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Effect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cutting

机译:陶瓷切割艾西D2钢硬质转向深冷冻处理的可加工性,微观结构和硬度

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This study examined the hard turning of AISI D2 cold work tool steel subjected to deep cryogenic processing and tempering and investigated the effects on surface roughness and tool wear. In addition, the effects of the deep cryogenic processes on mechanical properties (macro and micro hardness) and microstructure were investigated. Three groups of test samples were evaluated: conventional heat treatment (CHT), deep cryogenic treatment (DCT-36) and deep cryogenic treatment with tempering (DCTT-36). The samples in the first group were subjected to only CHT to 62 HRc hardness. The second group (DCT-36) underwent processing for 36 h at -145 degrees C after conventional heat treatment. The latter group (DCTT-36) had been subjected to both conventional heat treatment and deep cryogenic treatment followed by 2 h of tempering at 200 degrees C. In the experiments, Al2O3 + TiC matrix-based untreated mixed alumina ceramic (AB30) and Al2O3 + TiC matrix-based TiN-coated ceramic (AB2010) cutting tools were used. The artificial intelligence method known as artificial neural networks (ANNs) was used to estimate the surface roughness based on cutting speed, cutting tool, workpiece, depth of cut and feed rate. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. Three different cutting speeds (50, 100 and 150 m/min), three different feed rates (0.08, 0.16 and 0.24 mm/rev) and three different cutting depths (0.25, 0.50 and 0.75 mm) were selected. Tool wear experiments were carried out at a cutting speed of 150 m/min, a feed rate of 0.08 mm/rev and a cutting depth of 0.6 mm. As a result of the experiments, the best results for both surface roughness and tool wear were obtained with the DCTT-36 sample. When cutting tools were compared, the best results for surface roughness and tool wear were obtained with the coated ceramic tool (AB2010). The macroscopic and micro hardness values were highest for the DCT-36. From the microstructural point of view, the DCTT-36 sample showed the best results with homogeneous and thinner secondary carbide formations. (C) 2019 The Authors. Published by Elsevier B.V.
机译:本研究检测了AISI D2冷轧工具钢的硬转动,经受深度低温加工和回火,并研究了对表面粗糙度和工具磨损的影响。此外,研究了深浊度过程对机械性能(宏观和微硬度)和微观结构的影响。评估了三组试验样品:常规热处理(CHT),深冷冻处理(DCT-36)和耐回火(DCTT-36)的深浊度处理。第一组中的样品仅进行CHT至62个HRC硬度。在常规热处理后,第二组(DCT-36)在-145摄氏度下进行36小时的处理。后一组(DCTT-36)经过常规的热处理和深冷冻处理,然后在200摄氏度下进行2小时。在实验中,Al2O3 + TiC基质的未处理混合氧化铝陶瓷(AB30)和Al2O3。使用+ TiC基质的涂布陶瓷(AB2010)切割工具。称为人工神经网络(ANNS)的人工智能方法用于估计基于切割速度,切削工具,工件,切割和进料速度深度的表面粗糙度。对于人工神经网络建模,发现标准的反向传播算法是培训模型的最佳选择。选择三种不同的切割速度(50,100和150米/分钟),选择三种不同的进料速率(0.08,0.16和0.24 mm / Rev),选择三种不同的切割深度(0.25,0.50和0.75mm)。工具磨损实验以150米/分钟的切削速度进行,进给速度为0.08 mm / rev,切割深度为0.6mm。由于实验结果,用DCTT-36样品获得了表面粗糙度和工具磨损的最佳结果。比较切削工具时,用涂覆的陶瓷工具(AB2010)获得表面粗糙度和工具磨损的最佳结果。对于DCT-36,宏观和微硬度值最高。从微观结构的角度来看,DCTT-36样品显示出均匀和较薄的二次碳化物地层的最佳效果。 (c)2019年作者。 elsevier b.v出版。

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