
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Real-time cutting tool condition monitoring in millingFranc Čuš
, Uroš Župerl
, 2011, izvirni znanstveni članek
Opis: Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cuttingforces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker's microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a single sensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach.
Ključne besede: end-milling, tool condition monitoring, wear estimation, ANFIS
Objavljeno: 10.07.2015; Ogledov: 881; Prenosov: 72
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