A genetic algorithm to obtain optimum parameters for a halcon vision system
- Authors: Fulton, Dale Meares
- Date: 2017
- Subjects: Genetic algorithms , Artificial intelligence , Automation , User interfaces (Computer systems)
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/29751 , vital:30774
- Description: This report discusses the optimisation of a HALCON vision system using artificial intelligence, specifically a genetic algorithm. Within industrial applications, vision systems are often used for automated part inspection and quality control. A number of vision system parameters are to be selected when setting up a vision system. Since each vision system application differs, there is no specific set of optimal parameters. Parameters are selected during installation using a trial and error method. As a result, there is a need for an automated process for obtaining suitable vision system parameters. Within this report, research was conducted on both vision systems, genetic algorithms and integration of the two. A physical vision system was designed and developed utilising HALCON vision software. A genetic algorithm was then developed and integrated with the vision system. After integration, experimental testing was performed on the genetic algorithm in order to determine the ideal genetic algorithm control parameters which yield ideal genetic algorithm performance. Once the ideal genetic algorithm was obtained, the genetic algorithm was applied to the vision system in order to obtain optimal vision system parameters. Results showed that applying the genetic algorithm to the vision system optimised the vision system performance well.
- Full Text:
- Date Issued: 2017
- Authors: Fulton, Dale Meares
- Date: 2017
- Subjects: Genetic algorithms , Artificial intelligence , Automation , User interfaces (Computer systems)
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/29751 , vital:30774
- Description: This report discusses the optimisation of a HALCON vision system using artificial intelligence, specifically a genetic algorithm. Within industrial applications, vision systems are often used for automated part inspection and quality control. A number of vision system parameters are to be selected when setting up a vision system. Since each vision system application differs, there is no specific set of optimal parameters. Parameters are selected during installation using a trial and error method. As a result, there is a need for an automated process for obtaining suitable vision system parameters. Within this report, research was conducted on both vision systems, genetic algorithms and integration of the two. A physical vision system was designed and developed utilising HALCON vision software. A genetic algorithm was then developed and integrated with the vision system. After integration, experimental testing was performed on the genetic algorithm in order to determine the ideal genetic algorithm control parameters which yield ideal genetic algorithm performance. Once the ideal genetic algorithm was obtained, the genetic algorithm was applied to the vision system in order to obtain optimal vision system parameters. Results showed that applying the genetic algorithm to the vision system optimised the vision system performance well.
- Full Text:
- Date Issued: 2017
Diamond turning of contact lens polymers
- Authors: Liman, Muhammad Mukhtar
- Date: 2017
- Subjects: Diamond turning Contact lenses , Electrostatic lenses Lenses -- Design and construction Neural networks (Computer science)
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/19223 , vital:28789
- Description: Contact lens production requires high accuracy and good surface integrity. Surface roughness is generally used to measure the index quality of a turning process. It has been an important response because it has direct influence toward the part performance and the production cost. Hence, choosing optimal cutting parameters will not only improve the quality measure but also the productivity. In this study, an ONSI-56 (Onsifocon A) contact lens buttons were used to investigate the triboelectric phenomena and the effects of turning parameters on surface finish of the lens materials. ONSI-56 specimens are machined by Precitech Nanoform Ultra-grind 250 precision machine and the roughness values of the diamond turned surfaces are measured by Taylor Hopson PGI Profilometer. Electrostatics values were measured using electrostatic voltmeter. An artificial neural network (ANN) and response surface (RS) model were developed to predict surface roughness and electrostatic discharge (ESD) on the turned ONSI-56. In the development of predictive models, turning parameters of cutting speed, feed rate and depth of cut were considered as model variables. The required data for predictive models were obtained by conducting a series of turning test and measuring the surface roughness and ESD data. Good agreement is observed between the predictive models results and the experimental measurements. The ANN and RSM models for ONSI-56 are compared with each other using mean absolute percentage error (MAPE) for accuracy and computational cost.
