Thesis (M. Eng. Sc.)--University of Queensland, 2001. / Includes bibliographical references.

Gun-drilling is one of the highly efficient tools for deep-hole making. The drilled hole possesses good size, positioning, roughness, roundness, and straightness. When the deep-hole is drilled, it is easy to cause wear at the tool-chip and tool-work interfaces at the serious condition so that the gun-drill is subjected to larger feed force. If the feed force exceeds the critical load, then the cutting edge of the gun-drill is broken. However, nowadays the device to measure the feed force in the gun-drill press is not available. This study develops a gun-drill press which possesses two new functions as follow: (1) to measure and to record the feed force during the drilling process;¡]2¡^to return the gun-drill when the feed force exceeds a set value. These two functions can be used to detect the serious breakage of the gun-drill. According to the practice drilling test, the performance of this press can be obtained. Results show that the feed force can be measured using different feed speeds. The gun-drill can stop and return to its origin position when the feed force exceeds the set value. Hence, the performance test of this new machine meets the above-mentioned functions. Using this press, the relationship between the feed force and the wear of cutting edge is investigated at the same feed speed. Results show that wear occurs on the cutting edge of the gun-drill, and the wear area increases with increasing drilling time so that the feed force is also increased. The wear grows from the outer side of the cutting edge to the inner side.

The rising demand for exacting performances from manufacturing systems has led to new challenges for the development of complex tool condition monitoring techniques. Although a wide range of monitoring methods have been investigated and developed, there has been very little migration of these innovations into industrial practice. The principal factor behind this phenomenon is the stochastic nature of the environment in which the system must function. A truly universal application has yet to be developed. The work presented here centres around the application of an unsupervised neural network model to the said problem. These networks learn without the aid of a human teacher or supervisor and learn to organise and re-organise themselves in accordance to the input data. This leads to the network structure reflecting the given input distribution more precisely than a predefined model, which generally follows a decay schedule. The dynamic nature of the process provides an evaluation of the underlying connectivity and topology in the original data space. This makes the network far more capable of capturing details in the target space. These networks have been successfully used in speech recognition applications and various pattern recognition tasks involving very noisy signals. Work is in progress on their application to robotics, process control and telecommunications. The procedure followed here has been to conduct experimental drilling trials using solid carbide drills on a Duplex Stainless Steel workpiece. Duplex Stainless Steel was chosen as a preferred metal for drilling experiments because of this high strength, good resistance to corrosion, low thermal expansion and good fatigue resistance. During the drilling trials, forces on the workpiece along the x, y and z axes were captured in real time and moments of the forces were calculated using these values. These three axial forces, along with their power spectral densities and moments were used as input parameters to the Artificial Neural Network model which followed the Self-Organising Map algorithm to classify this data. After the network was able to adapt itself to classify this real world data, the generated model was tested against a different set of data values captured during the drilling trials. The network was able to correctly identify a worn out drill from a new drill from this previously unseen set of data. This autonomous classification of the drill wear state by the neural network is a step towards creating a “universal” application that will eventually be able to predict tool wear in any machining operation without prior training.

The rising demand for exacting performances from manufacturing systems has led to new challenges for the development of complex tool condition monitoring techniques. Although a wide range of monitoring methods have been investigated and developed, there has been very little migration of these innovations into industrial practice. The principal factor behind this phenomenon is the stochastic nature of the environment in which the system must function. A truly universal application has yet to be developed. The work presented here centres around the application of an unsupervised neural network model to the said problem. These networks learn without the aid of a human teacher or supervisor and learn to organise and re-organise themselves in accordance to the input data. This leads to the network structure reflecting the given input distribution more precisely than a predefined model, which generally follows a decay schedule. The dynamic nature of the process provides an evaluation of the underlying connectivity and topology in the original data space. This makes the network far more capable of capturing details in the target space. These networks have been successfully used in speech recognition applications and various pattern recognition tasks involving very noisy signals. Work is in progress on their application to robotics, process control and telecommunications. The procedure followed here has been to conduct experimental drilling trials using solid carbide drills on a Duplex Stainless Steel workpiece. Duplex Stainless Steel was chosen as a preferred metal for drilling experiments because of this high strength, good resistance to corrosion, low thermal expansion and good fatigue resistance. During the drilling trials, forces on the workpiece along the x, y and z axes were captured in real time and moments of the forces were calculated using these values. These three axial forces, along with their power spectral densities and moments were used as input parameters to the Artificial Neural Network model which followed the Self-Organising Map algorithm to classify this data. After the network was able to adapt itself to classify this real world data, the generated model was tested against a different set of data values captured during the drilling trials. The network was able to correctly identify a worn out drill from a new drill from this previously unseen set of data. This autonomous classification of the drill wear state by the neural network is a step towards creating a “universal” application that will eventually be able to predict tool wear in any machining operation without prior training.

