Cheng Minghui, Jiao Li, Yan Pei, Gu Huiqing, Sun Jie, Qiu Tianyang, Wang Xibin
School of Mechanical Engineering, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Haidian District, Beijing 100081, China.
Key Laboratory of Fundamental Science for Advanced Machining, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Haidian District, Beijing 100081, China.
Sensors (Basel). 2022 Jun 30;22(13):4943. doi: 10.3390/s22134943.
For data-driven intelligent manufacturing, many important in-process parameters should be estimated simultaneously to control the machining precision of the parts. However, as two of the most important in-process parameters, there is a lack of multi-task learning () model for simultaneous estimation of surface roughness and tool wear. To address the problem, a new model with shared layers and two task-specific layers was proposed. A novel parallel-stacked auto-encoder (PSAE) network based on stacked denoising auto-encoder (SDAE) and stacked contractive auto-encoder (SCAE) was designed as the shared layers to learn deep features from cutting force signals. To enhance the performance of the model, the scaled exponential linear unit (SELU) was introduced as the activation function of SDAE. Moreover, a dynamic weight averaging (DWA) strategy was implemented to dynamically adjust the learning rate of different tasks. Then, the time-domain features were extracted from raw cutting signals and low-frequency reconstructed wavelet packet coefficients. Frequency-domain features were extracted from the power spectrum obtained by the Fourier transform. After that, all features were combined as the input vectors of the proposed model. Finally, surface roughness and tool wear were simultaneously predicted by the trained model. To verify the superiority and effectiveness of the proposed model, nickel-based superalloy Haynes 230 was machined under different cutting parameter combinations and tool wear levels. Some other intelligent algorithms were also implemented to predict surface roughness and tool wear. The results showed that compared with the support vector regression (SVR), kernel extreme learning machine (KELM), with SDAE (MTL_SDAE), with SCAE (MTL_SCAE), and single-task learning with PSAE (STL_PSAE), the estimation accuracy of surface roughness was improved by 30.82%, 16.67%, 14.06%, 26.17%, and 16.67%, respectively. Meanwhile, the prediction accuracy of tool wear was improved by 46.74%, 39.57%, 41.51%, 38.68%, and 39.57%, respectively. For practical engineering application, the dimensional deviation and surface quality of the machined parts can be controlled through the established model.
对于数据驱动的智能制造，需要同时估计许多重要的加工过程参数，以控制零件的加工精度。然而，作为两个最重要的加工过程参数，目前缺乏用于同时估计表面粗糙度和刀具磨损的多任务学习（）模型。为了解决这个问题，提出了一种具有共享层和两个特定任务层的新模型。基于堆叠去噪自动编码器（SDAE）和堆叠收缩自动编码器（SCAE）设计了一种新颖的并行堆叠自动编码器（PSAE）网络作为共享层，以从切削力信号中学习深度特征。为了提高模型的性能，引入了缩放指数线性单元（SELU）作为SDAE的激活函数。此外，实施了动态权重平均（DWA）策略来动态调整不同任务的学习率。然后，从原始切削信号和低频重构小波包系数中提取时域特征。从通过傅里叶变换获得的功率谱中提取频域特征。之后，将所有特征组合作为所提出模型的输入向量。最后，通过训练好的模型同时预测表面粗糙度和刀具磨损。为了验证所提出模型的优越性和有效性，在不同的切削参数组合和刀具磨损水平下对镍基高温合金Haynes 230进行加工。还实施了一些其他智能算法来预测表面粗糙度和刀具磨损。结果表明，与支持向量回归（SVR）、核极限学习机（KELM）、基于SDAE的（MTL_SDAE）、基于SCAE的（MTL_SCAE）以及基于PSAE的单任务学习（STL_PSAE）相比，表面粗糙度的估计精度分别提高了30.82%、16.67%、14.06%、26.17%和16.67%。同时，刀具磨损的预测精度分别提高了46.74%、39.57%、41.51%、38.68%和39.57%。对于实际工程应用，可以通过建立的模型来控制加工零件的尺寸偏差和表面质量。
