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Jenab A.,Sharif University of Technology | Karimi Taheri A.,Sharif University of Technology | Jenab K.,Society of Reliability Engineering
Journal of Materials Engineering and Performance | Year: 2013

In this study, artificial neural network (ANN) was used to model the hot deformation behavior of 7075 aluminum alloy during compression test, in the strain rate range of 0.0003-1 s-1 and temperature range of 200-450 C. The inputs of the model were temperature, strain rate, and strain, while the output of the model was the flow stress. The feed-forward back-propagation network with two hidden layers was built and successfully trained at different deformation domains by Levenberg-Marquardt training algorithm. Comparative analysis of the results obtained from the hyperbolic sine, the power law constitutive equations, and the ANN shows that the newly developed ANN model has a better performance in predicting the hot deformation behavior of 7075 aluminum alloy. © 2012 ASM International. Source

Foumania M.,Islamic Azad University at Qazvin | Jenabb K.,Society of Reliability Engineering
International Journal of Computer Integrated Manufacturing | Year: 2013

In this paper, we study the robotic cell scheduling problem with two machines and identical parts. It is assumed that there are flexibility in the robot type, the pickup category, and the cell layout. Also, the objective is to find the robot move sequence that minimizes the cycle time. One of the assumptions in the pertaining literature is that the occupied robot cannot load a part into an occupied machine. In contrast, the authors consider the robot has the swap ability, so the busy robot will be able to load concurrently a part into an occupied machine. It is also presumed that each part can entirely be processed by one computer numerical control (CNC) machine. A wide range of m-unit cycles named pure cycles has been well documented in the literature. However, a new class of the pure cycles which can produce less than m parts is defined in this study. We prove that the improved pure cycles always dominate pure cycles. The performance of these improved pure cycles for linearly and circularly configured robotic cells, based on free-pickup and no-wait categories are examined. Also, the number of machines which should be idle or busy to obtain an optimal cycle is determined. The novelty of this research is the introduction of the improved pure cycles that increase the long run average throughput rate and also reduce the number of machines. Copyright © 2013 Taylor and Francis. Source

Jenab K.,Society of Reliability Engineering | Seyedhosseini S.M.,IUST | Khoury S.,East Carolina University | Sarfaraz A.,California State University, Northridge
Clean Technologies and Environmental Policy | Year: 2012

While low air quality may have negative effect on product quality in manufacturing, it has become a social concern as there are many reports on the result of worker exposure to low air quality. Manufacturing experienced a boom increase after World War I and II due to higher demands for products that gave birth to an unhealthy environment for workers. For example, Epidemiological investigations have linked unhealthy environment (air pollution) to adverse health effects such as respiratory diseases, and increased mortality and morbidity. These manufacturing systems represented less than 14% of the private employment and accounted for 42% of the nonfatal workplace illnesses. It is evident that manufacturing systems still have significant impact on the health of workers. Therefore, this study proposes a fuzzy Bayesian air quality monitoring model that is able to mimic human-like intelligent behavior in the environmental analysis. An illustrative example is demonstrated to present the application of the model. © US Government 2012. Source

Jenab K.,Society of Reliability Engineering | Rashidi K.,Ryerson University | Moslehpour S.,University of Hartford
International Journal of Enterprise Information Systems | Year: 2013

This paper reports a newly developed Condition-Based Maintenance (CBM) model based on Artificial Neural Networks (ANNs) which takes into account a feature (e.g., vibration signals) from a machine to classify the condition into normal or abnormal. The model can reduce equipment downtime, production loss, and maintenance cost based on a change in equipment condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, debris content, and volume of material). The model can effectively determine the maintenance/service time that leads to a low maintenance cost in comparison to other types of maintenance strategy. Neural Networks tool (NNTool) in Matlab is used to apply the model and an illustrative example is discussed. Copyright © 2013, IGI Global. Source

Samarrokhi A.,Multi Media University | Jenab K.,Society of Reliability Engineering | Arumugam V.C.,Multi Media University | Weinsier P.D.,Bowling Green State University
International Journal of Industrial and Systems Engineering | Year: 2015

As researchers and practitioners alike have focused on the importance of sustainability of competitive advantage, this study examined whether or not various operations strategies - low-cost leadership, quality differentiation, innovation differentiation and service differentiation - have serious effects on sustainability of competitive advantage, and whether suitable resources can improve the effects as a moderator. This study was an exploratory descriptive project with primary data collected from 80 Malaysian manufacturing companies. Results were analysed using bivariate correlation and multiple regressions. The findings showed that suitable resources cannot be considered as a strong moderator for the effects of low-cost leadership, quality differentiation and innovation differentiation on sustainable competitive advantage (SCA). On the other hand, suitable resources leveraged the impact of service differentiation on SCA. Given that no studies on this point were found in the literature, the findings from this study should bring to light several valuable points for scholars and manufacturers. © 2015 Inderscience Enterprises Ltd. Source

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