A Study on Five-Axis Flank Machining of Centrifugal Compressor Impellers

Tsay, Der-Min; Chen, Huo Chu; Her, Ming Jane

doi:10.1115/2000-gt-0342

Cited by 3 publications

(4 citation statements)

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“…The cutter axis was then determined according to the approximated cutting error. Tsay et al also provided a method in [3] using coordinate transformation to adjust cutter axis, and a B-spline surface was machined to show their method's result.…”

Section: Related Workmentioning

confidence: 99%

Optimal Quadrangulation of a Strip for Flank Milling

Tsai

Wang²,

Chu

et al. 2008

Computer-Aided Design and Applications

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This paper presents a modeling algorithm that helps automatically generate a quadrangulation strip for flank milling which minimize the cutting error to a designed reference surface. Two curves are given as the boundary of a ruled surface to be machined. We discrete two curves into sample points and try to connect them to form quadrangles by rule lines, where these rule lines simulate the cutter's moving trajectory (i.e. tool path). Not only the surface error but also some other engineering constraints are considered in our optimal quadrangulation algorithm.

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Section: Related Workmentioning

confidence: 99%

Optimal Quadrangulation of a Strip for Flank Milling

Tsai

Wang²,

Chu

et al. 2008

Computer-Aided Design and Applications

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“…So, the surface roughness prediction is needed to be acquired for evaluating the machining accuracy level of machine tool. Nowadays, the researchers have explored a series of factors influencing the surface roughness, including cutting conditions, 10–12 work-piece material, 18–20 tool position and tool wear, 21–23 the vibration of machining tool, 24 etc. Based on these literatures, prediction is a feasible method for carrying on surface roughness estimation for the whole disc turning machining.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness optimal estimation for disc parts turning using Gaussian-process-based Bayesian combined model

Liu

Lin

Mao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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The surface roughness is an important characterization of the products performance, and its estimation is required to evaluate the machining accuracy level of the turning machining or its machining condition monitoring. Traditional methods are using machining parameters or combined machining parameters with tool vibration to predict the surface roughness. But for a steel disc part turning machining, the surface roughness value on a circumference trajectory is not the same as one section of the trajectory because randomness element will exist in surface roughness value. This paper proposed a machine learning approach of using Gaussian-process-based Bayesian combined model to construct surface roughness trajectory prediction. Gaussian-process-based Bayesian combined model is a good machine learning method for dealing with variable with random element and introduced into building the correlation between surface roughness and surface roughness increment for next point in a trajectory. To show the process of the method establishing the model, a simulation is carried on first. Then, a turning experiment was conducted. The experimental results verify that Gaussian-process-based Bayesian combined model compared with Gaussian-process model can be used to predict the surface roughness in a more reliable way.

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“…Numerous previous literatures (Bedi et al 2003;Liu 1995;Bohez et al 1997;Tsay and Her 2001;Tsay et al 2002;Chu and Chen 2006) have studied tool path planning in 5-axis machining of ruled surfaces. Bedi et al (2003) analyzed the relationship between the tool position and the resultant machining errors like the amount of undercut and overcut.…”

mentioning

confidence: 99%

“…They claim that the approach can significantly reduce the amount of tool interference. Tsay and Her (2001), Tsay et al (2002) analyzed the influence of the yaw and tilt angles on the amount of tool interference at a cutter location. The result serves as look-up tables in a tool path planning algorithm.…”

mentioning

confidence: 99%

Tool path planning for 5-axis flank milling of ruled surfaces considering CNC linear interpolation

Chu

Lei

2010

J Intell Manuf

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This paper investigates tool path planning for 5-axis flank milling of ruled surfaces in consideration of CNC linear interpolation. Simulation analyses for machining error show insights into the tool motion that generates a precision machined surface. Contradicting to previous thoughts, the resultant tool path does not necessarily produce minimal machining error when the cutter contacts the rulings of a developable surface. This effect becomes more significant as the distance between two cutter locations is increased. An optimizing approach that adjusts the tool position locally may not produce minimal error as far as the entire surface is concerned. The optimal tool path computed by a global search scheme based on dynamic programming supports this argument. A flank milling experiment and CMM measurement further validate the findings of this work.

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A Study on Five-Axis Flank Machining of Centrifugal Compressor Impellers

Cited by 3 publications

References 0 publications

Optimal Quadrangulation of a Strip for Flank Milling

Optimal Quadrangulation of a Strip for Flank Milling

Surface roughness optimal estimation for disc parts turning using Gaussian-process-based Bayesian combined model

Tool path planning for 5-axis flank milling of ruled surfaces considering CNC linear interpolation

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