Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

Abstract

ABSTRACT:One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers' viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

Robust Optimal Placement and Sensitivity Analysis of Multiple Transmitting Antennas Under Uncertainty: A New Surrogate-Based Evolutionary Algorithm

“…Most of the standardized residuals should be within the interval −3 ≤ 𝐷 𝑠 ≤ 3, and any observation outside of this interval (outlier) is potentially unacceptable with respect to its observed simulation output [36], [37].…”

“…The surrogate model constructed in Step 5 has to be validated to ensure that its predictive power is sufficient for design optimization purposes. Here, validation is executed using the leave-one-out cross validation (𝑘 = 1) [36], [37], which works as follows. First delete the 𝑠 𝑡ℎ input combination and the relevant output from the complete set of the 𝑙th combination (𝑠 = 1,2, … , 𝑙), i.e., to avoid the extrapolation by Kriging, we avoid dropping the sample points in the margin.…”

A New Simulation-based Robust Optimal Design of FOPID Controller for Five Bar Linkage Robot in a Cyber-physical System

“…These optimization problems include dynamic and stochastic control system design, sub-communities in machine learning problems, discrete event systems (e.g. queues, operations, and networks), manufacturing, medicine and biology, engineering, computer science, electronics, transportation, and logistics, see [3], [5], [36], [38]- [40]. However, several studies have systematically illustrated the applications of surrogate-based optimization algorithms [36], [37], [41]- [43].…”

“…exponential, Gaussian, linear, spherical, cubic, and spline), see [56], [57]. Today, GP surrogate has been used as a widespread global approximation technique that is applied widely in control engineering design problems [38], [58].…”