- Full Text:
- Date Issued: 2017
- Authors: Liman, Muhammad Mukhtar
- Date: 2017
- Subjects: Diamond turning Contact lenses , Electrostatic lenses Lenses -- Design and construction Neural networks (Computer science)
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/19223 , vital:28789
- Description: Contact lens production requires high accuracy and good surface integrity. Surface roughness is generally used to measure the index quality of a turning process. It has been an important response because it has direct influence toward the part performance and the production cost. Hence, choosing optimal cutting parameters will not only improve the quality measure but also the productivity. In this study, an ONSI-56 (Onsifocon A) contact lens buttons were used to investigate the triboelectric phenomena and the effects of turning parameters on surface finish of the lens materials. ONSI-56 specimens are machined by Precitech Nanoform Ultra-grind 250 precision machine and the roughness values of the diamond turned surfaces are measured by Taylor Hopson PGI Profilometer. Electrostatics values were measured using electrostatic voltmeter. An artificial neural network (ANN) and response surface (RS) model were developed to predict surface roughness and electrostatic discharge (ESD) on the turned ONSI-56. In the development of predictive models, turning parameters of cutting speed, feed rate and depth of cut were considered as model variables. The required data for predictive models were obtained by conducting a series of turning test and measuring the surface roughness and ESD data. Good agreement is observed between the predictive models results and the experimental measurements. The ANN and RSM models for ONSI-56 are compared with each other using mean absolute percentage error (MAPE) for accuracy and computational cost.
- Full Text:
- Date Issued: 2017
Electrostatic discharge and roughness modelling in diamond turning of contact lenses
- Authors: Kopi, Fundiswa
- Date: 2017
- Subjects: Diamond turning Contact lenses
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/18783 , vital:28723
- Description: With the increased application of ultra-high precision machining of polymers and the limited research in single point diamond turning (SPDT) of contact lens polymers, it became imperative to gather understanding on the production of contact lenses using the above-mentioned technology. A limiting factor in SPDT of polymers is wear of the diamond tool, resulting into poor surface finish due to unintended charges generated as a result of the contact/rubbing action between the cutting tool and the cut material. Central Composite Design (CCD) Face Centred experimental design was developed and applied to the SPDT of ONSI-56 and Polymethly methacrylate (PMMA) contact lens buttons. An electrostatic sensor coupled to a computer monitored the electrostatic discharge generated and a profilometer measured the surface roughness. The Response Surface Method (RSM) was utilised during the development of predictive models for both the surface roughness and the electrostatic discharge generated, to deduce the effects of cutting parameters during machining. The cutting speed and the feed rate deemed as the influential parameters on the surface roughness and electrostatic discharge, for both materials. The depth of cut induced more charge generation for PMMA. Predictive models were successfully developed and they were aimed at creating a database a guide to the SPDT of contact lens polymers.
- Full Text:
- Date Issued: 2017
- Authors: Kopi, Fundiswa
- Date: 2017
- Subjects: Diamond turning Contact lenses
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/18783 , vital:28723
- Description: With the increased application of ultra-high precision machining of polymers and the limited research in single point diamond turning (SPDT) of contact lens polymers, it became imperative to gather understanding on the production of contact lenses using the above-mentioned technology. A limiting factor in SPDT of polymers is wear of the diamond tool, resulting into poor surface finish due to unintended charges generated as a result of the contact/rubbing action between the cutting tool and the cut material. Central Composite Design (CCD) Face Centred experimental design was developed and applied to the SPDT of ONSI-56 and Polymethly methacrylate (PMMA) contact lens buttons. An electrostatic sensor coupled to a computer monitored the electrostatic discharge generated and a profilometer measured the surface roughness. The Response Surface Method (RSM) was utilised during the development of predictive models for both the surface roughness and the electrostatic discharge generated, to deduce the effects of cutting parameters during machining. The cutting speed and the feed rate deemed as the influential parameters on the surface roughness and electrostatic discharge, for both materials. The depth of cut induced more charge generation for PMMA. Predictive models were successfully developed and they were aimed at creating a database a guide to the SPDT of contact lens polymers.
