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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Estimation of Casting Mold Interfacial Heat Transfer Coefficient in Pressure Die Casting Process by Artificial Intelligence Methods
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Estimation of Casting Mold Interfacial Heat Transfer Coefficient in Pressure Die Casting Process by Artificial Intelligence Methods

机译：人工智能方法估计压力压铸过程压力压铸过程中的铸模界面传热系数

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摘要

Pressure casting process, which is based on the principle of filling and solidifying the liquid metal into the mold cavity with the effect of speed and pressure, enables to obtain a serial product. The pressure casting process usually involves a thermal process. Starting with the casting process, the thermal resistances, especially formed at the casting mold interface, and the resultant interfacial heat transfer coefficient (IHTC) are among the most important factors determining the mechanical and physical properties of the produced part. The IHTC depends on the mold temperature, casting temperature, injection pressure, injection rate, vacuum application and many other incalculable parameters. In this study, it was aimed to determine the heat transfer coefficient and heat flux of the casting mold interface which has a significant effect on the quality of parts in the pressure casting of cylindrical mold geometry of AlSi_8Cu_3Fe aluminum alloy. The study was carried out depending on different casting temperatures, injection pressure, injection speed and vacuum application to the mold cavity. Temperatures were measured with thermocouples placed in the mold and casting material, IHTC and heat flux were calculated with finite difference method by using experimentally measured temperatures. In the application of artificial intelligence methods, casting temperature, injection speed, injection pressure and vacuum conditions are given as input parameters and interfacial flow coefficient and heat flux are accepted as output parameters. With the help of these parameters, DTR, MLR and ANNR deep learning algorithms were used to estimate the interfacial heat transfer coefficient. Among these algorithms, ANNR algorithm was found to be the most accurate estimating model at the rate of 99.9%. For the obtained model, a computer program was prepared for the users to be able to see and follow the experimental results and the results obtained from the model at the same time.
机译：压力铸造工艺，基于燃料和压力效果填充和凝固液体金属的原理，使得能够获得连续产物。压力铸造过程通常涉及热过程。从铸造过程开始，铸模界面处特别形成的热电阻，以及所得的界面传热系数（IHTC）是确定所产生部分的机械和物理性质的最重要因素之一。 IHTC取决于模具温度，铸造温度，注射压力，注射率，真空应用和许多其他不可估量的参数。在本研究中，旨在确定铸模界面的传热系数和热通量，这对Alsi_8Cu_3Fe铝合金圆柱形模具几何形状的压铸质量具有显着影响。根据不同的浇铸温度，注射压力，喷射速度和真空施加到模腔的情况下进行该研究。用放置在模具中的热电偶测量温度，通过使用实验测量的温度，用有限差分法计算IHTC和热通量。在人工智能方法的应用中，给出铸造温度，注射速度，注射压力和真空条件作为输入参数，并且界面流量系数和热通量被接受为输出参数。借助这些参数，DTR，MLR和ANNR深度学习算法用于估计界面传热系数。在这些算法中，发现AnnR算法是最准确的估计模型，速率为99.9％。对于所获得的模型，为用户准备了一种计算机程序，以便能够看到和遵循实验结果，同时从模型中获得的结果。

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