Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

Search results for: Talysurf

3 Wear Measuring and Wear Modelling Based On Archard, ASTM, and Neural Network Models

Authors: A. Shebani, C. Pislaru


Wear of materials is an everyday experience and has been observed and studied for long time. The prediction of wear is a fundamental problem in the industrial field, mainly correlated to the planning of maintenance interventions and economy. Pin-on-disc test is the most common test which is used to study the wear behaviour. In this paper, the pin-on-disc (AEROTECH UNIDEX 11) is used for the investigation of the effects of normal load and hardness of material on the wear under dry and sliding conditions. In the pin-on-disc rig, two specimens were used; one, a pin which is made of steel with a tip, is positioned perpendicular to the disc, where the disc is made of aluminium. The pin wear and disc wear were measured by using the following instruments: The Talysurf instrument, a digital microscope, and the alicona instrument; where the Talysurf profilometer was used to measure the pin/disc wear scar depth, and the alicona was used to measure the volume loss for pin and disc. After that, the Archard model, American Society for Testing and Materials model (ASTM), and neural network model were used for pin/disc wear modelling and the simulation results are implemented by using the Matlab program. This paper focuses on how the alicona can be considered as a powerful tool for wear measurements and how the neural network is an effective algorithm for wear estimation.

Keywords: wear modelling, Archard Model, ASTM Model, Neural Networks Model, Pin-on-disc Test, Talysurf, digital microscope, Alicona

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2 The Effect of Surface Roughness on the Fatigue Life of SCM440 Steel

Authors: C. Han, H. Kim, S. Park


The purpose of the present study is to analyze the effect of surface roughness on fatigue life of SCM440 steel. Two groups of specimens were made from SCM440 steel with and without surface polished after forging process and resulted in different values of surface roughness. The difference of the surface roughness between two groups was clearly distinguished even to the naked eye. Surface roughness of both groups of the specimens was quantitatively measured by a roughness measuring device, Talysurf series2 (Taylor-Hobson Co., USA). Average roughness (Ra) and maximum roughness depth (Rmax) values were obtained by scanning 45 mm with a speed of 0.25 mm/s. Fatigue tests were conducted using a three-point bending method with a cyclic sinusoidal profile of 5 Hz, stress ratio of R = 0.1 and reference life for fatigue limit of 1 × 106 cycles. Ra and Rmax without surface polished were 10.497 ± 1.721 μm and 87.936 ± 16.210 μm, respectively while those values with surface polished were much smaller (ongoing measurements). Fatigue lives of the surface-polished specimens achieved approximately 1 × 106 cycles under the maximum stress of 900 MPa, which was 10 times longer than those of the surface-untreated specimens with an average roughness of 10.082 μm. The results showed that an increase in surface roughness values led to a decrease in fatigue lives.

Keywords: surface roughness, fatigue test, fatigue life, SCM440 steel

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1 Analysis of Surface Hardness, Surface Roughness and near Surface Microstructure of AISI 4140 Steel Worked with Turn-Assisted Deep Cold Rolling Process

Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma, K. Jagannath, Achutha Kini U.


In the present study, response surface methodology has been used to optimize turn-assisted deep cold rolling process of AISI 4140 steel. A regression model is developed to predict surface hardness and surface roughness using response surface methodology and central composite design. In the development of predictive model, deep cold rolling force, ball diameter, initial roughness of the workpiece, and number of tool passes are considered as model variables. The rolling force and the ball diameter are the significant factors on the surface hardness and ball diameter and numbers of tool passes are found to be significant for surface roughness. The predicted surface hardness and surface roughness values and the subsequent verification experiments under the optimal operating conditions confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface hardness and surface roughness is calculated as 0.16% and 1.58% respectively. Using the optimal processing parameters, the hardness is improved from 225 to 306 HV, which resulted in an increase in the near surface hardness by about 36% and the surface roughness is improved from 4.84µm to 0.252 µm, which resulted in decrease in the surface roughness by about 95%. The depth of compression is found to be more than 300µm from the microstructure analysis and this is in correlation with the results obtained from the microhardness measurements. Taylor Hobson Talysurf tester, micro Vickers hardness tester, optical microscopy and X-ray diffractometer are used to characterize the modified surface layer.

Keywords: hardness, response surface methodology, microstructure, central composite design, deep cold rolling, surface roughness

Procedia PDF Downloads 318