Design and Performance of a Small Precision CNC Turning Machine

Donaldson, R. R.; Thompson, D. C.; Loewen, Erwin G.

doi:10.1016/s0007-8506(07)61909-x

Cited by 44 publications

(11 citation statements)

References 8 publications

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“…The largest gray comprehensive correlation degree was selected as the optimal measuring point, that is, ρ 1,9 = 0.5674 was the maximum. Above all, the placement locations of [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12] 0.8376 10 [1,9,10], [2], [3], [4], [5], [6], [7], [8], [11], [12] 0.8272 9 [1,2,9,10], [3], [4], [5], [6], [7], [8], [11], [12] 0.8114 8 [1,2,6,9,10], [3], [4], [5], [7], [8], [11], [12] 0.7819 7 [1,2,4,6,…”

Section: Optimization Analysis Of Temperature-sensitive Pointsmentioning

confidence: 99%

“…Machine tool thermal error is the main factor causing the reduction of machining accuracy and accounts for 40-70% of total errors [1][2][3]. There are two methods used to reduce machine tool thermal errors [3]: reducing thermal deformation by improving structural designs and controlling thermal errors from heat transfer and dissipation, such as the introduction of an additional equilibrium heat source or auxiliary cooling device, and increasing the rigidity of machine tools [4,5]. However, the second method requires modifying the machine tool structure and increasing the cost.…”

Section: Introductionmentioning

confidence: 99%

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Thermal error modeling of CNC milling machining spindle based on an adaptive chaos particle swarm optimization algorithm

Yue

Guo

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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The thermal error of machine tool spindle system has become an important factor affecting machining accuracy. Measurement experiments of a 3-axis vertical milling machine spindle system were conducted to assess temperature fields and thermal errors. The fuzzy clustering and gray correlation algorithms were adopted to cluster the temperature measuring points and identify the temperature-sensitive points. Based on the adaptive chaos particle swarm optimization algorithm, thermal error models were established in the axial and radial directions for the spindle system, and the compensation effects were evaluated by the workpiece machining accuracy. The results showed that the number of temperature measuring points was reduced from 12 to 6. The residual range of measured and predicted thermal error values in the axial direction was 6.17-4.19 μm, and the modeling accuracy was 95.53%. The radial residual ranges were − 2.75-3.05 μm and − 2.10-2.15 μm, and the modeling accuracies were 90.74% and 91.10%, respectively. The model compensation effect was demonstrated remarkably in the verification experiments. The thermal error models showed high prediction precision, could improve machining accuracy and had strong engineering application value.

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Section: Optimization Analysis Of Temperature-sensitive Pointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Thermal error modeling of CNC milling machining spindle based on an adaptive chaos particle swarm optimization algorithm

Yue

Guo

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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show abstract

“…There is a significant sensitivity with respect to the influence of moisture. However, also due to its thermal and damping characteristics, granite is the dominant material for CMMs and ultra-precision machines [41,43,72,123,272,278]. In Fig.…”

Section: Natural Stone and Ceramicsmentioning

confidence: 99%

Materials in machine tool structures

et al. 2015

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“…It soon became committed to parts production and a nearly identical machine, designated the PERL-II, was constructed a year later to carry on the research. 18 Full numerical control was implemented in 1985 and the design earned an IR 100 award a few years later.…”

Section: Fabricationmentioning

confidence: 99%