Ultra-high precision machining of contact lens polymers
- Authors: Olufayo, Oluwole Ayodeji
- Date: 2015
- Subjects: Contact lenses , Polymers
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/3001 , vital:20385
- Description: Contact lens manufacture requires a high level of accuracy and surface integrity in the range of a few nanometres. Amidst numerous optical manufacturing techniques, single-point diamond turning is widely employed in the making of contact lenses due to its capability of producing optical surfaces of complex shapes and nanometric accuracy. For process optimisation, it is ideal to assess the effects of various conditions and also establish their relationships with the surface finish. Presently, there is little information available on the performance of single point diamond turning when machining contact lens polymers. Therefore, the research work undertaken herewith is aimed at testing known facts in contact lens diamond turning and investigating the performance of ultra-high precision manufacturing of contact lens polymers. Experimental tests were conducted on Roflufocon E, which is a commercially available contact lens polymer and on Precitech Nanoform Ultra-grind 250 precision machining. Tests were performed at varying cutting feeds, speed and depth of cut. Initial experimental tests investigated the influence of process factors affecting surface finish in the UHPM of lenses. The acquired data were statistically analysed using Response Surface Method (RSM) to create a model of the process. Subsequently, a model which uses Runge-Kutta’s fourth order non-linear finite series scheme was developed and adapted to deduce the force occurring at the tool tip. These forces were also statistically analysed and modelled to also predict the effects process factors have on cutting force. Further experimental tests were aimed at establishing the presence of the triboelectric wear phenomena occurring during polymer machining and identifying the most influential process factors. Results indicate that feed rate is a significant factor in the generation of high optical surface quality. In addition, the depth of cut was identified as a significant factor in the generation of low surface roughness in lenses. The influence some of these process factors had was notably linked to triboelectric effects. This tribological effect was generated from the continuous rubbing action of magnetised chips on the cutting tool. This further stresses the presence of high static charging during cutting. Moderately humid cutting conditions presented an adequate means for static charge control and displayed improved surface finishes. In all experimental tests, the feed rate was identified as the most significant factor within the range of cutting parameters employed. Hence, the results validated the fact that feed rate had a high influence in polymer machining. The work also established the relationship on how surface roughness of an optical lens responded to monitoring signals and parameters such as force, feed, speed and depth of cut during machining and it generated models for prediction of surface finishes and appropriate selection of parameters. Furthermore, the study provides a molecular simulation analysis for validating observed conditions occurring at the nanometric scale in polymer machining. This is novel in molecular polymer modelling. The outcome of this research has contributed significantly to the body of knowledge and has provided basic information in the area of precision manufacturing of optical components of high surface integrity such as contact lenses. The application of the research findings presented here cuts across various fields such as medicine, semi-conductors, aerospace, defence, telecom, lasers, instrumentation and life sciences.
- Full Text:
- Date Issued: 2015
- Authors: Olufayo, Oluwole Ayodeji
- Date: 2015
- Subjects: Contact lenses , Polymers
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/3001 , vital:20385
- Description: Contact lens manufacture requires a high level of accuracy and surface integrity in the range of a few nanometres. Amidst numerous optical manufacturing techniques, single-point diamond turning is widely employed in the making of contact lenses due to its capability of producing optical surfaces of complex shapes and nanometric accuracy. For process optimisation, it is ideal to assess the effects of various conditions and also establish their relationships with the surface finish. Presently, there is little information available on the performance of single point diamond turning when machining contact lens polymers. Therefore, the research work undertaken herewith is aimed at testing known facts in contact lens diamond turning and investigating the performance of ultra-high precision manufacturing of contact lens polymers. Experimental tests were conducted on Roflufocon E, which is a commercially available contact lens polymer and on Precitech Nanoform Ultra-grind 250 precision machining. Tests were performed at varying cutting feeds, speed and depth of cut. Initial experimental tests investigated the influence of process factors affecting surface finish in the UHPM of lenses. The acquired data were statistically analysed using Response Surface Method (RSM) to create a model of the process. Subsequently, a model which uses Runge-Kutta’s fourth order non-linear finite series scheme was developed and adapted to deduce the force occurring at the tool tip. These forces were also statistically analysed and modelled to also predict the