- Title
- Ultraprecision Diamond Turning of Monocrystalline Germanium
- Creator
- Adeleke, Adeniyi Kehinde
- Subject
- Precision
- Subject
- Diamond Turning
- Date Issued
- 2021-04
- Date
- 2021-04
- Type
- Master's theses
- Type
- Thesis
- Identifier
- http://hdl.handle.net/10948/44453
- Identifier
- vital:37835
- Description
- Infrared lens production demands a surface with a high degree of accuracy and integrity. Surface roughness is a critical index of the measure of any product’s surface integrity. As a result of this, ultra-high machining technology has enjoyed extensive application, due to the continuous request for components within the range of 1 – 10nm roughness value. This technology has brought about the increased productivity and manufacture of quality products with a top-notch surface finish. Brittle materials such as germanium are hard to machine through the conventional processes such as lapping and polishing. Hence, the ultra-precision machining technology based on single point diamond turning (SPDT), is now been applied to machine germanium in the ductile mode, where material chip removal occurs by plastic deformation instead of a brittle fracture. During machining, selecting the optimal cutting conditions which includes cutting parameters and tool geometry, will not only improve productivity but ensure the minimisation of operating cost. In this research work, SPDT operation was used to conduct two experiments on a (monocrystalline germanium) workpiece. The first experiment was carried out using a diamond tool with a 1.5 mm nose radius, while the second experiment employed the use of a tool having a nose radius of 1.0 mm. A combination of machining parameters for each of the experimental runs were derived from a Box-Behnken method of design and the surface roughness was measured at each interval for both experiments with the aid of a Taylor Hopson PGI Dimension XL profilometer. Acoustic emission (AE) was also used as a quality sensing and tool-monitoring technique, to acquire signals and give indications of the expected surface roughness. Predictive models based on response surface methodology (RSM) and artificial neural networks (ANN), were developed for determining surface roughness. Optimisation was performed using RSM to determine the optimal set of machining factors, which results in optimal condition of the output response. Further investigations on the acquired signals were carried out using signal-processing techniques. Time-domain and time-frequency domain features acquired from the AE signals, together with the process parameters, were employed as input variables in the neural network design, having shown a good association with the surface roughness. ix Conclusively, it can be observed that the predictive model results and the experimental roughness measurements are in good agreement with each other. For accuracy and cost of computation, the RSM and ANN developed models for single-crystal germanium are compared using mean absolute error (MAE).
- Description
- Thesis (M.Eng) -- Faculty of Engineering, the built environment & Information Technology, 2021
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (xxii, 177 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Engineering, the built environment & Information Technology
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Francis Mathe.pdf | 5 MB | Adobe Acrobat PDF | View Details Download |