A novel representation method of non-ideal surface morphologies and its application in shaft-hole sealing simulation analysis

Wu, Jianshun; Qiao, Lihong; Zhu, Ziyi; Anwer, Nabil

doi:10.1177/0954405417738284

Cited by 14 publications

(6 citation statements)

References 35 publications

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“…Thus far, few mathematical models have been proposed for determining the geometric characteristics of planar and cylindrical features given manufacturing variation sources' deviations e.g. (Liu et al 2018;Wu et al 2017Wu et al , 2018. However, the mathematical approaches require expert knowledge to apply in more complex products .…”

Section: Variation Propagation Modellingmentioning

confidence: 99%

“…However, the mathematical approaches require expert knowledge to apply in more complex products . Even though, the work reported in Liu et al (2018) and Wu et al (2017Wu et al ( , 2018 can be extended in error source identification in multistage machining process, there has not been systematic attempt for error source identification based on SMSs.…”

Section: Variation Propagation Modellingmentioning

confidence: 99%

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A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes

Yacob

Semere

2020

J Intell Manuf

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Variation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task to model mathematically. Moreover, the application of the variation propagation approaches and associated variation source identification techniques using Skin Model Shapes is unclear. This paper proposes a multilayer shallow neural network regression approach to predict geometrical deviations of parts given manufacturing errors. The neural network is trained on a simulated data, generated from machining simulation of a point cloud of a part. Further, given a point cloud data of a machined feature, the source of variation can be identified by optimally matching the deviation patterns of the actual surface with that of shallow neural network generated surface. To demonstrate the method, a two-stage machining process and a virtual part that has planar, cylindrical and torus features was considered. The geometric characteristics of machined features and the sources variation could be predicted at an error of 1% and 4.25%, respectively. This work extends the application of Skin Model Shapes in variation propagation analysis in multistage manufacturing.

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Section: Variation Propagation Modellingmentioning

confidence: 99%

Section: Variation Propagation Modellingmentioning

confidence: 99%

A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes

Yacob

Semere

2020

J Intell Manuf

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“…1 In this simulation process, the contact status of the mating surfaces largely determines the tolerance analysis results. 2 In recent research outcomes, many studies have been conducted with consideration of non-ideal surfaces in order to obtain a more realistic simulation result based on different modeling methods of non-ideal surfaces. 3–6 Among these existing studies, the concepts of skin model 7 and skin model shapes 8,9 have been proposed to form a coherent and univocal language for geometric specifications.…”

Section: Introductionmentioning

confidence: 99%

Considerations of form defects and surface deformations for tolerance analysis of cylindrical components

Zhang

Liu

Pierre

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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The modeling and simulation of cylindrical surfaces with consideration of form defects have led to considerable research outcomes in the field of Computer-Aided Tolerancing (CAT). However, further consideration of surface deformations caused by external forces still remains a challenge. To address this issue, this paper properly considers the form defects and surface deformations for tolerance analysis of cylindrical components. First, form defects are considered by modeling skin model shapes of cylindrical surfaces. Afterwards, the tight fit and loose fit of a pair of cylindrical surfaces are identified, and the simulation methods of their positioning are presented. Specifically, for tight fit situation, a k-d tree based Iterative Closest Point (ICP) algorithm is used, and for loose fit situation, the constrained registration approach is adopted. Moreover, a Conjugate Gradient-Fast Fourier Transform (CG-FFT) method is presented for the consideration of surface deformations. In addition, simulations of given examples are conducted, which show the considerable effects of form defects and surface deformations. The simulations may also help determine the best performance of the to-be-assembled cylindrical components.

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“…To better research the form of non-ideal surfaces, Wu et al 13,14 proposed a modeling method of non-ideal surface deviation on the perspective of manufacturing error. By establishing a deviation coordinate system, different deviation functions are constructed to represent non-ideal surface under the influence of manufacturing errors.…”

Section: Introductionmentioning

confidence: 99%

An assembly deviation calculation method based on surface deviation modeling for circumferential grinding plane

Qiao

Shao

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Performance of mechanical product is highly influenced by assembly deviation. Due to manufacturing errors, the real part surface is machined with morphology deviations, which would cause mating surface deviating from ideal position in assembly behavior, consequently leading to assembly deviation. Meanwhile, the random variation of relative position and orientation between two non-ideal parts also affects the assembly deviation. To efficiently obtain the maximum assembly deviation considering the comprehensive influence of two factors above for circumferential grinding plane, an assembly deviation calculation method based on surface deviation modeling is proposed in this paper. In this method, morphology deviations models of part surfaces are firstly established from the deviation function. The randomness of two factors are represented by a multivariate group with randomness containing deviation function coefficients and three deflected parameters. Then based on surface deviation modeling method, differential evolution algorithm is applied to search the maximum assembly deviation, which involves the construction of fitness function by implementing optimized progressive contact method and iterative operations of mutation, crossover and selection. Finally, the effectiveness of this method is illustrated by an assembly in the end.

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A novel representation method of non-ideal surface morphologies and its application in shaft-hole sealing simulation analysis

Cited by 14 publications

References 35 publications

A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes

A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes

Considerations of form defects and surface deformations for tolerance analysis of cylindrical components

An assembly deviation calculation method based on surface deviation modeling for circumferential grinding plane

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