effects process factors have on cutting force. Further experimental tests were aimed at establishing the presence of the triboelectric wear phenomena occurring during polymer machining and identifying the most influential process factors. Results indicate that feed rate is a significant factor in the generation of high optical surface quality. In addition, the depth of cut was identified as a significant factor in the generation of low surface roughness in lenses. The influence some of these process factors had was notably linked to triboelectric effects. This tribological effect was generated from the continuous rubbing action of magnetised chips on the cutting tool. This further stresses the presence of high static charging during cutting. Moderately humid cutting conditions presented an adequate means for static charge control and displayed improved surface finishes. In all experimental tests, the feed rate was identified as the most significant factor within the range of cutting parameters employed. Hence, the results validated the fact that feed rate had a high influence in polymer machining. The work also established the relationship on how surface roughness of an optical lens responded to monitoring signals and parameters such as force, feed, speed and depth of cut during machining and it generated models for prediction of surface finishes and appropriate selection of parameters. Furthermore, the study provides a molecular simulation analysis for validating observed conditions occurring at the nanometric scale in polymer machining. This is novel in molecular polymer modelling. The outcome of this research has contributed significantly to the body of knowledge and has provided basic information in the area of precision manufacturing of optical components of high surface integrity such as contact lenses. The application of the research findings presented here cuts across various fields such as medicine, semi-conductors, aerospace, defence, telecom, lasers, instrumentation and life sciences.
- Full Text:
- Date Issued: 2015
Tool wear monitoring in end milling of mould steel using acoustic emission
- Authors: Olufayo, Oluwole Ayodeji
- Subjects: Acoustic emission testing , Tool-steel
- Language: English
- Type: Thesis , Masters , MEngineering
- Identifier: vital:9651 , http://hdl.handle.net/10948/d1014688
- Description: Today’s production industry is faced with the challenge of maximising its resources and productivity. Tool condition monitoring (TCM) is an important diagnostic tool and if integrated in manufacturing, machining efficiency will increase as a result of reducing downtime resulting from tool failures by intensive wear. The research work presented in the study highlights the principles in tool condition monitoring and identifies acoustic emission (AE) as a reliable sensing technique for the detection of wear conditions. It reviews the importance of acoustic emission as an efficient technique and proposes a TCM model for the prediction of tool wear. The study presents a TCM framework to monitor an end-milling operation of H13 tool steel at different cutting speeds and feed rates. For this, three industrial acoustic sensors were positioned on the workpiece. The framework identifies a feature selection, extraction and conditioning process and classifies AE signals using an artificial neural network algorithm to create an autonomous system. It concludes by recognizing the mean and rms features as viable features in the identification of tool state and observes that chip coloration provides direct correlation to the temperature of machining as well as tool condition. This proposed model is aimed at creating a timing schedule for tool change in industries. This model ultimately links the rate of wear formation to characteristic AE features.
- Full Text:
- Authors: Olufayo, Oluwole Ayodeji
- Subjects: Acoustic emission testing , Tool-steel
- Language: English
- Type: Thesis , Masters , MEngineering
- Identifier: vital:9651 , http://hdl.handle.net/10948/d1014688
- Description: Today’s production industry is faced with the challenge of maximising its resources and productivity. Tool condition monitoring (TCM) is an important diagnostic tool and if integrated in manufacturing, machining efficiency will increase as a result of reducing downtime resulting from tool failures by intensive wear. The research work presented in the study highlights the principles in tool condition monitoring and identifies acoustic emission (AE) as a reliable sensing technique for the detection of wear conditions. It reviews the importance of acoustic emission as an efficient technique and proposes a TCM model for the prediction of tool wear. The study presents a TCM framework to monitor an end-milling operation of H13 tool steel at different cutting speeds and feed rates. For this, three industrial acoustic sensors were positioned on the workpiece. The framework identifies a feature selection, extraction and conditioning process and classifies AE signals using an artificial neural network algorithm to create an autonomous system. It concludes by recognizing the mean and rms features as viable features in the identification of tool state and observes that chip coloration provides direct correlation to the temperature of machining as well as tool condition. This proposed model is aimed at creating a timing schedule for tool change in industries. This model ultimately links the rate of wear formation to characteristic AE features.
- Full Text:
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