Decision support systems in the metal casting industry 2

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Decision support systems in the metal casting industry 2

Analysis and finding

According to the proposed framework as shown in figure 2 above, we classified the identified articles in terms of the application across various stages of a metal casting, the decision support and intelligent system to each specific area, to the year of publication and the type of journal. Table 1 represents the distribution 89 articles.

Distribution by the application area

Table 2 represents the total number of articles found ‘based on a number of impressions,’ ‘based on the material poured’, and ‘application towards the pressure’ are as follows 7, 19, and 63 respectively.

In these, the articles are highly concentrated on ‘high pressure die casting’ and ‘Squeeze casting’ with 20 and 16 articles. In high pressure die casting most of the articles focused on how casting is done at high pressure using metallic alloys such Ca, Al, Mg, etc.

Table 2 Description of the decision and intelligent system

Distribution by the system type

Figure 3 represents the distribution of the articles by the system type. It is found from the below graph that ANN is most commonly used, and it is about 27 out of 89 selected articles whereas genetic algorithm is found to be 18.

The  ANN is used in various types in ‘number of impression’, ‘material poured’ and ‘applications that are applied towards the pressure’ which comprises about 8% of the selected articles. In this, the articles make use of the GA in order to address the issues that related to metallic chemical alloy casting.

Fig.3. Distribution of decision support and intelligent systems

Distribution by the year of publication

In this figure 4 represents the distribution of the articles between the years 2000-2015. The average number of papers published in each year is around 4.8. Overall, it is seen that atleast one paper was published each year is with the highest rate of 11, 10, 9, 8 and 7 in the years 2014, 2000, 2007, 2008, and 2015 respectively.

This indicates that decision support system in metal casting shows a gradual increase over the 2014. This shows that there is an increase in attention towards the casting and academics.

Fig. 4 Distribution of articles by year

Discussion and conclusion

In this proposed framework the application of the decision and intelligent support system in metal casting and it identifies the gaps from the extent literature review which provides in turn an opportunity in future to study  more about metal/die to cast.

From the literature review it is found that most of the study are concentrated on GA and ANN very few have conducted by the authorsZhang et al., 2007b, Sun et al., 2011, Jin & Kang, 2012, Wen et al., 2012, Kuo et al., 2013 concentrated towards DSS.

On the other hand, few authors have concentrated on KBS, ES, fuzzy and hybrid. In addition, it is found that largest journal of materials processing technology and materials science and engineering: A has the highest collection of journal articles and in this the collected articles are based on theonly casting of metals through metallic alloys.

In future, more research can be conducted based on KBS, ES, Hybrid system by considering metal casting in different journals and adapting a large number of metallic alloys.