Surface roughness prediction method of titanium alloy milling based on CDH platform

Liu, Xianli; Sun, Yanming; Yue, Caixu; Wei, Xudong; Qingzhen, Sun; Liang, Steven Y.; Wang, Lihui; Qin, Yiyuan

doi:10.1007/s00170-021-08554-6

Cited by 7 publications

(3 citation statements)

References 8 publications

(7 reference statements)

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“…However, it requires a lot of effort to realize the evolution of surface quality through theoretical modeling or automatic monitoring 1 – 4 . Surface roughness is a significant side of the entirely quality of machined parts, which rest with cutting parameters, tool condition, and machine vibrations 5 , 6 . Surface roughness is important for improving accuracy of assembly, fatigue strength, and resistance of corrosion, therefore, it is identified as a key indicator to estimate the quality of the manufacture components 7 – 10 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20

Guo,

Zhou,

et al. 2023

Sci Rep

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The roughness of the part surface is one of the most crucial standards for evaluating machining quality due to its relationship with service performance. For a preferable comprehension of the evolution of surface roughness, this study proposes a novel surface roughness prediction model on the basis of the unity of fuse d signal features and deep learning architecture. The force and vibration signals produced in the milling of P20 die steel are collected, and time and frequency domain feature from the acquired signals are extracted by variational modal decomposition. The GA-MI algorithm is taken to select the signal features that are relevant to the surface roughness of the workpiece. The optimal feature subset is analyzed and used as the input of the prediction model. DBN is adopted to estimate the surface roughness and the model parameters are optimized by ISSA. (Reviewer 1, Q1) The separate force, vibration and fusion signal information are brought into the DBN model and the ISSA-DBN model for the prediction of surface roughness, and the results show that the accuracy of the roughness prediction is as follows, respectively DBN: 78.1%, 68.8% and 84.4%, and ISSA-DBN: 93.8%, 87.5% and 100%.

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Section: Introductionmentioning

confidence: 99%

Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20

Guo,

Zhou,

et al. 2023

Sci Rep

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show abstract

“…Scholars have made a large amount of achievements in the field of predicting surface roughness. Liu et al [10] developed a prediction method based on CDH platform, which optimized SVM with an improved Harris hawk optimization (IHHO) and had higher accuracy in surface roughness prediction compared with other optimized SVMs. Frigieri et al [11] established an associated model between the acoustic signal during turning and the surface roughness of the workpiece.…”

Section: Introductionmentioning

confidence: 99%

Prediction for surface roughness of the large-pitch internal thread based on homologous isomerism data

Qin

et al. 2023

Int J Adv Manuf Technol

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For the problem of difficulty in measuring the surface roughness of large-pitch internal threads, predictions are used instead of measurements, whereas the common predictions are not highly accurate and narrow in scope of use. In this paper, the homologous isomerism data of vibration signal were utilized to establish a predictive model, which predicted the surface roughness of large-pitch internal threads. The corresponding homologous isomerism data were acquired by turning the large-pitch internal thread, and the data were processed using Relief-F algorithm to obtain the weights, which are the effects of different features on surface roughness. Additionally, influenced by the structural characteristics of the workpiece with a large pitch and a small number of teeth, support vector machine (SVM) and RBF neural network, which are suitable for researches with small sample sizes, from the machine learning algorithm were used to establish the predictive model with the homologous isomerism data of vibration signal as the input parameters. Eventually, the SVM model with higher accuracy of prediction and better ability of generalization was more appropriate for the research of this paper through comparison and analysis. The SVM model was verified to have absolute errors within 0.05μm and relative errors within 4% for turning both left and right threaded surface, demonstrating that the predictive model could take the place of measuring the surface roughness in the mass production of large-pitch internal threads. The method proposed in this paper can also be extended to other parts for the prediction of surface roughness, especially those whose surface roughness are difficult to measure due to structural characteristics.

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“…However, due to the complex structure, narrow channel, and poor openness of the overall blisk, it is difficult to manufacture and process it, which has become a major technical problem in the aviation field. In recent years, a series of manufacturing techniques have been explored, such as milling [ 1 ], electrical discharge machining [ 2 ], electrolytic machining [ 3 ], laser cladding [ 4 ], and other techniques to manufacture blisks. Among them, milling has the advantages of having high reliability and a small production cycle and has been widely used in the field of blisk manufacturing [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Study on Residual Stress in Disc-Milling Grooving of Blisks

Xin

Xing²,

Dai³

et al. 2022

Materials

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The disc-milling method is expected to increase the grooving efficiency of blisks. However, there are few studies about the residual stress on a blisk during disc-milling grooving. In this study, a single-factor experiment and an orthogonal experiment of blisk disc-milling and grooving were designed to obtain the residual stress. Surface subsurface residual stress were also studied. The results showed that the surface of the milling groove bore compressive stress. Residual stress decreased with increasing spindle speed and increased with increasing feed speed and spindle rotation angle. Moreover, residual stress was most sensitive to spindle rotation angle and least sensitive to feed speed. A higher residual stress produced on the machined surface led to a deeper layer of residual stress.

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Surface roughness prediction method of titanium alloy milling based on CDH platform

Cited by 7 publications

References 8 publications

Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20

Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20

Prediction for surface roughness of the large-pitch internal thread based on homologous isomerism data

Study on Residual Stress in Disc-Milling Grooving of Blisks

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