Thermally induced errors have a major significance on the positional accuracy of a machine tool. Heat generated during the machining process produces thermal gradients that flow through the machine structure causing linear and nonlinear thermal expansions and distortions of associated complex discrete structures, producing deformations that adversely affect structural stability. The heat passes through structural linkages and mechanical joints where interfacial parameters such as the roughness and form of the contacting surfaces affect the thermal resistance and thus the heat transfer coefficients. This paper presents a novel offline technique using finite element analysis (FEA) to simulate the effects of the major internal heat sources such as bearings, motors and belt drives of a small vertical milling machine (VMC) and the effects of ambient temperature pockets that build up during the machine operation. Simplified models of the machine have been created offline using FEA software and evaluated experimental results applied for offline thermal behaviour simulation of the full machine structure. The FEA simulated results are in close agreement with the experimental results ranging from 65% to 90% for a variety of testing regimes and revealed a maximum error range of 70 µm reduced to less than 10 µm.
Temperature measurement is one of the most important aspects of manufacturing. There have been many temperature measuring techniques applied for obtaining workpiece temperature in different types of manufacturing processes. The main limitations of conventional sensors have been the inability to indicate the core temperature of workpieces and the low accuracy that may result due to the harsh nature of some manufacturing environments. The speed of sound is dependent on the temperature of the material through which it passes. This relationship can be used to obtain the temperature of the material provided that the speed of sound can be reliably obtained. This paper investigates the feasibility of creating a cost-effective solution suitable for precision applications that require the ability to resolve a better than 0.5 °C change in temperature with ±1 °C accuracy. To achieve these, simulations were performed in MATLAB using the k-wave toolbox to determine the most effective method. Based upon the simulation results, experiments were conducted using ultrasonic phase-shift method on a steel sample (type EN24T). The results show that the method gives reliable and repeatable readings. Based on the results from this paper, the same setup will be used in future work in the machining environment to determine the effect of the harsh environment on the phase-shift ultrasonic thermometry, in order to create a novel technique for in-process temperature measurement in subtractive manufacturing processes.
This paper investigates the use of the discrete wavelet transform (DWT) and Fast Fourier Transform (FFT) to improve the quality of extracted features for machine learning. The case study in this paper is detecting the health state of the ballscrew of a gantry type machine tool. For the implementation of the algorithm for feature extraction, wavelet is first applied to the data, followed by FFT and then useful features are extracted from the resultant signal. The extracted features were then used in various machine learning algorithms like decision tree, K-nearest neighbour (KNN) and support vector machine (SVM) for binary classification of the ballscrew state. The result shows significant improvement in the classification accuracy after the wavelet transform and FFT has been performed on the data.
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