Multimode tool tip dynamics prediction based on transfer learning

Liu, Xu; Li, Yingguang; Chen, Gengxiang

doi:10.1016/j.rcim.2018.12.001

Cited by 26 publications

(6 citation statements)

References 31 publications

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“…This method can not only predict the performance of the machine tool, but also be more convenient for the performance analysis of the machine tool with the tool spindle combination changed. Chen et al [ 23 ] and Liu et al [ 24 ] combined substructure coupling with deep learning and used the transfer learning method to predict tool tip dynamic information at different positions. The existing modal [ 25 ] analysis methods are limited to the static conditions of machine tools, and most of them are applied to the transition of machining state, and few of them are used to analyze the influence of cutting force.…”

Section: Introductionmentioning

confidence: 99%

Cutting Force Transition Model Considering the Influence of Tool System by Using Standard Test Table

Chen

Zhang

Wang

2021

Sensors

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The cutting force prediction model usually uses the classical oblique transformation method, which introduces the orthogonal cutting parameters into the oblique milling edge shape, and combines the geometric parameters of the tool to convert the orthogonal cutting force into the actual cutting force, thereby predicting the cutting force. However, this cutting force prediction method ignores the impact of tool vibration in actual machining, resulting in a large difference between the prediction model and the actual measurement. This paper proposes a cutting force conversion model considering the influence of the tool system. The proposed model fully considers the impact of tool vibration on the cutting force. On the basis of the orthogonal model, superimposing the additional cutting force generated by tool vibration makes the predicted value of the model closer to the actual cutting force. The results of milling experiments show that the conversion model can obtain higher prediction accuracy. Moreover, compared with the original conversion model, the accuracy of the proposed model is significantly improved.

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Section: Introductionmentioning

confidence: 99%

Cutting Force Transition Model Considering the Influence of Tool System by Using Standard Test Table

Chen

Zhang

Wang

2021

Sensors

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“…And in addition, the predicted FRFs at the base points were further used to predict the FRFs for different tools based on the receptance coupling substructure analysis (RCSA). Similarly, considering the different tool-holder assemblies, Liu and Chen et al [17,18] used transfer learning to predict the pose-dependent tool tip dynamics by integrating domain adaptation and adaptive weighting. Once the pose-dependent tool tip dynamics were obtained by sufficient impact tests, only few impact tests to measure the new tool tip dynamics were required.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tool Point Frequency Response Functions within Machine Tool Work Volume Considering the Position and Feed Direction Dependence

et al. 2020

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A chatter vibration in milling process results in poor surface finish and machining efficiency. To avoid the chatter vibration, the stability lobe diagram (SLD) which is the function of tool point frequency response functions (FRFs) is adopted to predict the chatter-free machining parameters. However, the tool point FRF varies with the changes of machining positions and feed directions within machine tool work volume. Considering this situation, this paper presents a method to predict the position and feed direction-dependent tool point FRF. First, modal parameters of the tool point FRFs obtained at some typical positions and feed directions are identified by the modal theory and matrix transformation method. With the sample information, a back propagation (BP) neural network whose inputs are the position coordinates and feed angle and outputs are the modal parameters can be trained with the aid of the particle swarm optimization (PSO) algorithm. Then, modal parameters corresponding to any position and feed direction can be predicted by the trained BP neural network and used to reorganize the tool point FRFs with the modal fitting technique. A case study was performed on a real vertical machining center to demonstrate the accurate prediction of position and feed direction-dependent tool point FRFs. Furthermore, the position and feed direction-dependent milling stability was researched and origin-symmetric distributions of the limiting axial cutting depths at each machining position were observed.

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“…e fitting methods are widely used to calculate the dynamic parameters based on FRF. Liu et al [14] used a fitting method to achieve the dynamic parameters of the tooltip from FRF. Sun and Altintas [15] and Song et al [16] build FRF of the cutting tool by impact test and identified the modal parameters using the fitting method.…”

Section: Introductionmentioning

confidence: 99%

“…Sun and Altintas [15] and Song et al [16] build FRF of the cutting tool by impact test and identified the modal parameters using the fitting method. From [13][14][15][16], it is known that the fitting method is easily performed in computing the modal parameters. However, the fitting method always tries to obtain a mean curve of FRF which would dismiss the peaks of the original FRF curve.…”

Section: Introductionmentioning

confidence: 99%

Measurement-Based Modal Analysis and Stability Prediction on Turn-Milling of Hollow Turbine Blade

Zhao

Hou

2020

Shock and Vibration

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Hollow blades with honeycomb structures are increasingly used in the turbine engines for reducing weight and saving costs. The hollow blade is a typical thin-walled structural part with low stiffness, the machining system of which is often unstable and likely to chatter. The most effective solution to avoid the machining chatter is to guide the hollow blade to be machined in a stable machining zone. This paper proposes a measurement-based approach for modal analysis and stability prediction of turn-milling hollow blade. The impact test was carried out to achieve the FRF curves on the hollow blade and the milling tool. An extremum method was employed to obtain an equivalent FRF curve, from which the modal parameters involving the natural frequency, damping ratio, and stiffness were computed. Afterwards, the semidiscretization method was used to draw a stability lobe diagram to predict the stability when turn-milling hollow blades. The experimental results confirm the feasibility of the predicted stability lobe diagram.

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Multimode tool tip dynamics prediction based on transfer learning

Cited by 26 publications

References 31 publications

Cutting Force Transition Model Considering the Influence of Tool System by Using Standard Test Table

Cutting Force Transition Model Considering the Influence of Tool System by Using Standard Test Table

Prediction of Tool Point Frequency Response Functions within Machine Tool Work Volume Considering the Position and Feed Direction Dependence

Measurement-Based Modal Analysis and Stability Prediction on Turn-Milling of Hollow Turbine Blade

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