Estimation of Casting Mold Interfacial Heat Transfer Coefficient in Pressure Die Casting Process by Artificial Intelligence Methods

Aksoy, Bekir; Koru, Murat

doi:10.1007/s13369-020-04648-7

Cited by 13 publications

(5 citation statements)

References 45 publications

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“…Due to the complexity of events in real casting conditions and the number of interdependent parameters, the use of mathematical methods would be unreliable without their tuning and verification according to real conditions. Close cooperation with the results of experimental measurements is always necessary as they introduce the characteristic features of a particular casting machine into the model [29]. It is not always possible to obtain the values of certain quantities on a real device that can be used to directly verify the results of the models: for example, the thickness of the solid shell.…”

Section: Modelling Methods Of the Thickness Of The Solid Shellmentioning

confidence: 99%

“…The results of such prediction models, which work online in real time, can be also used as input data in breakout prediction systems [28]. Currently, sophisticated numerical models can be used to perform not only thermal calculations but also calculations of stress, structure and chemical heterogeneity, including segregation prediction [29,30]. Due to the high computational complexity, calculations of such quantities are performed offline for the purpose of research and development of casting new steel grades or formats.…”

Section: Introductionmentioning

confidence: 99%

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Research on Solid Shell Growth during Continuous Steel Casting

et al. 2023

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The continuous steel casting process must simultaneously meet the requirements for production performance, quality and safety against breakouts. Knowing the thickness of the solidified shell, particularly at the exit of the mould, is useful for the casting process control and breakout prevention. Shell thickness is difficult to measure during casting; in practice, it is predicted by indirect methods and models. But after undesired rupture of the shell and leakage of the liquid steel, it is possible to measure the shell thickness directly. This article is focused on the problem of the growth and measurement of the solid shell obtained after the breakout of a round block with a diameter of 410 mm. An original methodology was developed in which a surface mesh of points was created from the individual scanned parts of the block using a 3D laser scanner. Research has shown differences of up to 6 mm between the maximum and minimum shell thickness at the mould exit. A regression function of the average shell thickness on time was found. The results of the real shell growth were further used for the verification of the original numerical model of cooling and solidification of the round block.

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Section: Modelling Methods Of the Thickness Of The Solid Shellmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Solid Shell Growth during Continuous Steel Casting

et al. 2023

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“…Apparao et al (2017) also proposed using the Taguchi method to optimize the press casting technique in [6]. In 2020, Aksoy used an AI method to evaluate the heat transfer coefficient of casting mold interface in the press casting process [7]. The above academic studies showed how to reduce the influence of porosity on the quality of cast products.…”

Section: Introductionmentioning

confidence: 99%

Press Casting Quality Prediction and Analysis Based on Machine Learning

Lin

et al. 2022

Electronics

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In an industrial mass production pattern, quality prediction is one of the important processes when guarding quality. The products are extracted periodically or quantitatively for inspection in order to observe the relationship between process variation and engineering specification. When these irregularities are not instantly detected by lot sampling inspection, lot defectives are produced, and the defective cost increases. Failure to identify defects during sampling inspection leads to product returns or harm to business reputation. Press casting is a common mass production method in the metal industry. After the metal is molten at a high temperature, high pressure is injected into the mold, and then it is solidified and formed in the mold. Thus, pressure stability inside the mold is one of the key factors that influences quality. The melting point of aluminum alloy is normally around 650 °C, but there was no sensor that could withstand this high temperature. To combat this, we developed a high temperature resistant sensor and installed it into pressure casting mold grounded on the principles of fluid mechanics and experts’ suggestions in order to realize the impact of pressure change on the mold. To our limited knowledge, it was a seminal study on predicting mold’s casting quality via in-mold pressure data. We propose a press casting quality prediction method based on machine learning. By collecting the in-mold pressure data in real time. Savitzky-Golay Filter is used for data smoothing, and first-order difference is taken to extract the time interval of an actual injection of molten metal in-to the mold. We extract the key data that influence the quality and employ XGBoost to establish a classifier. In the experiment we prove that the method achieves good accuracy of quality prediction and recall of defectives for in-mold pressure. Via this model, we not only can save large amount of time and costs, but also can carry out maintenance warnings in advance, notify professionals to stop produce defective products, reduce the shipping risk and maintain reputation so as to strengthen its competitive edge.

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“…Bu ilişkilerin belirlenmesinde, özellikle de veri sayısının çok fazla olduğu durumlarda veriler arasında sağlıklı ilişkiler kurabilmek için istatistiksel programlardan ve yapay zekâ tekniklerinden yararlanılmaktadır. Bu tekniklerden en yaygın olarak kullanılanlara yapay sinir ağları, bulanık mantık, genetik algoritma, veri madenciliği, derin öğrenme ve makine öğrenmesi örnek olarak verilebilir [13][14][15].…”

Section: Introductionunclassified

Poli̇mer Betonlarin Mekani̇k Performanslarinin Veri̇ Madenci̇li̇ği̇ Kullanarak Beli̇rlenmesi̇

Kaplan

Özel

2022

International Journal of 3D Printing Technologies and Digital Industry

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Gelişen teknoloji ile birlikte artan yapı malzemeleri çeşitliliğinde kendilerine özgü yapıları ile yer bulan polimer betonların kullanımı, yüksek mukavemetlerinin yanı sıra çevresel etkilere karşı dayanım özelliklerinden dolayı her geçen gün artmaktadır. Bu doğrultuda çeşitli amaçlar için kullanılan polimer betonları üretmek için kullanılacak malzemeleri, bu malzemelerin karışım oranlarını, polimer betonların fiziksel ve mekanik özellikleri gibi kendine özgü mühendislik özelliklerini bilmek kullanımları için büyük öneme sahiptir. Özellikle basınç ve eğilme mukavemeti gibi mekanik performansların betonun sahip olduğu diğer özelliklerle yakından ilişkili olması sertleşmiş beton için önemli bir durum olarak ortaya çıkmaktadır. Bu nedenle betonun mekanik özelliklerinin önceden belirlenmesine yönelik birçok çalışma son zamanlarda yoğun olarak yapılmaktadır. Bu çalışma kapsamında, farklı polimer reçine türleri ve farklı agregaların hacimce %0, %15, %30 ve %45 oranlarında karıştırılmasıyla üretilen polimer betonların; fiziksel ve mekanik özellikleri deneysel olarak belirlenmiştir. Elde edilen deneysel veriler kapsamında; üretilen numunelerin bileşenlerinin, karışım oranlarının, fiziksel özelliklerinin ve mekanik özelliklerinin aralarındaki ilişkiler alternatif bir metot olarak veri madenciliği kullanılarak ele alınmıştır. Çalışma sonucunda betonların mühendislik özelliklerinin modellenmesinde veri madenciliğinin başarılı bir şekilde kullanılabileceği sonucuna varılmıştır.

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Estimation of Casting Mold Interfacial Heat Transfer Coefficient in Pressure Die Casting Process by Artificial Intelligence Methods

Cited by 13 publications

References 45 publications

Research on Solid Shell Growth during Continuous Steel Casting

Research on Solid Shell Growth during Continuous Steel Casting

Press Casting Quality Prediction and Analysis Based on Machine Learning

Poli̇mer Betonlarin Mekani̇k Performanslarinin Veri̇ Madenci̇li̇ği̇ Kullanarak Beli̇rlenmesi̇

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