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)
Gradivo ima več datotek! Več...

Opis: ŠTORE STEEL Ltd. is one of the three steel plants in Slovenia. Continuous cast 180 mm × 180 mm billets can undergo cooling to room temperature using a turnover cooling bed. They can also be cooled down under hoods or heat treated to reduce residual stresses. Additional operations of heat treatment from 36 h up to 72 h and cooling of the billets for 24 h, with limited capacities (with only two heat treatment furnaces and only six hoods), drastically influence productivity. Accordingly, the casting must be carefully planned (i.e., the main thing is casting in sequences), while the internal quality of the billets (i.e., the occurrence of inner defects) may be compromised. Also, the stock of billets can increase dramatically. As a result, it was necessary to consider the abandoning of cooling under hoods and heat treatment of billets. Based on the collected scrap data after ultrasonic examination of rolled bars, linear regression and genetic programming were used for prediction of the occurrence of inner defects. Based on modeling results, cooling under hoods and heat treatment of billets were abandoned at the casting of several steel grades. Accordingly, the casting sequences increased, and the stock of billets decreased drastically while the internal quality of the rolled bars remained the same.
Ključne besede: billet cooling, continuous casting, ultrasonic testing, logistic regression, genetic programming, industrial study, steel making, optimization
Objavljeno v DKUM: 25.11.2024; Ogledov: 0; Prenosov: 9
Celotno besedilo (1,68 MB)
Gradivo ima več datotek! Več...

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)
Gradivo ima več datotek! Več...

Opis: Štore Steel je največji proizvajalec vzmetnega jekla v Evropi. Podjetje Štore Steel izdeluje več kot 1400 jekel različnih kvalitet z različnimi kemijskimi sestavami. Med njimi je 16MnCrS5, ki spada v skupino jekel za cementacijo. Le-ta so namenjena za strojno obdelavo različnih delov (npr. palic, plošč, trakov, odkovkov), pri katerih se zahteva kombinacija obrabne odpornosti, žilavosti ter trajno-nihajne trdnosti. Vse te lastnosti lahko povezujemo z natezno trdnostjo, ki je odvisna predvsem od kemične sestave in toplotne obdelave po valjanju. Prav tako je pomemben raztezek. V diplomskem delu je predstavljena metoda napovedovanja natezne trdnosti in raztezka s pomočjo linearne regresije. V napovednem modelu smo uporabili vsebnosti legirnih elementov v jeklu (C, Mn, S in Cr) ter načine toplotne obdelave. Glede na rezultate analize lahko povečamo natezno trdnost in izboljšamo raztezek.
Ključne besede: 16MnCrS5, natezna trdnost, linearna regresija, raztezek, modeliranje
Objavljeno v DKUM: 03.04.2017; Ogledov: 2732; Prenosov: 299
Celotno besedilo (3,83 MB)

Opis: Optimizacija je v današnjem konkurenčnem in hitro odzivnem okolju bistvena za doseganje najboljših rezultatov in uspešno poslovanje. V podjetju Štore Steel d.o.o. nenehno stremijo k izboljšavam in povečanju produktivnosti posameznih proizvodnih obratov. V tem magistrskem delu se optimizacija nanaša na valjarno, in sicer na proces valjanja okroglih profilov jeklenih palic. Pri valjanju za preoblikovanje obdelovanca uporabljamo valjarska ogrodja, v katera so vstavljeni valji. Valji imajo po svojem obodu postružene oblike-kalibre, s katerimi z natančnim vodenjem valjanca neposredno v kalibro dajemo valjancu novo obliko in dimenzijo prereza. Vodenje se izvaja s skrinjami, ki so montirane na ogrodjih, pred in za valjem. Pri prehodu valjanja iz ene dimenzije na drugo se v sistemu pojavi prilagoditev posameznih valjev, tako da bomo z novo postavitvijo dosegli želeno dimenzijo. Ob tem se pojavi tudi prestavitev skrinj, tako da vodijo valjanec v zahtevano kalibro. Z analizo valjanja, opreme, s katero ga izvajamo, in planov, ki ji v proizvodnem procesu upoštevamo, smo iskali optimizacijo razmestitve kaliber na valjih ob montaži več skrinj na ogrodje. S tem smo želeli zmanjšati število menjav skrinj in zastoje, ki jih menjava povzroči. Z analizo valjev, kaliber in pripadajočih skrinj smo ugotovili glavne pogoje za optimizacijo. Za iskanje rešitve smo uporabili genetski algoritem. Cilj magistrskega dela je bil zmanjšati število menjav za 20 %, s predstavljeno optimizacijo pa se je število menjav zmanjšalo za 36,3 %. S tem je zastavljeni cilj dosežen. 
Ključne besede: valjanje, optimizacija, razmestitev kaliber, genetski algoritmi
Objavljeno v DKUM: 21.10.2016; Ogledov: 2198; Prenosov: 205
Celotno besedilo (4,04 MB)