Showing 3 results for Bakhshi
S. Rastegari, Z. Salehpour , Bakhshi , H. Arabi,
Volume 19, Issue 5 (IJES 2008)
Abstract
Formation mechanism of silicon modified aluminide coating applied on a nickel base super alloy IN-738 LC by pack cementation process was the subject of investigation in this research. Study of the microstructure and compositions of the coating was carried out, using optical and scanning electron microscopes, EDS and X-ray diffraction (XRD) techniques. The results showed that due to low partial pressure of silicon halide in Pack process, the amount of soluble silicon in the coating can not exceed 1 wt % of the total coating composition, although the Si content of the particles present within the outer coating sub-layer could reach as far as 5 wt%. Thus, the small amount of soluble Si within the coating could not provide the necessary conditions for formation of any intermetallic and it seems that the formation and growth behavior of various sub-layers in Si-modified aluminide coating is similar to that of simple aluminide coating. Three sub-layers were detected in the coating structure after being subjected to diffusion heat treatment. They were an outer Ni-rich NiAl sub-layer a middle Ni-rich NiAl and an inter diffusion sub-layers. The details of formations and growth mechanism of these sub-layers has been discussed in this research.
Ali Vaysi, Abbas Rohani, Mohammad Tabasizadeh, Rasool Khodabakhshian, Farhad Kolahan,
Volume 29, Issue 3 (IJIEPR 2018)
Abstract
Nowadays, the CNC machining industry uses FMEA approach to improve performance, reduce component failure, and downtime of the machines. FMEA method is one of the most useful approach for the maintenance scheduling and consequently improvement of the reliability. This paper presents an approach to prioritize and assessment the failures of electrical and control components of CNC lathe machine. In this method, the electrical and control components were analyzed independently for every failure mode according to RPN. The results showed that the conventional method by means of a weighted average, generates different RPN values for the subsystems subjected to the study. The best result for Fuzzy FMEA obtained for the 10-scale and centroid defuzzification method. The Fuzzy FMEA sensitivity analysis showed that the subsystem risk level is dependent on O, S, and D indices, respectively. The result of the risk clustering showed that the failure modes can be clustered into three risk groups and a similar maintenance policy can be adopted for all failure modes placed in a cluster. Also, The prioritization of risks could also help the maintenance team to choose corrective actions consciously. In conclusion, the Fuzzy FMEA method was found to be suitably adopted in the CNC machining industry. Finally, this method helped to increase the level of confidence on CNC lathe machine.
Elaheh Bakhshizadeh, Hossein Aliasghari, Rassoul Noorossana, Rouzbeh Ghousi,
Volume 33, Issue 1 (IJIEPR 2022)
Abstract
Organizations have used Customer Lifetime Value (CLV) as an appropriate pattern to classify their customers. Data mining techniques have enabled organizations to analyze their customers’ behaviors more quantitatively. This research has been carried out to cluster customers based on factors of CLV model including length, recency, frequency, and monetary (LRFM) through data mining. Based on LRFM, transaction data of 1865 customers in a software company has been analyzed through Crisp-DM method and the research roadmap. Four CLV factors have been developed based on feature selection algorithm. They also have been prepared for clustering using quintile method. To determine the optimum number of clusters, silhouette and SSE indexes have been evaluated. Additionally, k-means algorithm has been applied to cluster the customers. Then, CLV amounts have been evaluated and the clusters have been ranked. The results show that customers have been clustered in 4 groups namely high value loyal customers, uncertain lost customers, uncertain new customers, and high consumption cost customers. The first cluster customers with the highest number and the highest CLV are the most valuable customers and the fourth, third, and second cluster customers are in the second, third, and fourth positions respectively. The attributes of customers in each cluster have been analyzed and the marketing strategies have been proposed for each group.