A predictive model of milling surface roughness

Zheng

Zhou

2022

Sensors

Monitoring surface quality during machining has considerable practical significance for the performance of high-value products, particularly for their assembly interfaces. Surface roughness is the most important metric of surface quality. Currently, the research on online surface roughness prediction has several limitations. The effect of tool wear variation on surface roughness is seldom considered in machining. In addition, the deterioration trend of surface roughness and tool wear differs under variable cutting parameters. The prediction models trained under one set of cutting parameters fail when cutting parameters change. Accordingly, to timely monitor the surface quality of assembly interfaces of high-value products, this paper proposes a surface roughness prediction method that considers the tool wear variation under variable cutting parameters. In this method, a stacked autoencoder and long short-term memory network (SAE–LSTM) is designed as the fundamental surface roughness prediction model using tool wear conditions and sensor signals as inputs. The transfer learning strategy is applied to the SAE–LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (using Ti6Al4V as material) of an aircraft’s vertical tail are conducted, and monitoring data are used to validate the proposed method. Ablation studies are implemented to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and is capable of tracking the true surface roughness with time. Specifically, the minimum values of the root mean square error and mean absolute percentage error of the prediction results after transfer learning are 0.027 μm and 1.56%, respectively.

Section: Polynomial Regression Modelmentioning

confidence: 99%

Online Surface Roughness Prediction for Assembly Interfaces of Vertical Tail Integrating Tool Wear under Variable Cutting Parameters

Zheng

Zhou

2022

Sensors

“…and cutting force for Ti6A14V. The model has high applicability and accuracy for the prediction of milling surface roughness [18]. However, it only considers the combination of several groups of discrete cutting parameters and does not take into account the prediction of surface roughness at different times in machining.…”

Section: Wang Et Al Established a Polynomial Regression Model For Surface Roughness Prediction Considering The Factors Of Tool Geometrymentioning

confidence: 99%

Transfer Learning Based Surface Roughness Prediction Integrating Tool Wear Under Variable Cutting Parameters

Zheng

et al. 2021

Preprint

The monitoring of surface quality in machining is of great practical significance for the reliability and life of high-value products such as rocket, spacecraft and aircraft, particularly for their assembly interfaces of these products. Surface roughness is an important metric to evaluate the surface quality. The current research of online surface roughness prediction has the following limitations. The effect of the varying tool wear on the surface roughness is rarely considered in machining. In addition, the deteriorating trend of surface roughness and tool wear is different under variable cutting parameters. Prediction models trained under one set of cutting parameters fail when cutting parameters change. This paper proposes a surface roughness prediction method considering the varying tool wear under variable cutting parameters. A stacked autoencoder and long short-term memory network (SAE-LSTM) is designed as the basic surface roughness prediction model that uses tool wear conditions and sensor signals as the input. The transfer learning strategy is applied on SAE-LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (Ti6Al4V material) of the aircraft’s vertical tail are conducted and the monitoring data are used to validate the proposed method. Ablations studies are carried out to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and well track the true surface roughness over time.

“…Because belt grinding is a “cold state” processing method, it has high economic efficiency and fast processing efficiency and is widely used in various manufacturing fields. Since the grinding process involves multi-edge cutting, the material removal rate is low, and the cutting head is small, so the roughness after grinding is small [ 3 , 4 , 5 , 6 , 7 , 8 ]. Huang et al [ 9 ] analyzed the vibration mechanism from the perspective of dynamics and established a vibration model of impeller belt grinding, which was verified experimentally with different parameters and was found to be effective in reducing the vibration phenomenon during the grinding process with better parameters achieved in the experiments.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness of Abrasive Belt Grinding of Superalloy Material Based on RLSOM-RBF

et al. 2021

It is difficult to accurately predict the surface roughness of belt grinding with superalloy materials due to the uneven material distribution and complex material processing. In this paper, a radial basis neural network is proposed to predict surface roughness. Firstly, the grinding system of the superalloy belt is introduced. The effects of the material removal process and grinding parameters on the surface roughness in belt grinding were analyzed. Secondly, an RBF neural network is trained by reinforcement learning of a self-organizing mapping method. Finally, the prediction accuracy and simulation results of the proposed method and the traditional prediction method are analyzed using the ten-fold cross method. The results show that the relative error of the improved RLSOM-RBF neural network prediction model is 1.72%, and the R-value of the RLSOM-RBF fitting result is 0.996.