- Full Text:
- Date Issued: 2017
Friction welding of thin walled zircaloy-4 tubes for the nuclear industry
- Authors: Koloi, Nthatisi Dinah
- Date: 2017
- Subjects: Friction welding Zirconium alloys
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/18794 , vital:28724
- Description: This work reports on the process development of solid state welding as an alternative joining process for assembling Zircaloy-4 fuel rod components for the nuclear industry. A typical fuel rod consists of a thin tube that is blocked at both ends by end-caps. The welding of the thin wall tubes onto the end-caps is currently accomplished by employing fusion techniques. Due to limited thin wall Zircaloy-4 tube supplied, preliminary welding was initially performed with thin wall 316L stainless steel tube for the development of a joint geometry and establishment of an experimental welding and testing setup. A suitable joint geometry that would achieve higher static strength equal or above that of the parent material, as well as complete circumferential bonding was investigated through welding a tube on different volume interface geometries of the end-caps. Higher joint efficiency was obtained from a tube-to-tube joint geometry that allowed sufficient frictional heat input at the interface. Consequently, the successful joint geometry was employed to develop a friction welding process for the joining of thin wall Zircaloy-4 tubes. The influential process parameters, axial force, rotational speed and upset distance were varied during the investigation. The completed weld joints were evaluated by visual, metallurgical and mechanical means. Successful welds showed complete circumferential bonding and high joint efficiency that was above the parent plate material as well as parent tube material. The evaluation of the microstructure showed transformation of grain structure on the heat affected zone (HAZ) and friction weld zone when compared to the parent materials. Even though, this work could not resolve inner flash formation, there is enough evidence that friction welding can be used for assembling fuel rod components in the nuclear industry.
- Full Text:
- Date Issued: 2017
- Authors: Koloi, Nthatisi Dinah
- Date: 2017
- Subjects: Friction welding Zirconium alloys
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/18794 , vital:28724
- Description: This work reports on the process development of solid state welding as an alternative joining process for assembling Zircaloy-4 fuel rod components for the nuclear industry. A typical fuel rod consists of a thin tube that is blocked at both ends by end-caps. The welding of the thin wall tubes onto the end-caps is currently accomplished by employing fusion techniques. Due to limited thin wall Zircaloy-4 tube supplied, preliminary welding was initially performed with thin wall 316L stainless steel tube for the development of a joint geometry and establishment of an experimental welding and testing setup. A suitable joint geometry that would achieve higher static strength equal or above that of the parent material, as well as complete circumferential bonding was investigated through welding a tube on different volume interface geometries of the end-caps. Higher joint efficiency was obtained from a tube-to-tube joint geometry that allowed sufficient frictional heat input at the interface. Consequently, the successful joint geometry was employed to develop a friction welding process for the joining of thin wall Zircaloy-4 tubes. The influential process parameters, axial force, rotational speed and upset distance were varied during the investigation. The completed weld joints were evaluated by visual, metallurgical and mechanical means. Successful welds showed complete circumferential bonding and high joint efficiency that was above the parent plate material as well as parent tube material. The evaluation of the microstructure showed transformation of grain structure on the heat affected zone (HAZ) and friction weld zone when compared to the parent materials. Even though, this work could not resolve inner flash formation, there is enough evidence that friction welding can be used for assembling fuel rod components in the nuclear industry.
- Full Text:
- Date Issued: 2017
Hybrid additive manufacturing platform for the production of composite wind turbine blade moulds
- Authors: Momsen, Timothy Benjamin
- Date: 2017
- Subjects: Manufacturing processes -- Automation Production control -- Automation , Production management
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/19091 , vital:28772
- Description: This dissertation discusses the application of additive manufacturing technologies for production of a large-scale rapid prototyping machine, which will be used to produce moulds for prototype composite turbine blades for the emerging renewables energy industry within the Eastern Cape region in South Africa. The conceptualization and design of three complete printer builds resulted in the amalgamation of a final system, following stringent theoretical design, simulation, and feasibility analysis. Following the initial product design cycle stage, construction and performance testing of a large-scale additive manufacturing platform were performed. In-depth statistical analysis of the mechatronic system was undertaken, particularly related to print-head locational accuracy, repeatability, and effects of parameter variation on printer performance. The machine was analysed to assess feasibility for use in the mould-making industry with accuracy and repeatability metrics of 0.121 mm and 0.156 mm rivalling those produced by some of the more accurate fused deposition modellers commercially available. The research data gathered serves to confirm that rapid prototyping is a good alternative manufacturing method for wind turbine blade plug and mould production.
