The ultimate goal of the online quality control initiatives is to prevent the occurrence of defects or rather to detect the occurrence of defects as fast as possible in order to interfere with corrective actions. Generally, Statistical Process Control (SPC) tools such as control charts can be effectively applied to monitor the production processes and can be used as an indicator for the occurrence of assignable causes in the process which are mainly responsible for the occurrence of defects. Typically, the out of control signals resulted from control charts or other signs of developing defective items that are observed directly through visual inspection necessitate a rapid response from the shop floor operators. Meanwhile, the lack of worker empowerment and poor communication between shop floor workers and engineers as well as managers are mainly responsible for the late decisions and actions which lead to increased defect rates. This paper presents a framework that integrates SPC approaches with worker empowerment practices to enhance the responsiveness toward detected defects as well as preventing the occurrence of more ones. Besides, a case study concerned with the manufacturing of Unplasticized Polyvinyl Chloride (UPVC) pipes has been conducted to demonstrate the application of the proposed framework.
Design for Six Sigma (DFSS) is a proactive approach that aims at designing in quality during the early stages of product or process development. In this paper, the DMADV (Define-Measure-Analyze-Design-Verify) methodology has been applied to an industrial process. The considered application involves cutting tubes to predefined lengths in order to be supplied to custom-made automotive exhaust tube manufacturers. Investigating the process reveals several problems that adversely affect the production. Accordingly, customer requirements have been identified and the Quality Function Deployment (QFD) has been implemented to translate these requirements into technical characteristics. A conceptual design of a compound mechanism for both feeding and cutting has been suggested to overcome the drawbacks in the current practice. A detailed 3D CAD model has been developed, in addition to a prototype that has been manufactured to test the validity of the proposed design. Pilot runs of the prototype reveal that the developed mechanism not only performs its intended task adequately, but also the chances of cutting wrong tube lengths have been eliminated.
In today's competitive manufacturing environment, the challenge is to responsively produce products with minimum cost and high quality. Achieving and controlling the targeted quality level in manufacturing processes does not only increase customer satisfaction, but it can also result in significant cost and time savings. Further, measuring the process performance is a critical issue in process improvement initiatives. The common practice in several industries is using the Univariate Process Capability Indices (UPCIs) to measure the process performance, which are based on only a single quality characteristic. In most of the applications, it is not acceptable to judge the performance based on a single quality characteristic as it actually relies on more than one characteristic. In this paper, univariate and multivariate PCIs are used to measure the performance of the flare making process. This process is a critical step in the straight fluorescent light bulb production line. In addition, multivariate control charts such as the Hotelling ܶ ଶ as well as the Multivariate Exponentially Weighted Moving Average (MEWMA) are constructed for the collected data to verify that the process is in control before assessing its capability. Besides, Principal Component Analysis (PCA) and Joint Normal Distribution (JND) techniques are applied in the multivariate process capability assessment. In this paper, Multivariate Process Capability Indices (MPCIs) have been evaluated to compare the process performance before and after improvement efforts. In the considered case study, MPCIs provide the user with an overall assessment of process capability regardless of the fluctuations in the individual variables capabilities.
Wave energy is one of the most promising clean and renewable sources of energy, particularly in coastal countries. Both of governmental and researchers interests in this field have resulted in developing several designs for wave energy converters (WECs). Generally, a WEC is a device that converts the kinetic and potential energy associated with wave motion into a beneficial mechanical or electrical energy. Typically, those developed WECs have different working principles, cost of installation, maintenance requirements as well as different impacts on environment. Considering the selection of a particular converter to be implemented in a definite scenario, it is critical to have a model to support the decision making process. In this paper, a multi-criteria decision making model is developed for this purpose. The provided model is capable of ranking the most popular WECs such as Point Absorber Converters, Attenuator Converters, Terminator Converters, Multi-Degrees of Freedom (MDF) Converters, Floating Oscillating Water Column (OWC) Converters, Fixed OWC Converters, Floating Overtopping Converters, and Fixed Overtopping Converters. In this context, the uncertainties associated with vague data and reliance on linguistic assessment in rating alternatives with respect to different criteria, it is essential to consider using the fuzzy approach in model development. Accordingly, this research relies on employing fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The developed fuzzy TOPSIS decision making model accounts for different assessment criteria and evaluates different design alternatives against each criterion and accordingly provides the decision maker with an overall ranking for the different considered WECs.
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