Optimal performance of plastic pipes’ extrusion process using Min-Max model in fuzzy goal programming

Abstract

The main objective of this research is to optimize performance of plastic pipes' extrusion process with two main quality responses, including pipe's diameter and thickness, using Min-Max model in fuzzy goal programming. First, the variables control charts are constructed at initial factor settings of extrusion process, where the results reveal that the extrusion process is in statistical control. However, the actual capability index values for diameter and thickness are estimated 0.7094 and 0.7968, respectively. The process capability for a complete product, MC pk , is calculated as 0.752. These values indicate that the extrusion process is incapable. To improve process performance, the L 18 array is utilized for experimental design with three 3-level process factors. Then, the Min-Max model is used to determine the combination of optimal factor settings. The estimated capability index values for diameter and thickness at the combination of optimal factor settings are estimated and found to be 1.504 and 1.879, respectively. The integrated process capability index, MC pk , is calculated as 1.681. Confirmation experiments should that the Min-Max model results in enhancing process capability for both responses. In conclusions, the Min-Max Model may provide valuable assistance to practitioners in optimizing performance while considering both product and process preferences.

A genetic-fuzzy procedure for solving fuzzy multiresponses problem

“…Recently, process engineers should determine the optimal combination of process factor settings of a manufacturing process to enhance multiple quality characteristics of products simultaneously. Therefore, various optimization techniques were proposed in literature to deal with multiresponses problem in the Taguchi method; including the Taguchi methodology and neuro-fuzzy based model [7][8][9], genetic algorithm [10][11][12], grey-fuzzy logic Chiang [13], response surface methodology and Taguchi's technique [14], comparisons of efficiency between different systems technique in data envelopment analysis [15], fuzzy goal programming approach [16], Taguchi-based grey relational analysis [17][18][19], Taguchi methods, neural networks, desirability function, and genetic algorithms [20], particle swarm optimization [21], regression and neural network [22], neural networks and Taguchi method [23], Taguchi technique and upper bound technique [24], fuzzy neural network approach [25], Min-Max model in fuzzy goal programming [26], fuzzy goal programming-regression approach [27], multiple pentagon fuzzy responses [28], non-dominated sorting genetic algorithm II [29]. Nevertheless, most of these approached are deterministic optimization, which were carried out without considering the uncertainty due to measurement and process variations; therefore, the optimal solution will be sensitive to variations of input and process parameters.…”

“…To solve this issue, the optimal factor settings for each replicate that are obtained by using GA can be further processed to determine a fuzzy combination of optimal process factor settings for each quality characteristic and/or multiple quality characteristics. An appropriate technique to achieve this objective is the fuzzy goal programming (FGP) technique, which was widely used in optimizing performance for several business applications [26][27][28]. The FGP utilizes the fuzzy regression models, pay-off matrices, and desirability function to transform multiple objectives into a single equivalent objective function with the consideration of overall desirability.…”

Optimal Performance of Caps’ Pressing Process Using Taguchi-Grey Method

“…In reality, products are manufactured with multiple built in quality characteristics of main customer interest (Al-Refaie et al, 2009;Al-Refaie, 2012). Several optimization techniques were employed in previous studies to optimize process performance (Al-Refaie, 2015;Al-Refaie et al, 2016;Al-Refaie, 2017). Among them, the grey relational analysis based on the grey system theory (Deng, 1982;Tsao, 2009) can be utilized for solving complicated interrelationships among multiple quality responses (Deng, 1989;Al-Refaie, 2010).…”

Using mixed goal programming to optimize performance of extrusion process for multiple responses of irrigation pipes

“…23 studied the parameters of the plastic injection molding process by using the Taguchi method to optimize a brake booster valve body. Al-Refaie 24 optimized performance of extrusion process of plastic pipes using Min-Max model in fuzzy goal programming (FGP).…”