‘Zero-wear’ of piston skirt surface topography

Krzyzak, Z; Pawlus, Paweł

doi:10.1016/j.wear.2005.03.038

Cited by 32 publications

(22 citation statements)

References 20 publications

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“…In [13], five amplitude parameters (Ra, Rq, Rz, Rku, and Rsk) and five material ratio parameters (Rk, Rpk, Rvk, MR1, and MR2) were used to determine the best indicator of run-in. However, it follows from the study presented in [14] that one can fairly precisely describe a piston skirt surface topography using the following parameters: Sq, Ssk, Str, and Sdq. For the detailed description of topography, the authors of work [14] also recommend parameters St±3σ, Sku, Sds, Ssc, and P∆ax/P∆ay.…”

Section: Introductionmentioning

confidence: 99%

Experimental Models and Correlations between Surface Parameters after Slide Diamond Burnishing

Korzynski

Dudek

Palczak

et al. 2018

Measurement Science Review

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In the paper the set of representative parameters for a comprehensive assessment of the surface texture status after slide burnishing has been proposed. The analysis of correlations between the parameters of the surface texture, obtained by slide diamond burnishing of 317Ti steel has been performed. Correlations have been determined and several groups of surface texture parameters with strong mutual correlations (also parameters uncorrelated with the other) have been selected. For both groups of parameters - representative and uncorrelated - experimental mathematical relations defining influences of the input parameters of slide diamond burnishing on the surface texture parameters have been developed. Also, interaction effects for individual parameters of this finishing process have been disclosed. It has been found that by appropriate selection of input conditions of the slide diamond burnishing process, it is possible to obtain a wide range of states of the surface texture.

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Section: Introductionmentioning

confidence: 99%

Experimental Models and Correlations between Surface Parameters after Slide Diamond Burnishing

Korzynski

Dudek

Palczak

et al. 2018

Measurement Science Review

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show abstract

“…For instance, one can mention research developments to use expensive materials like Functionally Graded Materials (FGM) and composites [1,2], to substitute new, expensive fuels like hydrogen instead of fossil fuels [3], to develop new bio-fuels [4,5], to use their combination with other fuels (bi-fuel concept) [6], and to inject them by a (multiple-) spray injector [7][8][9]. Additionally, state-of-the-art technologies such as highly boosting, down-speeding, downsizing and high compression ratio are introduced to meet the upcoming severe fuel consumption demand in SI engines [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A new and efficient mechanism for spark ignition engines

Shadloo¹,

Poultangari²,

Jamalabadi³

et al. 2015

Energy Conversion and Management

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“…This method was dedicated to the random base profile. When the valley part of two-process surface has a deterministic character (piston skirt after a low wear [39] can be the example), it can be modeled more easily compared to the random valley portion [12].…”

Section: Discussionmentioning

confidence: 99%

Reverse Problem in Surface Texture Analysis—One-Process Profile Modeling on the Basis of Measured Two-Process Profile after Machining or Wear

2019

Self Cite

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The method of the base (valley) one-process profile modeling on the basis of the measured two-process profile was developed. The base one-process random profile of the Gaussian ordinate distribution is characterized by the standard deviation of the profile height and the correlation length. The problem of estimation of the correlation length of this one-process profile exists. In the procedure of the correlation length estimation, information about the averaged shape of the autocorrelation functions of many one-process profiles after the same type of machining is required. The correlation length of the base one-process profile can be obtained on the basis of the vertical truncation of the measured two-process profile. The average error of the correlation length estimation was not higher than 7%, while the maximum error was not larger than 14%. This method can be extended to simulate the one-process texture of 3D (areal) surface topography.One-process surfaces are well described. The analysis of the measured one-process texture is simple. The digital filtration is an easy task. A typical Gaussian filter can be used. A one-process random profile can be easily described by only two parameters: height and horizontal. It can be completely characterized by the standard deviation of height (Pq), and a correlation length (CL), i.e., the distance at which the autocorrelation function slowly decays to a value of 0.1 [11,12]. Figure 1 presents the example of the random one-process profile and its autocorrelation function with the CL parameter. Materials 2019, 12, x FOR PEER REVIEW 2 of 12 Author Contributions: Conceptualization: P.P., R.R., and M.W.; methodology, investigation and formal analysis: P.P., R.R., and M.W.; writing-original draft preparation: P.P., R.R., and M.W.; writing-review and editing: P.P., R.R., and M.W.Funding: This research received no external funding. Conflicts of Interest:The authors declare no conflict of interest.

show abstract

‘Zero-wear’ of piston skirt surface topography

Cited by 32 publications

References 20 publications

Experimental Models and Correlations between Surface Parameters after Slide Diamond Burnishing

Experimental Models and Correlations between Surface Parameters after Slide Diamond Burnishing

A new and efficient mechanism for spark ignition engines

Reverse Problem in Surface Texture Analysis—One-Process Profile Modeling on the Basis of Measured Two-Process Profile after Machining or Wear

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