Quality control in machining using order statistics

Diamoutene, Abdoulaye; Noureddine, Farid; Kamsu-Foguem, Bernard; Barro, Diakarya

doi:10.1016/j.measurement.2017.11.036

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…As mentioned in section ''Operating and environmental conditions'' and thanks to the values shown in Tables 2 and 3, three individual contributions were studied. Thus, the influence of vibration, material hardness defect, and lubrication have been calculated and shown in Finally, the GEV proportional hazard function, given in equation (19), with all corresponding values of u(b 1 X 1 ), u(b 2 X 2 ), and u(b 3 X 3 ), enables Figure 5 to be drawn, where it can be deduced that all contributions of covariates deteriorate the baseline hazard function.…”

Section: Methodsmentioning

confidence: 99%

“…Benstock and Cegla 17 use extreme value analysis in modeling corroded surfaces, whereas Letot et al 18 involve four approaches to study the reliability of cutting tool. Recently, Diamoutene et al 19 use order statistics in the modeling of workpiece quality, thanks to surface roughness measurements.…”

Section: Cutting Tool Reliabilitymentioning

confidence: 99%

“…This factor is the cutting tool state which has been broadly studied, for more details, see previous works. 15,19 A performing tool is fundamental to obtain a given surface roughness. According to the parts type and, above all, according to the concerned field, constraints will be more or less severe.…”

Section: Cutting Tool Reliabilitymentioning

confidence: 99%

See 2 more Smart Citations

Proportional hazard model for cutting tool recovery in machining

Diamoutene

Noureddine

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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The proportional hazard model is a statistical method capable of including information on environmental and operating conditions. In machining, in the reliability field of a cutting tool, the interest of using proportional hazard model is highlighted. On one hand, the environmental and operating conditions are described and taken into account as explanatory variables. Three covariates are considered, namely, the vibration signal, the hardness material, and the lubrication/cooling. On the other hand, a new baseline hazard function is designed according to phenomena of tiny tool breakage followed by a self-sharpening process. This latter phenomenon, which can be considered as a rare event, prompted us to study extreme value theory to propose an adequate baseline hazard function. The newly obtained baseline hazard function will be named generalized extreme value proportional hazard model. This function is obtained thanks to the Gumbel function and has the property to be non-monotonic, an increasing then decreasing function. An alternative option as a baseline hazard function, based on the flexible Weibull distributions, is also proposed. Results produced in this article show the impact of all these variables on the surface roughness of the machined parts. According to reliability studies, the premature replacement of the cutting tool implying financial losses can be delayed. This may be of particular significance and benefit, in terms of sustainable development, in the case of mass production, by limiting the frequency of tool replacement.

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Section: Methodsmentioning

confidence: 99%