Development of Application Specific Intelligent Framework for the Optimized Selection of Industrial Grade Magnetic Material

Abstract

This article attempts to introduce a simple and robust way for the classification of soft magnetic material by using multivariate statistics. The six magnetic properties including coercive magnetic field, relative magnetic permeability, electrical resistivity magnetic inductions, i.e., remanence and saturation along with Curie temperature are used for the classification of 16 soft magnetic materials. Descriptive statistics have been used for defining the prioritization order of the mentioned magnetic characteristics with coercive magnetic field and Curie temperature as the most and least important characteristics for classification of soft magnetic material. Moreover, it has also justified the usage of cluster analysis and principal component analysis for classifying the enlisted materials. After descriptive statistics, cluster analysis is used for classification of materials into four groups, i.e., excellent, good, fair and poor while defining the prioritization order of materials on a relative scale. Principal component analysis reveals that the relative permeability is responsible for defining 99.69% of total variance and is also negatively correlated with the coercive magnetic field. Therefore, these two characteristics are considered the responsible factors for categorically placing the enlisted materials into four clusters. Furthermore, principal component analysis also helps in figuring out the fact that a combined influential consequence of relative permeability, coercive magnetic field, electrical resistivity and critical temperature are responsible for defining prioritization ordering of materials within the clusters. The material’s suitability index is identified while making use of adjacency and decision matrices obtained from material assessment graph and relative importance of magnetic properties, respectively. Afterward this material suitability index is used to rank the enlisted materials based on selected attributes. According to the suitability index, the best choice among enlisted soft magnetic materials is Supermalloy, Magnifer 7904 which is present in group 1 labeled as excellent by multivariate analysis. Therefore, the results of graph theory are in accordance with cluster analysis and principal component analysis, thus confirming the potential of this intelligent approach for the selection application specific magnetic materials.

A Multi-Attribute Decision-Making Model for the Selection of Polymer-Based Biomaterial for Orthopedic Industrial Applications

“…It can be seen from Figure 7 that the corresponding factors for PC-1 are either polyethylene characteristics bands or the IR active region induced by fillers i.e., 440 cm −1 , 456 cm −1 , from 900–1200 cm −1 , 1210 cm −1 , 1596 cm −1 . PC-2 unveils the modifications because of irradiation, e.g., gamma irradiations results in an increase of –CH 2 stretching vibrations at 2849 and 2924 cm −1 [ 2 , 6 , 12 , 44 ], it is therefore, these two IR active bands have the positive loadings on PC-2 as shown in Figure 7 . In addition to this, edged Mg–OH stretching vibrations are also quantified by PC-2.…”

“…The diagonal of the 2nd quadrant indicated in Figure 9 shows a decrease in absorbance of filler’s IR active bands (in the region 900–1200 cm −1 ) and increase in absorbance of IR active bands associated to –CH 2 bending vibrations of long chain polyethylene at 1460 and 1470 cm −1 . This points toward the filler’s efficacy as free radical scavenger and enhancing the cross linking within the UHMWPE matrix because it is well established that radiation cross linking results in an increase in absorbance of CH 2 bending vibrations of long chain polyethylene [ 12 ]. The score plot of PCA shown in Figure 8 and Figure 9 confirmed that for the group of irradiated sample, samples irradiated with 65 and 100 kGy are the best ones, and for the group of UHMWPE/Mg-silicate, sample with 2.0% of Mg-silicate and irradiated with 25 kGy of gamma dose is the optimum choice, and UHMWPE/γMWCNTs composites containing 1.0% of γMWCNTs and irradiated with 25 kGy are the better choice among the existing alternatives in the group.…”

“…This is due to the formation of free radicals during the process of irradiation, which are responsible for continuing long-term oxidation reactions thus limiting the service life of UHMWPE. In order to stabilize the radiation-induced free radicals, number of methodologies are adopted including post irradiation annealing, melting, incorporation of bio-compatible of anti-oxidants, i.e., α-tocopherol (vitamin E) [ 9 , 10 , 11 ], irradiating the UHMWPE in the presence of predetermined amount of organo-silane [ 6 , 12 , 13 , 14 , 15 ], using the suitable silane-based clay [ 16 ], and preparing the UHMWPE nano composites with MWCNTs for quenching the radiation-induced free radicals [ 7 , 8 , 17 , 18 ].…”

Simulation of IoT-based Vehicular Ad Hoc Networks (VANETs) for Smart Traffic Management Systems

“…As the traffic density of a network grows, more cars will reach the optimal portion. Therefore, automobiles in the optimal sector rapidly distribute data packets [29,30]. Collisions in MAC layer packet transmission.…”

Optimization of Polyolefin-Bonded Hydroxyapatite Graphite for Sustainable Industrial Applications

“…The free mixing energy [ 15 ] is used as a criterion to establish whether two polymers are compatible with one another. Both entropy and enthalpy may be thought of as kinds of energy that are present in the system.…”