- Full Text:
- Date Issued: 2017
- Authors: Momsen, Timothy Benjamin
- Date: 2017
- Subjects: Manufacturing processes -- Automation Production control -- Automation , Production management
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/19091 , vital:28772
- Description: This dissertation discusses the application of additive manufacturing technologies for production of a large-scale rapid prototyping machine, which will be used to produce moulds for prototype composite turbine blades for the emerging renewables energy industry within the Eastern Cape region in South Africa. The conceptualization and design of three complete printer builds resulted in the amalgamation of a final system, following stringent theoretical design, simulation, and feasibility analysis. Following the initial product design cycle stage, construction and performance testing of a large-scale additive manufacturing platform were performed. In-depth statistical analysis of the mechatronic system was undertaken, particularly related to print-head locational accuracy, repeatability, and effects of parameter variation on printer performance. The machine was analysed to assess feasibility for use in the mould-making industry with accuracy and repeatability metrics of 0.121 mm and 0.156 mm rivalling those produced by some of the more accurate fused deposition modellers commercially available. The research data gathered serves to confirm that rapid prototyping is a good alternative manufacturing method for wind turbine blade plug and mould production.
- Full Text:
- Date Issued: 2017
Intelligence based error detection and classification for 3D measurement systems
- Van Rooyen, Ivän Jan-Richard
- Authors: Van Rooyen, Ivän Jan-Richard
- Date: 2017
- Subjects: Computer integrated manufacturing systems Manufacturing processes -- Automation , Computers, Special purpose Neural networks (Computer science)
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/21241 , vital:29461
- Description: For many years 2D machine vision has been used to perform automated inspection and measuring in the manufacturing environment. A strong drive to automate manufacturing has meant improvements in robotics and sensor technologies. So has machine vision seen a steady movement away from 2D and towards 3D. It is necessary to research and develop software that can use these new 3D sensing equipment in novel and useful ways. One task that is particularly useful, for a variety of situations is object recognition. It was hypothesised that it should be possible to train artificial neural networks to recognise 3D objects. For this purpose a 3D laser scanner was developed. This scanner and its software was developed and tested first in a virtual environment and what was learned there was then used to implemented an actual scanner. This scanner served the purpose of verifying what was done in the virtual environment. Neural networks of different sized were trained to establish whether they are a feasible classifier for the task of object recognition. Testing showed that, with the correct preprocessing, it is possible to perform 3D object recognition on simple geometric shapes by means of artificial neural networks.
- Full Text:
- Date Issued: 2017
- Authors: Van Rooyen, Ivän Jan-Richard
- Date: 2017
- Subjects: Computer integrated manufacturing systems Manufacturing processes -- Automation , Computers, Special purpose Neural networks (Computer science)
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/21241 , vital:29461
- Description: For many years 2D machine vision has been used to perform automated inspection and measuring in the manufacturing environment. A strong drive to automate manufacturing has meant improvements in robotics and sensor technologies. So has machine vision seen a steady movement away from 2D and towards 3D. It is necessary to research and develop software that can use these new 3D sensing equipment in novel and useful ways. One task that is particularly useful, for a variety of situations is object recognition. It was hypothesised that it should be possible to train artificial neural networks to recognise 3D objects. For this purpose a 3D laser scanner was developed. This scanner and its software was developed and tested first in a virtual environment and what was learned there was then used to implemented an actual scanner. This scanner served the purpose of verifying what was done in the virtual environment. Neural networks of different sized were trained to establish whether they are a feasible classifier for the task of object recognition. Testing showed that, with the correct preprocessing, it is possible to perform 3D object recognition on simple geometric shapes by means of artificial neural networks.
- Full Text:
- Date Issued: 2017
Modelling the effect of graphitization on the fracture toughness (JIC) of service exposed ASTM A-515 Gr. 65 material by the small punch test method
- Authors: Grewar, Stephen James
- Date: 2017
- Subjects: Graphitization Fracture mechanics
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/18849 , vital:28737
- Description: Small Punch Testing is a recent testing methodology with numerous favourable applications in engineering assessments. The advantages of this small specimen method are utilised to derive fracture toughness measurements on service exposed and graphitized steel designated ASTM A-515 Gr. 65. The EPRI-FAA “innovative method”, involving finite element analysis, is applied to obtain fracture toughness estimates and investigate the effect of localised graphitization on localised fracture toughness. The method is described in a stepwise manner and validated favourably against standard fracture toughness testing methods as well as the work of forerunners in this field. Analysis of twenty tested small punch disk specimens extracted from a service exposed welded pipe coupon showed that toughness decreases logarithmically with increased graphitization volumetric percentages in the small samples. Therefore graphitization is found to have a significant influence on local fracture toughness (JIC) of ASTM A-515 Gr. 65 steel under room temperature conditions. The possibility of documenting the effect of microstructural changes on other static properties such as yield strength and strain hardening exists provided that analysis of each disk specimen is performed prior to punch testing. A relationship between percentage graphitization and material toughness has been proposed for ASTM A-515 Gr. 65 at room temperature.
