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Duplex ceramic coating produced by low temperature thermo-reactive deposition and diffusion on the cold work tool steel substrate: Thermodynamics, kinetics and modeling

机译:通过低温热反应沉积和扩散在冷作工具钢基材上产生的双层陶瓷涂层:热力学,动力学和模型

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摘要

Specimens of DIN 100MnCrW4 steel (type 01 tool steel) have been cut and prepared for performing a duplex surface treatment involving nitriding and low temperature vanadium thermo-reactive deposition and diffusion (TRD) technique. The TRD process was performed in a molten salt bath at different temperatures of 575, 650 and 725 degrees C for 1-30 h. The treatment formed a vanadium carbonitride coating with the thickness up to 10.5 mu m on a hardened diffusion zone. Characterizations by means of an optical microscope (OM), scanning electron microscope equipped with energy dispersive X-ray spectrometer (SEM-EDS) and X-ray diffraction analysis (XRD) indicated that the compact and dense coating mainly consisted of V(C,N) and V-2(C,N) phases. All the growth processes of the formed vanadium carbonitride layer obtained by TRD followed a parabolic kinetics while the calculated activation energy (Q) for the treatment was 181.1 kJ/mol. An artificial neural network (ANN) based model for predicting the layer thickness of ceramic coatings was presented. Constructing the model, training, validating and testing of experimental results from 72 different specimens were conducted. The data used as inputs in the proposed model were arranged in a format of five parameters that comprised of "pre-nitriding time", "ferro-vanadium particle size", "ferro-vanadium weight percent", "salt bath temperature" and "coating time". Accordingly, the thickness of duplex coating in each specimen was estimated accurately. Finally, the proposed ANN-based model showed a strong potential for predicting the layer thickness of duplex ceramic coating performed by the TRD technique on the substrate of cold work tool steel. (C) 2015 Elsevier Ltd and Techna Group S.r.l. All rights reserved.
机译:已将DIN 100MnCrW4钢(01型工具钢)的样品切割并准备用于进行双相表面处理,包括氮化和低温钒热反应沉积和扩散(TRD)技术。 TRD工艺在熔融盐浴中于575、650和725摄氏度的不同温度下进行1-30小时。该处理在硬化的扩散区上形成了厚度高达10.5μm的碳氮化钒涂层。通过配备有能量色散X射线光谱仪(SEM-EDS)的光学显微镜(OM),扫描电子显微镜和X射线衍射分析(XRD)进行表征,结果表明致密致密的涂层主要由V(C, N)和V-2(C,N)相。通过TRD获得的所形成的碳氮化钒层的所有生长过程均遵循抛物线动力学,而计算出的用于处理的活化能(Q)为181.1 kJ / mol。提出了一种基于人工神经网络(ANN)的陶瓷涂层层厚预测模型。建立了模型,对来自72个不同样本的实验结果进行了训练,验证和测试。在建议的模型中,用作输入数据的数据按以下五个参数的格式排列:“预氮化时间”,“钒铁粒度”,“钒铁重量百分比”,“盐浴温度”和“涂层时间”。因此,准确地估计了每个样品中的双面涂层的厚度。最后,所提出的基于ANN的模型显示出强大的潜力,可以预测通过TRD技术在冷作工具钢的基材上进行的双相陶瓷涂层的层厚。 (C)2015 Elsevier Ltd和Techna Group S.r.l.版权所有。

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