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

• Published in: Inventions (Basel) Vol. 9; no. 2; p. 31
• Main Authors: Rezgui, Mohamed Ali, Trabelsi, Ali, Barbana, Nesrine, Ben Youssef, Adel, Al-Addous, Mohammad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2024
• Subjects: composite central design; Content analysis; Data envelopment analysis; Decision making; Design optimization; Electrophoretic deposition; Fuzzy logic; Genetic algorithms; Linear programming; multi-response problems; Parameter robustness; Principal components analysis; pulsed electrophoretic deposition; Response surface methodology; Robust design; Signal to noise ratio; static robust design; Taguchi method; Taguchi methods; Thin films; Titanium dioxide; titanium dioxide film
• ISSN: 2411-5134; 2411-5134
• DOI: 10.3390/inventions9020031

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).