Intelligent G-code-based power prediction of ultra-precision CNC machine tools through 1DCNN-LSTM-Attention model

Abstract

As the most promising and advanced technology, ultra-precision machining (UPM) has dramatically increased its production volume for wide-range applications in various high-tech fields such as chips, optics, microcircuits, biotechnology, etc. The concomitantly negative environmental impact resulting from huge-volume UPM has attracted unprecedented attention from both academia and industry. Accurate energy prediction of ultra-precision machine tools (UPMTs) can provide significant insight into energy planning, machining strategy, and energy conservation. Data-driven models for predicting energy have become increasingly popular due to their high accuracy and low modeling difficulty. However, existing data-driven models only focus on ordinary precision machine tools, and their applications on UPMTs are hardly studied. To fill the gap, this paper proposed a data-driven model constructed with 1DCNN-LSTM-Attention layers for predicting the instantaneous power profile of a five-axes UPMT. In the data-preparation phase, an advanced G-code interpreter was developed to generate the working status dataset from the G-code command and accurately match them with the power data collected. Random hyperparameters searching method was adopted to tune the 1DCNN-LSTM-Attention structure for better accuracy in the model creation phase. Finally, the sensitivity of these hyperparameters on the model performance was analyzed. Results demonstrate that the learning rate, 1DCNN, LSTM and dense layer numbers are identified as critical parameters affecting the model performance. The optimized 1DCNN-LSTM-Attention model outperforms other models, achieving an R² value of 0.93. This work first validate the feasibility of utilizing advanced machine learning techniques for predicting energy consumption in UPM field, which can further promoting energy-efficient and sustainable UPM practices by digitalizing the energy consumption process.