International Journal of Quality Engineering and Technology
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International Journal of Quality Engineering and Technology (3 papers in press)
Strategies for Failure Prevention in a Gas Motor of Launch Vehicle Control Actuation System by Murugesan V., Sreejith P.S, Sajiv V, Anilkumar A.K Abstract: Hydraulic servo actuators are employed for the thrust vector control of the liquid engines. Vane type gas motor, the prime mover of the system, is a critical subsystem with intricate dynamic elements such as rotor, bearings, vanes and speed regulator. Failure prevention of such critical systems, which is of paramount importance to the success of a launch vehicle mission, depends on making the design robust and ensuring that the critical parameters of the system hardware are within the specified range to give optimal performance. This paper addresses the strategies for achieving the design robustness through severe environmental stress testing and realisation of system hardware complying with quality requirements, which are essential for failure prevention. Towards this, critical environments for the gas motor have been identified and testing extended much beyond the system qualification levels, inducing failures. The test observations and data are analysed critically and design modifications made for broadening the operating environments. The quality systems and procedures for the system hardware are strengthened to assure reliable performance of the gas motor. Keywords: Gas motor; Thermal Regulator; Actuator; Failure prevention; Environments; Design robustness; Quality system; Functional Critical Dimension (FCD); Critical Functional Parameter (CFP).
A Hybrid NSGA-II-DEA Method for the Economic-Statistical Design of the C-Control Charts with Multiple Assignable Causes by Mostafa Zandieh, Amir Hossein Hosseinian, Reza Derakhshani Abstract: In this paper, we reviewed a considerable number of studies on the economic, statistical and economic-statistical design of control charts. The outcome shows that the attribute control charts have not been studied deeply in comparison with the variable control charts. Therefore, in this study, a multi-objective model for the economic-statistical design of the C control charts is first presented. The proposed model considers that multiple assignable causes can occur during the production process. The proposed model is called the C-MOESDWM which to the best of the authors knowledge has not been studied in the literature yet. Then, a hybrid meta-heuristic algorithm is developed to solve the model. The proposed algorithm consists of an improved version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the data envelopment analysis (DEA) which is called the IM-NSGA-II-DEA. For the proposed algorithm, new crossover and mutation operators based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) have been designed. These operators always generate feasible solutions for the C-MOESDWM and give higher opportunities for the best ranked solutions so as to generate offspring for the next generation. After obtaining the non-dominated solutions, the DEA is employed to find the efficient ones. The performance of the IM-NSGA-II is evaluated in comparison with the classical NSGA-II and NRGA. All algorithms are tuned by means of the Taguchi method. The results of numerical experiments imply that the proposed method is superior to other algorithms in terms of objective function values and several well-known multi-objective metrics. Keywords: Economic-statistical design; Meta-heuristics; DEA; Multiple assignable causes.
Quality Insight: Data Quality is a Requirement for Product Quality by Adedeji Badiru Abstract: In business and industry, the focus is often solely on product quality. This is okay, provided we recognize the multitude of other factors that can imping on product quality. The premise of this article is that product quality is a function of data quality. As such, data quality is a requirement for product quality. This article presently a combination of the qualitative and quantitative framework for enhancing product quality on the basis of assessing the quality of the input data. Keywords: Data; Data Quality; Product Quality,.