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Generally, off-line methods are used for surface roughness prediction of titanium alloy milling. However, studies show that these methods have poor prediction accuracy. In order to resolve this shortcoming, a prediction method based on Cloudera's Distribution Including Apache Hadoop (CDH) platform is proposed in the present study. In this regard, data analysis and process platform is designed based on the CDH, which can upload, calculate and store data in real-time. Then this platform is combined with the Harris hawk optimization (HHO) algorithm and pattern search strategy, and an improved hybrid optimization (IHHO) method is proposed accordingly. Then this method is applied to optimize the SVM algorithm and predict the surface roughness in the CDH platform. The obtained results show that the prediction accuracy of IHHO method reaches 95%, which is higher than the conventional methods of SVM, BAT-SVM, GWO-SVM and WOA-SVM.

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During the machining process, the tool wear state is closely related to the quality of the workpiece, which will directly affect the performance of the equipment. Not timely replacement of tools will lead to increased processing costs, low workpiece surface quality, and even damage to processing equipment. Therefore, research on tool wear monitoring is necessary for the tool processing industry. By analyzing the relationship between tool wear and sensor signals to determine the required acquisition signal. Aiming at the problem that the original sensor data cannot be directly used in the machining process, the signal processing technology is used to preprocess the original signal, remove the invalid signal collected during the cutting process, and use the filtering method to eliminate the singular points in the original signal. The time domain and frequency domain features of the data are extracted. Firstly, the features are optimized by the extreme random tree(ET), and the tool wear is taken as the target vector. The Pearson correlation coefficient(PCC) between the target vector and the filtered features is calculated, and the features with solid correlation with the target vector are selected. The results show that the relevance vector machine(RVM) model proposed in the research can effectively monitor tool wear.