The rising demand for exacting performances from manufacturing systems has led to new challenges for the development of complex tool condition monitoring techniques. Although a wide range of monitoring methods have been investigated and developed, there has been very little migration of these innovations into industrial practice. The principal factor behind this phenomenon is the stochastic nature of the environment in which the system must function. A truly universal application has yet to be developed. The work presented here centres around the application of an unsupervised neural network model to the said problem. These networks learn without the aid of a human teacher or supervisor and learn to organise and re-organise themselves in accordance to the input data. This leads to the network structure reflecting the given input distribution more precisely than a predefined model, which generally follows a decay schedule. The dynamic nature of the process provides an evaluation of the underlying connectivity and topology in the original data space. This makes the network far more capable of capturing details in the target space. These networks have been successfully used in speech recognition applications and various pattern recognition tasks involving very noisy signals. Work is in progress on their application to robotics, process control and telecommunications. The procedure followed here has been to conduct experimental drilling trials using solid carbide drills on a Duplex Stainless Steel workpiece. Duplex Stainless Steel was chosen as a preferred metal for drilling experiments because of this high strength, good resistance to corrosion, low thermal expansion and good fatigue resistance. During the drilling trials, forces on the workpiece along the x, y and z axes were captured in real time and moments of the forces were calculated using these values. These three axial forces, along with their power spectral densities and moments were used as input parameters to the Artificial Neural Network model which followed the Self-Organising Map algorithm to classify this data. After the network was able to adapt itself to classify this real world data, the generated model was tested against a different set of data values captured during the drilling trials. The network was able to correctly identify a worn out drill from a new drill from this previously unseen set of data. This autonomous classification of the drill wear state by the neural network is a step towards creating a “universal” application that will eventually be able to predict tool wear in any machining operation without prior training.

Thesis (Professional Degree)--University of Missouri, School of Mines and Metallurgy, 1922. / The entire thesis text is included in file. Typescript. Illustrated by author. Title from title screen of thesis/dissertation PDF file (viewed June 10, 2009) Includes bibliographical references (p. 9).

Thesis (Ph. D.) - University of North Carolina, 1961. / Bibliography: leaves 41-46.

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Hearing loss is a common impairment or disability for human beings, and is impacting an increasing amount of people, augmented by the growing aging population around the globe. Cochlear implantation, as one of the most effective ways to restore hearing, can only applied to profoundly deaf patients at the moment. In order to expand the group of people who can benefit from cochlear implantation to those with less severe hearing loss, endeavours need to be made to best preserve residual hearing and minimise trauma induced during cochlear implantation surgery. In this thesis, the disturbance induced in the cochlea, i.e. the acoustic and mechanical energy transmitted into the cochlea, during cochleostomy drilling is studied - as well as establishing a comparison between a manually guided conventional technique and a manually supported tissue guided robotic drilling technique. The results show that by changing surgical techniques and how they are applied can have a significant impact on levels of disturbance induced - robotic-aided approach induced lower level of equivalent SPL for up to 86% of the time and can be as much as 39 dB lower than that generated by conventional surgical drilling. This work is timely because trauma is an important consideration to clinicians and health care providers. Cochleostomy is one of the major and most disruptive surgical process during cochlear implantation. With the increasing amount of cochlear implant electrode array designs that are shorter and less intrusive, and the increasing demand of electric-acoustic stimulation via cochlear implant to better resemble the human auditory system, the approach to reduce disruption during cochleostomy drilling is highly relevant to the progression in the hearing care industry and the benefits of the growing hearing impairment community.

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