Feedrate scheduling method for constant peak cutting force in five-axis flank milling process

Wang, Liping; Xing, Yuan; Si, Hao; Duan, Feiyu

doi:10.1016/j.cja.2019.09.014

Cited by 19 publications

(13 citation statements)

References 35 publications

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“…Zhang et al presented an accurate calculation model for the tool-worker engagement region of five-axis ball-end milling, based on which a tool orientation optimization strategy was proposed for the milling process [8]. In terms of chip thickness solving, Wang et al provided a solution method for the instantaneous undeformed chip thickness taking the tool entry angle and feed rate as variables, based on which an explicit analytical expression for the peak milling force at each cutting point was established [9]. Li et al developed a Boolean method consisting of subtraction and intersection operations to solve the instantaneous undeformed chip thickness, which compensates for the defects of analytical and numerical solutions [10].…”

Section: Introductionmentioning

confidence: 99%

Error analysis of blade milling considering surface features and deformation

Wu,

Chunfeng,

Xianli

et al. 2024

Preprint

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Thin-walled impeller blade curvature changes in the milling process, low stiffness and other reasons lead to large milling processing error, in order to compensate for online blade milling processing error,A milling machining error prediction method is proposed by considering the curved surface features and deformation of the blade. First, based on the tool-worker contact relationship of blade curvature and machining deformation individually, the undeformed and deformation chip thickness models considering curvature and deformation are constructed to analyze influence law of curvature change and deformation on the chip thickness individually; Then, change amount of the chip thickness considering undeformed and deformed in the tool coordinate system is converted to the surface coordinate system. The surface normal vector of variation is taken as the predicted machining error; Finally, corresponding experiments are conducted on five-axis machine to indicate that the error between the predicted and the experimentally measured machining error during stable milling falls within 21%.

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Section: Introductionmentioning

confidence: 99%

Error analysis of blade milling considering surface features and deformation

Wu,

Chunfeng,

Xianli

et al. 2024

Preprint

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“…In flank milling each cutting tool tooth cuts the workpiece intermittently, removing material from the workpiece to form the final surface. The milling force is the basis for feedrate optimization [1][2], chatter [3], and prediction of the quality of the machined surface [4][5]. The IUCT calculation is a key issue in the milling force prediction, which directly affects the efficiency and accuracy of the force model.…”

Section: Introductionmentioning

confidence: 99%

Modeling of instantaneous uncut chip thickness in 5-AXIS flank milling considering cutter runout

Cheng,

Xu,

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…The reparametrization was based on knowledge of RDM (ruling distance metric), which represents the distance of two consecutive tool positions on the path. Wang et al [17] proposed a feed rate optimization method based on the cutting force model. The feed rate was optimized so that the cutting forces were constant.…”

Section: Introductionmentioning

confidence: 99%

Determination of Impeller Blade Fillet Radius for Productive Finish Milling

Vavruska

Kratena²,

Cech³

et al. 2023

Preprint

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Impellers, which are part of the turbochargers in many transport and agricultural vehicles such as cars, trucks, tractors, boats as well as lawnmowers, are parts where monitoring the efficiency of design and subsequent manufacturing is very important. In order to maximize the efficiency of the compressor stage, many iterations in impeller blade geometry design must be performed to achieve the operating conditions in terms of both performance and strength. Impeller manufacturing is then strongly dependent on the impeller blade geometry design. Manufacturing efficiency varies greatly if the impeller is designed with the blades made up of complex surfaces (requires more machining time) or ruled surfaces (requires less machining time). However, the two impeller geometries have a common element that strongly influences their manufacturing efficiency – the impeller blade fillet radius. The choice of the blade fillet radius value is very limited by the impeller blade geometry design. Therefore, this paper proposes a method to determine the blade fillet radius value in order to achieve an efficient impeller geometry in terms of manufacturing efficiency at the impeller design stage. The method is verified on the example of milling several impeller variants.

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Feedrate scheduling method for constant peak cutting force in five-axis flank milling process

Cited by 19 publications

References 35 publications

Error analysis of blade milling considering surface features and deformation

Error analysis of blade milling considering surface features and deformation

Modeling of instantaneous uncut chip thickness in 5-AXIS flank milling considering cutter runout

Determination of Impeller Blade Fillet Radius for Productive Finish Milling

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