This work investigates the roundness of holes by application of Taguchi methods to BTA deep-hole drilling with reference to process factors. Taguchi methods and statistical techniques are used in experimental layout, in the analysis of each control factor, and in prediction of the optimum setting for control factors. A set of confirmatory tests is then conducted to verify the estimated response. Influences of the control factors are discussed, and the levels of factors that produce holes of desired roundness with sufficient robustness are presented.

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The original purpose of this work was to propose an equation system to describe the kind of drilling in which the tools are with pronounced shaft length. The equation system turned out to disclose the low frequency mechanism of hole lobes. The proposed system is composed of Euler-Bernoulli beam equation representing tool shaft, and an excitation force in form of Fourier series. An empirical cutting force was established and related cutting force components were calculated. And the accumulated contribution of modes and harmonics was evaluated. The solution for the proposed equation system allowed a prediction of hole roundness which was consistent with experimental results for different conditions. The solution also allowed an investigation into the waviness and lobes. The results suggested that the bizarre phenomenon of lobes on hole is in reality waviness described by Tlusty for milling. The rigorous mathematical nature of the proposed equation built solid foundation for former empirical observation of waviness and lobes on work pieces.

When using the Taguchi method, an L18 or L27 orthogonal array is usually adopted. However, this requires many experiments (18 or 27 runs, respectively), consuming time, and resources. This study proposes a progressive Taguchi neural network model, which combines the Taguchi method with the artificial neural network to construct a prediction model for a CO 2 laser cutting experiment. During CO 2 laser cutting, energy from the moving laser is accumulative. The paper develops an integral equation of energy density during laser beam movement and lets it determine the sliding level of control factor. Meanwhile, the paper proposes that in Stage 1, only less number of experiments is required to be conducted by L9 orthogonal array. After the crucial supplementary experimental training samples proposed in Stage 2 are also included, high-accuracy prediction of artificial neural network can be completed. Based on analysis from the progressive Taguchi neural network, the Stage 1 preliminary network-with only a few available experimental examples-has achieved good predictive ability from regions near the Taguchi control points. For regions further out, the predictions have been increasingly unreliable. Nevertheless, the high precision of Stage 2 Taguchi network has good predictive results for all regions.

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