Ultra-high precision machining of rapidly solidified aluminium (RSA) alloys for optics
- Authors: Odedeyi, Peter Babatunde
- Date: 2021-12
- Subjects: Mechatronics Surface roughness -- Measurement
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10948/55921 , vital:54400
- Description: The advancement of ultra-precision is one of the most adaptable machining processes in the manufacturing of very complex and high-quality surface structures for optics, industrial, medical, aerospace and communication applications. Studies have shown that single-point diamond turning has an outstanding ability to machine high-quality optical components at a nanometric scale. However, in a responsive cutting process, the nanometric machinability of these optical components can easily be affected by several factors. The call for increasing needs of optical systems has recently led to the development of newly modified aluminium grades of non-ferrous alloys characterized by finer microstructures, defined mechanical and physical properties. To date, there has been a lack of sufficient research into these new aluminium alloys. In modern ultra-precision machining, the high demands for smart and inexpensive cutting tools are becoming more relevant in recent precision machines. In monitoring and predicting high-quality surface, cutting forces in single point diamond turning are believed to be as critical as other machining processes due to their potential effects on the quality of surface roughness. Undermining such an important factor is a compromise between the machining process's efficiency and the increased cost of production. Therefore, a comprehensive scientific understanding of the Nano-cutting mechanics is critical, particularly on modelling and analysis of cutting force, surface roughness, chip vii formation, acoustic emission, material removal rates, and molecular dynamic simulation of the rapidly solidified aluminium alloys to bridge the gap between fundamentals and industrial-scale application. The study is divided into three essential sections. First, the development of a force sensor. Secondly, investigation of the effect of cutting parameters (i.e., cutting speed, feed rate, and cutting depth) on cutting force, acoustic emission (AE), material removal rate (MRR), chip formation, Nose radius, and surface roughness (Ra), which play a leading role in the determination of machine productivity and efficiency of single-point diamond turning of rapidly solidified aluminium alloys. Thirdly, a 3-D molecular dynamic (MD) simulation of RSA 6061 is also carried out to further understand the nanometric mechanism and characterization of the alloy. The experiment was mainly conducted using Precitech Nanoform ultra-grind 250 lathe machines on three different advanced optical aluminium alloys materials; these are RSA 443, RSA 905, and RSA 6061. , Thesis (PhD) -- Faculty of Engineering, the Built Environment and Information Technology, School of Engineering, 2021
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- Date Issued: 2021-12
Adaptive Neuro-Fuzzy Inference System modelling of surface topology in ultra-high precision diamond turning of rapidly solidified aluminium grade (RSA 443)
- Authors: Zvikomborero, Hweju
- Date: 2020
- Subjects: Mechatronics Surface roughness -- Measurement
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/49441 , vital:41721
- Description: Surface roughness prediction is a crucial stage during product manufacturing since it acts as a quality indicator. This investigative research thesis presents an online surface roughness prediction, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) model during Ultra-High Precision Diamond Turning (UHPDT) of Rapidly Solidified Aluminium (RSA-443) using water and kerosene as coolants. Based on the Taguchi L9 orthogonal array, the cutting parameters (spindle speed, depth of cut and feed rate) are varied at three levels. Acoustic Emission (AE) signals are detected during the UHPDT process using a piezoelectric sensor. Spindle speed, depth of cut, feed rate, AE root mean square, prominent frequency and peak rate are considered as model inputs in this thesis. The experimental results reveal that a better surface finish is obtained using water coolant in comparison to kerosene coolant. Mean Absolute Percentage Error (MAPE) based comparison between ANFIS and Response Surface Method (RSM) is carried out. In this study, the ANFIS model has a prediction accuracy of 79.42% and 69.40% on water-based and kerosene-based results respectively. The RSM model yields higher prediction accuracies of 98.59% and 95.55% on water-based and kerosene-based results respectively.
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- Date Issued: 2020