Iskanje po katalogu digitalne knjižnice

Opis: This article presents a cloud-based system for the on-line monitoring of tool conditions in end milling. The novelty of this research is the developed system that connects the IoT (Internet of Things) platform for the monitoring of tool conditions in the cloud to the machine tool and optical system for the detection of cutting chip size. The optical system takes care of the acquisition and transfer of signals regarding chip size to the IoT application, where they are used as an indicator for the determination of tool conditions. In addition, the novelty of the presented approach is in the artificial intelligence integrated into the platform, which monitors a tool’s condition through identification of the current cutting force trend and protects the tool against excessive loading by correcting process parameters. The practical significance of the research is that it is a new system for fast tool condition monitoring, which ensures savings, reduces investment costs due to the use of a more cost-effective sensor, improves machining efficiency and allows remote process monitoring on mobile devices. A machining test was performed to verify the feasibility of the monitoring system. The results show that the developed system with an ANN (artificial neural network) for the recognition of cutting force patterns successfully detects tool damage and stops the process within 35 ms. This article reports a classification accuracy of 85.3% using an ANN with no error in the identification of tool breakage, which verifies the effectiveness and practicality of the approach.
Ključne besede: machining, end milling, tool condition monitoring, chip size detection, cutting force trend identification, visual sensor monitoring, cloud manufacturing technologies
Objavljeno v DKUM: 26.03.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (5,65 MB)
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Opis: V sklopu magistrske naloge smo razvili in izdelali vizualizacijo za precizno pozicioniranje pnevmatskega valja. Servo-pnevmatski pozicionirni sistem, katerega ključna sestavna dela sta servo-pnevmatski ventil Enfield S2 in senzorska letev SICK, omogoča natančno pozicioniranje bata na določeni točki vzdolž cilindra. Sistem smo krmilili preko programskega vmesnika S2 Configuration Interface, preko potenciometra v ročnem načinu in preko industrijskega krmilnika Siemens S7-1200 v avtomatskem načinu. V programskem okolju TIA Portal smo izdelali krmilne programe za različne načine delovanja. Glede na krmilne programe oz. načine delovanja smo izdelali vizualizacijo z uporabo Siemens HMI. Nastavitve željene in dejanske pozicije ter točnost bata smo primerjali z ustreznimi funkcijami na HMI zaslonu. Za posamezen krmilni program smo izdelali ustrezno tehnološko dokumentacijo.
Ključne besede: Vizualizacija, Siemens, HMI, pozicioniranje
Objavljeno v DKUM: 23.12.2024; Ogledov: 0; Prenosov: 40
Celotno besedilo (4,44 MB)

Opis: Glavni cilj diplomske naloge je bil zasnovati in izdelati sistem, ki omogoča sinhroniziran pomik dveh pnevmatskih cilindrov, med katera je vstavljen krhek predmet. Sistem temelji na uporabi servo-pnevmatskega ventila Enfield TR, ki skupaj z dvema potnima ventiloma nadzira gibanje pnevmatskih aktuatorjev. Z uporabo programirljivega logičnega krmilnika Siemens S7 je bilo omogočeno natančno in sinhrono premikanje cilindrov. Sistem deluje avtomatsko in ga je mogoče upravljati preko tipk na nadzorni plošči. Po preizkusu delovanja sistema smo na podlagi zbranih podatkov in analiziranih grafov ugotovili visoko natančnost ventila Enfield TR pri zagotavljanju sinhronega premika, kar potrjuje učinkovitost zasnovanega sistema.
Ključne besede: servo-pnevmatski ventil, krmiljenje, PLK, pnevmatika, sinhrono premikanje
Objavljeno v DKUM: 09.10.2024; Ogledov: 0; Prenosov: 36
Celotno besedilo (2,05 MB)

Opis: V diplomski nalogi je predstavljen razvoj, izgradnja, programiranje in rezultati sistema, ki natančno izdela 3D posnetek obravnavanega objekta in je zmožen prepoznati okrogle in pravokotne oblike s tega 3D posnetka. Prav tako se je sistem zmožen naučiti celotne oblike objekta preko prepoznavanja robov z uporabo 3D kamere SICK Ranger3 in senzorskega integracijskega modula SICK SIM4000. S tem sistemom bi se lahko pospešil postopek pregledovanja izdelkov v proizvodnji, hkrati pa močno zvišala natančnost pregledovanja izdelkov, kar bi zvišalo nivo kvalitete.
Ključne besede: SICK, 3D kamera, prepoznavanje oblik, učenje oblike, kontrola kakovosti
Objavljeno v DKUM: 19.09.2024; Ogledov: 0; Prenosov: 16  (2 glasa)
Celotno besedilo (10,46 MB)

Opis: The machinability of steel is a crucial factor in manufacturing, influencing tool life, cutting forces, surface finish, and production costs. Traditional machinability assessments are labor-intensive and costly. This study presents a novel methodology to rapidly determine steel machinability using spark testing and convolutional neural networks (CNNs). We evaluated 45 steel samples, including various low-alloy and high-alloy steels, with most samples being calcium steels known for their superior machinability. Grinding experiments were conducted using a CNC machine with a ceramic grinding wheel under controlled conditions to ensure a constant cutting force. Spark images captured during grinding were analyzed using CNN models with the ResNet18 architecture to predict V15 values, which were measured using the standard ISO 3685 test. Our results demonstrate that the created prediction models achieved a mean absolute percentage error (MAPE) of 12.88%. While some samples exhibited high MAPE values, the method overall provided accurate machinability predictions. Compared to the standard ISO test, which takes several hours to complete, our method is significantly faster, taking only a few minutes. This study highlights the potential for a cost-effective and time-efficient alternative testing method, thereby supporting improved manufacturing processes.
Ključne besede: steel machinability, spark testing, data mining, machine vision, convolutional neural networks
Objavljeno v DKUM: 12.09.2024; Ogledov: 15; Prenosov: 20
Celotno besedilo (5,24 MB)
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