- Full Text:
- Date Issued: 2017
- Authors: Grewar, Stephen James
- Date: 2017
- Subjects: Graphitization Fracture mechanics
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/18849 , vital:28737
- Description: Small Punch Testing is a recent testing methodology with numerous favourable applications in engineering assessments. The advantages of this small specimen method are utilised to derive fracture toughness measurements on service exposed and graphitized steel designated ASTM A-515 Gr. 65. The EPRI-FAA “innovative method”, involving finite element analysis, is applied to obtain fracture toughness estimates and investigate the effect of localised graphitization on localised fracture toughness. The method is described in a stepwise manner and validated favourably against standard fracture toughness testing methods as well as the work of forerunners in this field. Analysis of twenty tested small punch disk specimens extracted from a service exposed welded pipe coupon showed that toughness decreases logarithmically with increased graphitization volumetric percentages in the small samples. Therefore graphitization is found to have a significant influence on local fracture toughness (JIC) of ASTM A-515 Gr. 65 steel under room temperature conditions. The possibility of documenting the effect of microstructural changes on other static properties such as yield strength and strain hardening exists provided that analysis of each disk specimen is performed prior to punch testing. A relationship between percentage graphitization and material toughness has been proposed for ASTM A-515 Gr. 65 at room temperature.
- Full Text:
- Date Issued: 2017
Neural network fault diagnosis system for a diesel-electric locomotive's closed loop excitation control system
- Authors: Barnard, Morne
- Date: 2017
- Subjects: Neural networks (Computer science) Diesel locomotives -- Motors -- Control systems
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/15955 , vital:28294
- Description: In closed loop control systems fault isolation is extremely difficult due to the fact that if feedbacks are corrupted or actuators can’t produce a desired output, a system reacts due to an increase in error between the measured variable and the set input variable, which can cause oscillations. The goal of this project is to develop a fault detection and isolation system for the isolation of faults, which cause oscillatory conditions on a GE Diesel-Electric Locomotive’s excitation control system. The proposed system will illustrate the use of artificial neural networks as a replacement to classical analytical models. The artificial neural network model’s design will be based on model-based dedicated observer theory to isolate sensor, as well as component faults, where observer theory will be utilised to effectively select input-output data configurations for detection of sensor and component faults causing oscillations. Owing to the nature of the locomotive’s data acquisition abilities, the model-based observer design will utilise historical data to design an effective model of the system which will be used to perform offline sampled fault detection. This method is proposed as an alternative to trend checking, data mining, etc. Faults are thus detected through the use of an offline model-based dedicated observer residual generator. With the use of a neural network a number of parameters affect the accuracy of the network where the primary source of ensuring an accurate model is training. The project highlights and experiments with these parameters to ensure an accurate model is trained with the use of the gradient descent training algorithm. The parameters which are considered are learning rate, hidden layer neurons, momentum and data preparation. The project will also provide a literature review on residual evaluation techniques used in practice and describe and evaluate the proposed method to perform residual evaluation for this specific application. The proposed method for residual evaluation was based on two principles, namely the moving average, as well as the simple thresholding techniques. The developed FDI system’s performance was measured against known faults and produced 100% accuracy for the detection and isolation of sensor and components causing oscillatory conditions on the locomotive’s excitation system.
