Resistance to crack propagation of steel 40Kh in relation to tempering temperature

1972 ◽  
Vol 13 (7-8) ◽  
pp. 614-615
Author(s):  
M. A. Kramarov ◽  
Yu. V. Shakhnazarov
Keyword(s):  
Crack Propagation ◽  
Steel 40Kh ◽  
Tempering Temperature

Mechanism of irregular crack-propagation in thermal controlled fracture of ceramics induced by microwave

2020 ◽  
Vol 21 (6) ◽  
pp. 610
Author(s):  
Xiaoliang Cheng ◽  
Chunyang Zhao ◽  
Hailong Wang ◽  
Yang Wang ◽  
Zhenlong Wang
Keyword(s):  
Crack Propagation ◽  
Industrial Application ◽  
Ceramic Materials ◽  
Advanced Technology ◽  
Analytical Models ◽  
Propagation Mechanism ◽  
Unstable Crack ◽  
Fracture Simulation ◽  
Initial Stage ◽  
Controlled Fracture

Microwave cutting glass and ceramics based on thermal controlled fracture method has gained much attention recently for its advantages in lower energy-consumption and higher efficiency than conventional processing method. However, the irregular crack-propagation is problematic in this procedure, which hinders the industrial application of this advanced technology. In this study, the irregular crack-propagation is summarized as the unstable propagation in the initial stage, the deviated propagation in the middle stage, and the non-penetrating propagation in the end segment based on experimental work. Method for predicting the unstable propagation in the initial stage has been developed by combining analytical models with thermal-fracture simulation. Experimental results show good agreement with the prediction results, and the relative deviation between them can be <5% in cutting of some ceramics. The mechanism of deviated propagation and the non-penetrating propagation have been revealed by simulation and theoretical analysis. Since this study provides effective methods to predict unstable crack-propagation in the initial stage and understand the irregular propagation mechanism in the whole crack-propagation stage in microwave cutting ceramics, it is of great significance to the industrial application of thermal controlled fracture method for cutting ceramic materials using microwave.

High Cycle Fatigue and Fatigue Crack Propagation Behaviors of Modified A7075-T73 Alloy

2014 ◽  
Vol 52 (4) ◽  
pp. 283-291 ◽  
Author(s):  
Gwan Yeong Kim ◽  
Kyu Sik Kim ◽  
Joong Cheol Park ◽  
Shae Kwang Kim ◽  
Young Ok Yoon ◽  
...  
Keyword(s):  
Fatigue Crack ◽  
Crack Propagation ◽  
Fatigue Crack Propagation ◽  
High Cycle Fatigue ◽  
Cycle Fatigue

Wear volume prediction of AISI H13 die steel using response surface methodology and artificial neural network

2020 ◽  
Vol 14 (2) ◽  
pp. 6789-6800
Author(s):  
Vishal Jagota ◽  
Rajesh Kumar Sharma
Keyword(s):  
Neural Network ◽  
Heat Treatment ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Sliding Wear ◽  
Austenitizing Temperature ◽  
Tempering Temperature ◽  
Die Steel ◽  
Artificial Neural ◽  
Tempering Time

Resistance to wear of hot die steel is dependent on its mechanical properties governed by the microstructure. The required properties for given application of hot die steel can be obtained with control the microstructure by heat treatment parameters. In the present paper impact of different heat treatment parameters like austenitizing temperature, tempering time, tempering temperature is studied using response surface methodology (RSM) and artificial neural network (ANN) to predict sliding wear of H13 hot die steel. After heat treating samples at austenitizing temperature of 1020°C, 1040°C and 1060°C; tempering temperature 540°C, 560°C and 580°C; tempering time 1hour, 2hours and 3hours, experimentation on pin-on-disc tribo-tester is done to measure the sliding wear of H13 die steel. Box-Behnken design is used to develop a regression model and analysis of variance technique is used to verify the adequacy of developed model in case of RSM. Whereas, multi-layer feed-forward backpropagation architecture with input layer, single hidden layer and an output layer is used in ANN. It was found that ANN proves to be a better tool to predict sliding wear with more accuracy. Correlation coefficient R2 of the artificial neural network model is 0.986 compared to R2 of 0.957 for RSM. However, impact of input parameter interactions can only be analysed using response surface method. In addition, sensitivity analysis is done to determine the heat treatment parameter exerting most influence on the wear resistance of H13 hot die steel and it showed that tempering time has maximum influence on wear volume, followed by tempering temperature and austenitizing temperature. The prediction models will help to estimate the variation in die lifetime by finding the amount of wear that will occur during use of hot die steel, if the heat treatment parameters are varied to achieve different properties.

Sign in / Sign up

Export Citation Format

Share Document