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Rapid prototyping (RP) is a supplementary additive manufacturing method to the traditional Computer Numerical Controlled (CNC) machining. The selection of the manufacturing method between RP and CNC machining is currently based on qualitative analysis and engineers’ experience. There are situations when parts can be produced using either of the methods. In such cases, cost will be the decisive factor. However, lack of a quantitative cost estimation method to guide the selection between RP and CNC machining makes the decision process difficult. This thesis proposes an automated cost estimator for CNC machining and Rapid Prototyping. Vertical CNC milling and Stereolithography Apparatus (SLA) RP technology are selected in specific, for cost modeling and process comparison. A binary questionnaire is designed to help estimate the CNC setup cost. An SLA build time estimator is implemented based on 3D systems’ SLA3500 machine. SLA post processing cost is also investigated. Based on the developed methods, a prototype software tool was created with an output to Excel chart to facilitate the selection. Five cases have been studied with the software and the predicted results are found reasonable and effective.

This thesis is concerned with the determination of cutting parameters (cutting speed, feed rate and depth of cut) in turning operations within an industrial environment. The parameters are required for the purposes of tool selection, working with a variety of batches of different materials. Previous work of this nature, little of which has been transferred into industry, has concentrated primarily on deriving optimum cutting conditions, based on a variety of deterministic and non- deterministic approaches, with a general reliance on experimentally-derived input variables. However, this work is only suited to tool selection for a single material. Under industrial conditions tools will frequently need to be selected for more than one material, in tool/material combinations not recommended by tool makers. Consequently, the objective of the research described in this thesis was to employ existing cutting data technology and to use it as the basis for a cutting data system, suitable for multi-batch tool selection. Two companies collaborated in this work, by making available suitable personnel and the provision of shop floor facilities on their premises. The initial work concentrated on the development of an algorithmic model, based on established theory. This was then tested industrially, using the concept of shop floor approved data as a substitute for optimum cutting data. The model was found to work reasonably, but required further development to make it suitable for multi- batch tool selection. This development took three main forms: a) a reduction of input data, particularly in the number of experimentally-derived variables, b) the removal of the tool/material-specific constraints traditionally used in cutting data optimisation, c) a method of data correction based on adjustment of the mean and standard deviation of the data. Further industrial testing was carried out using the resulting system. It was demonstrated that it was possible for a relaxed system with reduced input variables and appropriate data correction to function within an industrial environment.

The need is expressed for mathematical models which describe the cutting tool-workpiece interaction and for accurate on-line monitoring of tool-state. These are essential requirements for the achievement of unmanned and computerized machining processes. Techniques are used to design the experiments which substantially reduce the number of tests while providing all the essential information for statistical analysis and for the development of mathematical models. The testing conditions are chosen to reasonably conform with the practical requirements. Multi-coated carbide tool inserts [Sandvik GC435] are used to cut an alloy steel [EN 19] under normal cutting conditions and for a wide range of operating parameters. An accurate and sensitive three-component dynamometer was designed, manufactured and used to measure the tool forces through a BBC microcomputer. Continuous records of the tool vibration have been collected in two different co-ordinate directions simultaneously together with measurements of tool wear and cutting forces. Linear and non-linear regression techniques are used to develop mathematical models for the experimentally measured responses: cutting forces, tool vibration, and tool wear. Special attention is devoted to the identification of the most appropriate models. Each model being capable of representing the tool state throughout its working lifetime. Tool life wear-based models are developed to relate the expected tool lifetime to the operating parameters: speed, feed, and depth of cut. A robust regression analysis technique, used in conjunction with iteratively re-weighting least-squares, has been found to improve the accuracy of the models, and to stabilize its computed residuals through the elimination of the effect of influential observations having high experimental error. Response surface methodology RSM has been used to signify the non-linear nature of tool life response. The force variation has been shown to correlate strongly with the wear progress so that it can be used for accurate in-process determination of tool wear and for monitoring tool state. It has been shown that the variation in the ratio between force components correlates with wear and is independent of the effect of other machining parameters; this enables the approach to be used for a wider range of materials and more extensive operational domain. Study of the power spectral analysis of the tool vibration indicates that among the tool's vibration modes, the first fundamental natural frequency of vibration in the feed direction exhibits a consistent correlation with wear-progress. The Vibration amplitude decreases with the increase of the wear level until it reaches a limit after which it tends to reverse its characteristic. The time at which the characteristic changes is found to closely correspond with the practical end of the tool lifetime. Based on this fact, an in-process approach is investigated to determine the tool life on-line. Also, a model has been developed for tool wear estimation based on a combination of vibration and force; and, very good agreement has been obtained with the experimental data. The validity of the models; their feasibility; and, their industrial significance are confirmed for adaptive control AC systems, and for machinability data base systems MDBS.

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Thesis (Ph.D.) -- McMaster University, 1999. / Includes bibliographical references (leaves 168-179). Also available via World Wide Web.