- Full Text:
- Date Issued: 2017
- Authors: Barnard, Morne
- Date: 2017
- Subjects: Neural networks (Computer science) Diesel locomotives -- Motors -- Control systems
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/15955 , vital:28294
- Description: In closed loop control systems fault isolation is extremely difficult due to the fact that if feedbacks are corrupted or actuators can’t produce a desired output, a system reacts due to an increase in error between the measured variable and the set input variable, which can cause oscillations. The goal of this project is to develop a fault detection and isolation system for the isolation of faults, which cause oscillatory conditions on a GE Diesel-Electric Locomotive’s excitation control system. The proposed system will illustrate the use of artificial neural networks as a replacement to classical analytical models. The artificial neural network model’s design will be based on model-based dedicated observer theory to isolate sensor, as well as component faults, where observer theory will be utilised to effectively select input-output data configurations for detection of sensor and component faults causing oscillations. Owing to the nature of the locomotive’s data acquisition abilities, the model-based observer design will utilise historical data to design an effective model of the system which will be used to perform offline sampled fault detection. This method is proposed as an alternative to trend checking, data mining, etc. Faults are thus detected through the use of an offline model-based dedicated observer residual generator. With the use of a neural network a number of parameters affect the accuracy of the network where the primary source of ensuring an accurate model is training. The project highlights and experiments with these parameters to ensure an accurate model is trained with the use of the gradient descent training algorithm. The parameters which are considered are learning rate, hidden layer neurons, momentum and data preparation. The project will also provide a literature review on residual evaluation techniques used in practice and describe and evaluate the proposed method to perform residual evaluation for this specific application. The proposed method for residual evaluation was based on two principles, namely the moving average, as well as the simple thresholding techniques. The developed FDI system’s performance was measured against known faults and produced 100% accuracy for the detection and isolation of sensor and components causing oscillatory conditions on the locomotive’s excitation system.
- Full Text:
- Date Issued: 2017
Strain behaviour of an eco-car wheel rim designed through topology and composite layup optimization
- Authors: Badenhorst, Martin Wessel
- Date: 2017
- Subjects: Wheels -- Design and construction Mechanical engineering
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/16066 , vital:28317
- Description: This research aimed to reduce the mass of a purpose built eco-car wheel through the sequential use of structural topology and composites optimization software packages while investigating the changes in mass and strain behaviour resulting from altering component geometry, lamina shape, and stacking sequence. The strain behaviour of a commercially available wheel constructed using pre-tensioned steel spokes was established through the comparison of measured physical and FEA strains resulting from applied pressure, radial, lateral, torsional, and combined loads. Structural topology optimization software was then utilized to produce 48 different wheel geometries corresponding to a combined loading scenario consisting of pressure, radial, and lateral loads. The variables controlled during this process included the objective optimization function, safety factor, target design volume, split-draw constraint, and degrees of cyclic symmetry. The optimum geometry was determined by means of evaluating specific stiffness and potential towards being manufactured as a composite component. Three composite wheel FEA base models, with uni-directional laminae stacked at different fibre orientation intervals, were created according to this geometry and lightened by means of composite free size optimization. Composite sizing and shuffling optimizations were then utilized to further enhance the mass and strain characteristics of the lightest of these three solutions Two composite wheels were manufactured according to the wheel geometry, lamina shapes, and stacking sequences determined by means of structural topology and composites ptimizations. The physical mass and strain behaviour of these wheels were measured and compared to those corresponding to the optimized FEA model, as well as the commercially available wheel. This comparison showed that structural topology and composites optimization software packages can be sequentially utilized to produce an adequately stiff composite wheel of lower mass than a commercially available wheel constructed using pre-tensioned steel spokes.
- Full Text:
- Date Issued: 2017
- Authors: Badenhorst, Martin Wessel
- Date: 2017
- Subjects: Wheels -- Design and construction Mechanical engineering
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/16066 , vital:28317
- Description: This research aimed to reduce the mass of a purpose built eco-car wheel through the sequential use of structural topology and composites optimization software packages while investigating the changes in mass and strain behaviour resulting from altering component geometry, lamina shape, and stacking sequence. The strain behaviour of a commercially available wheel constructed using pre-tensioned steel spokes was established through the comparison of measured physical and FEA strains resulting from applied pressure, radial, lateral, torsional, and combined loads. Structural topology optimization software was then utilized to produce 48 different wheel geometries corresponding to a combined loading scenario consisting of pressure, radial, and lateral loads. The variables controlled during this process included the objective optimization function, safety factor, target design volume, split-draw constraint, and degrees of cyclic symmetry. The optimum geometry was determined by means of evaluating specific stiffness and potential towards being manufactured as a composite component. Three composite wheel FEA base models, with uni-directional laminae stacked at different fibre orientation intervals, were created according to this geometry and lightened by means of composite free size optimization. Composite sizing and shuffling optimizations were then utilized to further enhance the mass and strain characteristics of the lightest of these three solutions Two composite wheels were manufactured according to the wheel geometry, lamina shapes, and stacking sequences determined by means of structural topology and composites ptimizations. The physical mass and strain behaviour of these wheels were measured and compared to those corresponding to the optimized FEA model, as well as the commercially available wheel. This comparison showed that structural topology and composites optimization software packages can be sequentially utilized to produce an adequately stiff composite wheel of lower mass than a commercially available wheel constructed using pre-tensioned steel spokes.
