Hard turning is a machining process where a single point cutting tool removes material harder than 45 HRC from a rotating workpiece. Due to the advent of polycrystalline cubic boron nitride (PCBN) cutting tools and improved machine tool designs, hard turning is an attractive alternative to grinding for steel parts within the range of 58-68 HRC, such as bearings. There is reluctance in industry to adopt hard turning because of a defect called white layer. White layer is a hard, 1-5 ě deep layer on the surface of the specimen that resists etching and therefore appears white on a micrograph. When aggressive cutting parameters are used, even using a new tool, white layer is expected. If more conservative parameters are selected, one does not expect white layer. There is some debate if white layer actually decreases the strength or fatigue life of a part, but nevertheless it is not well understood and therefore is avoided.
This research examines the use of two different non-destructive evaluation (NDE) sensors to detect white layer in hard turned components. The first, called a Barkhausen sensor, is an NDE instrument that works by applying a magnetic field to a ferromagnetic metal and observing the induced electrical field. The amplitude of the signal produced by the induced electrical field is affected by the hardness of the material and surface residual stresses.
This work also examines the electrochemical properties of white layer defects using electrochemical impedance spectroscopy. This idea is verified by measuring the electrochemical potential of surfaces with white layer and comparing to surfaces without any. Further corrosion tests using the electrochemical impedance spectroscopy method indicate that parts with white layer have a higher corrosion rate.
The goal of this study is to determine if it is possible to infer white layer thickness reliably using either the Barkhausen sensor or electrochemical impedance spectroscopy (EIS). Measurements from both sensors are compared with direct observation of the microstructure in order to determine if either sensor can reliably detect the presence of white layer.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/6982 |
| Date | 20 January 2005 |
| Creators | Harrison, Ian Spencer |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | 2179060 bytes, application/pdf |
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