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GAMLSS models as an alternative to improve the coating process of surgical instruments with hard chromium

GAMLSS models to study coating process

10.24050/reia.v19i38.1536
Published 2022-06-08
Articles

Abstract

Surgical instruments are used in surgical interventions and must be resistant to corrosion and wear to avoid contamination. To improve the mechanical properties of surgical instruments, a surface modification can be made by coating the instruments with some material. Several factors intervene in the coating process that affects interest characteristics, and the classical linear regression model has traditionally been used to explore these relationships. The variables of interest are random variables and do not always follow the normal distribution, which is the statistical distribution assumed in the classical linear regression model. Using an appropriate regression model to study surgical instrument coating processes is essential. This article uses experimental data obtained from coating stainless steel surgical needle holders with hard chromium. The experimental data were analyzed using linear models to study the effect of exposure time, current density, and temperature on the average thickness of the coating of the needle holders. The final model had a gamma response, and the significant variables were time and density. Using this model, we obtained mathematical expressions to estimate the mean and variance of the average thickness.

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References

Akaike, H. (1983). Information measures and model selection. Int Stat Inst, 44, pp. 277–291.

Arias, J.; Aller, M. Á.; Fernández-Miranda, E.; Arias, J. I.; Lorente, L. (2004). Propedéutica quirúrgica Preoperatorio, operatorio, postoperatorio. Editorial Tebar.

Bayramoglu, M.; Onat, B.; Geren, N. (2008). Statistical optimization of process parameters to obtain maximum thickness and brightness in chromium plating. Journal of Materials Processing Technology, 203(1–3), pp. 277–286. https://doi.org/10.1016/j.jmatprotec.2007.09.063

Buuren, S. van,; Fredriks, M. (2001). Worm plot: a simple diagnostic device for modelling growth reference curves. Statistics in Medicine, 20(8). https://doi.org/10.1002/sim.746

Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientum.

Gorokhovsky, V.; Heckerman, B.; Watson, P.; Bekesch, N. (2006). The effect of multilayer filtered arc coatings on mechanical properties, corrosion resistance and performance of periodontal dental instruments. Surface and Coatings Technology, 200(18), pp. 5614–5630. https://doi.org/https://doi.org/10.1016/j.surfcoat.2005.07.104

Lasheras, F. S.; Vilán, J. A. V.; Nieto, P. J. G.; del Coz Díaz, J. J. (2010). The use of design of experiments to improve a neural network model in order to predict the thickness of the chromium layer in a hard chromium plating process. Mathematical and Computer Modelling, 52(7–8), pp. 1169–1176. https://doi.org/10.1016/j.mcm.2010.03.007

Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des cométes. Firmin Didot, Paris.

Montgomery, D. C.; Peck, E.; Vining, G. (2006). Introducción al Análisis de Regresión Lineal (3rd ed.). Editorial Continental.

Nascimento, M. P.; Souza, R. C.; Miguel, I. M.; Pigatin, W. L.; Voorwald, H. J. C. (2001). Effects of tungsten carbide thermal spray coating by HP/HVOF and hard chromium electroplating on AISI 4340 high strength steel. Surface and Coatings Technology, 138(2–3), pp. 113–124. https://doi.org/10.1016/S0257-8972(00)01148-8

Rigby, R. A.; Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Applied Statistics, 54(3), pp. 507–554.

Ruiz, J.; Parra, C. M.; Bustamente, C. M.; Vélez, E.; Rivera, L. A. (2003). Modelamiento del proceso de recubrimiento de instrumental quirúrgico con cromoduro. Revista Facultad de Ingeniería Universidad de Antioquia, 30, pp. 89–94. https://www.redalyc.org/articulo.oa?id=43003009

Sánchez-Sarrí, A, O.; González-Diez, Y.; Hernández-Dávila, C.; Dávila-Cabo-de-Villa, E. (2014). Manual de instrumental quirúrgico. MediSur, 12(5), pp. 781–818. http://medisur.sld.cu/index.php/medisur/article/view/2662

Stasinopoulos, M. D.; Rigby, R. A.; Heller, G. Z.; Voudouris, V.; De Bastiani, F. (2017). Flexible regression and smoothing: using GAMLSS in R. CRC Press.

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