- Full Text:
- Date Issued: 2017
Tool wear monitoring in machining of stainless steel
- Authors: Odedeyi Peter Babatunde
- Date: 2017
- Subjects: Mechanical wear Machine-tools -- Monitoring
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/15900 , vital:28288
- Description: monitoring systems for automated machines must be capable of operating on-line and interpret the working condition of machining process at a given point in time because it is an automated and unmanned system. But this has posed a challenge that lead to this research study. Generally, optimization of machining process can be categorized as minimization of tool wear, minimization of operating cost, maximization of process output and optimization of machine parameter. Tool wear is a complex phenomenon, capable of reducing surface quality, increases power consumption and increased reflection rate of machined parts. Tool wear has a direct effect on the quality of the surface finish for any given work-piece, dimensional precision and ultimately the cost of parts produced. Tool wear usually occur in combination with the principal wear mode which depends on cutting conditions, tool insert geometry, work piece and tool material. Therefore, there is a need to develop a continuous tool monitoring systems that would notify operator the state of tool to avoid tool failure or undesirable circumstances. Tool wear monitoring system for macro-milling has been studied using design and analysis of experiment (DOE) approach. Regression analysis, analysis of variance (ANOVA), Box Behnken and Response Surface Methodology (RSM). These analysis tools were used to model the tool wear. Hence, further investigations were carried out on the data acquired using signal processing and Neural networks frame work to validate the model. The effects of cutting parameters are evaluated and the optimal cutting conditions are determined. The interaction of cutting parameters is established to illustrate the intrinsic relationship between cutting parameters, tool wear and material removal rate. It was observed that when working with stainless steel 316, a maximum tool wear value of 0.29mm was achieved through optimization at low values of feed about 0.06mm/rev, speed of 4050mm/min and depth of cut about 2mm.
- Full Text:
- Date Issued: 2017
- Authors: Odedeyi Peter Babatunde
- Date: 2017
- Subjects: Mechanical wear Machine-tools -- Monitoring
- Language: English
- Type: Thesis , Masters , MEng
- Identifier: http://hdl.handle.net/10948/15900 , vital:28288
- Description: monitoring systems for automated machines must be capable of operating on-line and interpret the working condition of machining process at a given point in time because it is an automated and unmanned system. But this has posed a challenge that lead to this research study. Generally, optimization of machining process can be categorized as minimization of tool wear, minimization of operating cost, maximization of process output and optimization of machine parameter. Tool wear is a complex phenomenon, capable of reducing surface quality, increases power consumption and increased reflection rate of machined parts. Tool wear has a direct effect on the quality of the surface finish for any given work-piece, dimensional precision and ultimately the cost of parts produced. Tool wear usually occur in combination with the principal wear mode which depends on cutting conditions, tool insert geometry, work piece and tool material. Therefore, there is a need to develop a continuous tool monitoring systems that would notify operator the state of tool to avoid tool failure or undesirable circumstances. Tool wear monitoring system for macro-milling has been studied using design and analysis of experiment (DOE) approach. Regression analysis, analysis of variance (ANOVA), Box Behnken and Response Surface Methodology (RSM). These analysis tools were used to model the tool wear. Hence, further investigations were carried out on the data acquired using signal processing and Neural networks frame work to validate the model. The effects of cutting parameters are evaluated and the optimal cutting conditions are determined. The interaction of cutting parameters is established to illustrate the intrinsic relationship between cutting parameters, tool wear and material removal rate. It was observed that when working with stainless steel 316, a maximum tool wear value of 0.29mm was achieved through optimization at low values of feed about 0.06mm/rev, speed of 4050mm/min and depth of cut about 2mm.
- Full Text:
- Date Issued: 2017
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