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

Deng, Congying; Feng, Yong; Shu, Jie; Huang, Zhiyu; Tang, Qian

doi:10.3390/sym12071073

Cited by 5 publications

(3 citation statements)

References 30 publications

(46 reference statements)

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“…However, the continuous cutting process will inevitably lead to tool wear, which further affects the surface integrity of the machined surface [7]. During milling, changes in machining position and cutting geometry might exacerbate tool load changes, which also degrades the surface formation process and further constrains the improvement of workpiece surface quality [8]. Besides, as presented above, some key parameters (e.g.…”

Section: Introductionmentioning

confidence: 99%

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Man,

Zhou,

Sun

et al. 2024

Surf. Topogr.: Metrol. Prop.

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Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in-situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is proposed in this research. By evaluating the texture similarity intelligently between the testing image and the reference image, the surface roughness of the testing image can be acquired. Firstly, with a proposed adaptive texture extraction method, the texture feature of an image can be extracted even under a complex background. Secondly, by establishing the graph structure of the texture grayscale features, the similarity between different images is evaluated. Finally, by establishing a sparrow-optimized support vector machine regression method, the mapping relationship between the similarity and the surface roughness can be acquired. The experimental results indicate that the proposed method for intelligent evaluation of roughness has superior prediction performance (the average relative prediction error of Ra and Rz are 8.8156% and 8.0571%, respectively). Therefore, this work provides a useful tool for non-contact detection of workpiece surface roughness.

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Section: Introductionmentioning

confidence: 99%

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Man,

Zhou,

Sun

et al. 2024

Surf. Topogr.: Metrol. Prop.

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“…Law et al [15] proposed a reduced model substructural synthesis method to efficiently establish a position-dependent multibody dynamic of a machine tool, benefiting rapid predictions of tool point FRFs and milling stability within the whole machine tool working space. Deng et al [16] establish a backpropagation neural network model to predict the position and feed direction-dependent tool point FRFs, and then study the effects of tool position and feed direction angle on the milling stability.…”

Section: Introductionmentioning

confidence: 99%

Chatter Stability Prediction and Process Parameters’ Optimization of Milling Considering Uncertain Tool Information

Lin

He²,

Wang

et al. 2021

Symmetry

Self Cite

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Stability is the prerequisite of a milling operation, and it seriously depends on machining parameters and machine tool dynamics. Considering that the tool information, including the tool clamping length, feeding direction, and spatial position, has significant effects on machine tool dynamics, this paper presents an efficient method to predict the tool information dependent-milling stability. A generalized regression neural network (GRNN) is established to predict the limiting axial cutting depth, where the machining parameters and tool information are taken as input variables. Moreover, an optimization model is proposed based on the machining parameters and tool information to maximize the material removal rate (MRR), where the GRNN model is taken as the stability constraint. A particle swarm optimization (PSO) algorithm is introduced to solve the optimization model and provide an optimal configuration of the machining parameters and tool information. A case study has been developed to train a GRNN model and establish an optimization model of a real machine tool. Then, effects of the tool information on milling stability were discussed, and an origin-symmetric phenomenon was observed as the feeding direction varied. The accuracy of the solved optimal process parameters corresponding to the maximum MRR was validated through a milling test.

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“…The feasibility of the proposed approach was checked by case studies on a vertical machining center. The authors stated that the developed approach can contribute to select the optimal process planning based on actual milling conditions at the design stage [13]. Additionally, a reacceptance coupling-based approach was developed to describe the dynamic properties of the machine-tool-workpiece system [14].…”

Section: Introductionmentioning

confidence: 99%

Sustainability-Based Optimization of the Rotary Turning of the Hardened Steel

Nguyen

Duong

Mia

2020

Metals

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The rotary turning is an effective manufacturing method to machine hardened metals due to longer tool life, higher production rate, and acceptable quality. However, sustainability-based optimization of the rotary turning has not been thoroughly considered because of the huge efforts. This study presents an optimization to enhance the energy efficiency (EFR), turning cost (CT), average roughness (Ra), and the operational safety (POS) for the rotary turning of the hardened steel. Four key process parameters considered are the inclined angle (α), depth of cut (ap), feed rate (f), and cutting speed (vc). The improved Kriging (IK) models were used to construct the relations between the parameters and performances. The optimum varied factors were obtained utilizing the neighborhood cultivation genetic algorithm (NCGA). The findings revealed that the performance models are primarily affected by the feed rate, depth of cut, speed, and inclined angle, respectively. The optimal values of the α, ap, f, and vc are 26°, 0.44 mm, 0.37 mm/rev, and 200 mm/min, respectively. The improvements in energy efficiency, average roughness, and cost are 8.91%, 20.00%, and 14.75%, as compared to the initial values. Moreover, the NCGA may perform an efficient operation to obtain the optimal outcomes, as compared to conventional algorithms.

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

Cited by 5 publications

References 30 publications

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Chatter Stability Prediction and Process Parameters’ Optimization of Milling Considering Uncertain Tool Information

Sustainability-Based Optimization of the Rotary Turning of the Hardened Steel

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