Abstract
Chatter occurring in the milling process can seriously deteriorate the machining efficiency and surface quality. The stability diagram predicted using the tool tip frequency response functions (FRFs) is an effective approach to avoid the chatter vibration. The tool tip FRFs highly depend on the characteristics of the tool-holder-spindle-machine tool frame assembly. Thus, when the tool-holder assembly or only the tool overhang length changed, the FRFs will be reobtained to plot the stability diagrams. Considering this time-consuming situation, this paper introduces the transfer learning to efficiently predict the milling stability of arbitrary tool-holder combinations. First, a source tool-holder assembly is selected to measure sufficient overhang length-dependent tool tip FRFs and then predict the limiting axial cutting depth aplim values under different process parameters for forming the source data. For a new tool-holder assembly, impact tests are only performed under a few key tool overhang lengths to measure the tool tip FRFs and then predict the aplim values to form the target data. Combining the target data and the source data, the transfer learning containing the domain adaptation and adaptative weighting is introduced to train an overhang length-dependent milling stability prediction model of a target tool-holder assembly. A case study has been performed on a vertical machine tool with four different tool-holder assemblies to validate the feasibility of the proposed transfer learning-based milling stability prediction method.
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Abbreviations
- a plim :
-
The limiting axial cutting depth (mm)
- n :
-
The spindle speed (rpm)
- a e :
-
The radial cutting width (mm)
- f z :
-
The feed rate per tooth (mm/z)
- l t :
-
The tool overhang length (mm)
- X s, X t :
-
The instance spaces of the source and target domains
- Y s, Y t :
-
The label spaces of the source and target domains
- x s, x t :
-
The instances of the Xs and Xt
- y s, y t :
-
The labels of Ys andYt
- T ds, T dt :
-
The source and target data
- T a :
-
The combination of the source and target data
- M, M 1 :
-
The transfer matrixes
- T ds_new, T dt_new :
-
The source and target data in the homogeneous coordinate
- T dt_new2 :
-
The source data transferred by M1
- W k :
-
The sample weight vector
- BPNN:
-
Back propagation neural network
- RM:
-
Random forest
- GBDT:
-
Gradient boosting decision tree
- T1 :
-
The source tool with the diameter 14 mm
- T2, T3, T4 :
-
The target tools with diameters 16 mm, 12 mm and 8 mm
- R 2 :
-
R-square
- RMSE:
-
Root mean square error
- E_m :
-
Mean value of the relative errors
- DS:
-
Data size determined according to the tool overhang length
- TLPM:
-
The transfer learning-based prediction model with a domain adaptation
- PM_2:
-
The prediction model trained only by the target data
- PM_3:
-
The prediction model trained by the target data and the source data processed by a domain adaptation
- PM_4:
-
The prediction model directly trained by the target and source data
- PM_5:
-
The transfer learning based-prediction model without a domain adaptation
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Funding
National Natural Science Foundation of China, 51705058, Congying Deng, China Postdoctoral Science Foundation Funded Project, 2018M633314, Congying Deng, Chongqing Special Postdoctoral Science Foundation, XmT2018040, Congying Deng, Chongqing Graduate Scientific Research Innovation Project, CYS21317, Jielin Tang
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Deng, C., Tang, J., Miao, J. et al. Efficient stability prediction of milling process with arbitrary tool-holder combinations based on transfer learning. J Intell Manuf 34, 2263–2279 (2023). https://doi.org/10.1007/s10845-022-01912-5
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DOI: https://doi.org/10.1007/s10845-022-01912-5