Static Robust Design Optimization Using the Stochastic Frontier Method: A Case Study of Pulsed EPD Process on TiO2 Films

Author:

Rezgui Mohamed Ali1,Trabelsi Ali1,Barbana Nesrine2ORCID,Ben Youssef Adel3,Al-Addous Mohammad45ORCID

Affiliation:

1. Laboratory of Mechanics, Production and Energy, Higher National Engineering School of Tunis, University of Tunis, Tunis 1008, Tunisia

2. Department of Environmental Process Engineering, Institute of Environmental Technology, Technische Universität Berlin, 10623 Berlin, Germany

3. Higher National Engineering School of Tunis, University of Tunis, Tunis 1008, Tunisia

4. Department of Energy Engineering, School of Natural Resources Engineering and Management, German Jordanian University, Amman 11180, Jordan

5. Fraunhofer-Institut für Solare Energiesysteme, ISE, 79110 Freiburg, Germany

Abstract

This paper aims to optimize a pulsed electrophoretic deposition (EPD) process for TiO2 films. This is accomplished by determining the optimal configuration of the coating parameters from a robust optimization perspective. The experimental study uses a composite central design (CCD) with four control factors, i.e., the initial concentration (x1 in g/L), the deposition time (x2 in s), the duty cycle (x3 in %), and the voltage (x4 in V). The process responses that should all be maximized are the photocatalytic efficiency of the thin film (De) and three critical charges, which characterize the adhesion failure, i.e., LC1: the load at which the first cracks occurred; LC2: the load at which the film starts to delaminate at the edge level of the scratch track; and LC3: the load when the damage of the film exceeds 50%. This paper compares the robust optimization design of the EPD process using two methods: the robust design of processes and products using the stochastic frontier (RDPP-SF) and the surface response and desirability function methods. The findings show that the RDPP-SF method is superior to the response surface–desirability method for the process responses De and LC2 because of non-natural sources of variation; however, both methods perform comparably well while analyzing the LC1 and LC3 responses, which are subjected to pure random variability. The parameters setting for the process robust optimization are met in run 25 (x1 = 14 g/L, x2 = 150 s, x3 = 50%, and x4 40 V).

Publisher

MDPI AG

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