• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.6
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• pp.871-877
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• 2012

New materials widely used for automobile related industry, aircraft, space development area are mostly high hardness materials. The hardness value of some hardened materials is over HRC45 and machining of this hardened materials is called as hard turning. Hard turning has its advantage on processing flexibility, cycle time and tool cost reduction. Also this process obtains high efficiency in processing and precise surface roughness through application of the CBN tools. In hard turning process with CBN tool, surface integrity is the important factor for considering the design of machine part and component under high stress and load conditions. A purpose of this study is to analyze optimal condition in hard turning process of AISI 52100 steel (HRC62) with high CBN and low CBN on turning characteristics, tool wear mechanism comparison and surface integrity.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.6
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• pp.16-21
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• 2005

The cutting characteristics of hardened steel(AISI 52100) by PCBN tools is investigated with respect to cutting force, workpiece surface roughness and tool flank wear by the vision system. Hard Owning is carried out with various cutting conditions; spindle rotational speed, depth of cut and feed rate. Backpropagation neural networks(BPNs) are used for detection of tool wear. The input vectors of neural network comprise of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output is the tool wear state which is either usable or failure. The detection of the abnormal states using BPNs achieves $96.8\%$ reliability even when the spindle rotational speed and feedrate are changed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.5
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• pp.469-476
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• 2009

Hard facing overlay welding in high chromium carbide is a representative way of extending the fatigue life or recompensing damage, because workpiece surface is uniformly overlay-welded by alloy material. In general, grinding process is currently used for finish due to hardness of weld material. The development of tool material, such as PCBN, has made it possible to use turning instead of grinding. There are many advantages of hard Owning, as lower equipment costs, shorter setup time, fewer process steps, higher material removal rate, better surface integrity and the elimination of cutting fluid. In this paper, machining characteristics and cutting performance are examined to investigate turning possibility of overly welding in high chromium carbide.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.452-455
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• 2005

The cutting characteristics of hardened steel by a PCBN tool is investigated with respect to workpiece surface roughness, cutting force and tool flank wear of the vision system. Backpropagation neural networks (BPNs) were used for detection of tool wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output was the tool wear state which was either usable or failure. Hard turning experiments with various spindle rotational speed and feed rates were carried out. The learning process was performed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.