Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

results obtained through the graph theory approach can easily be modeled through the collaborative practice through artificial neural network methodology which is inspired from the AI. The artificial neural network (ANN) can be defined as an interconnected group of nodes, similar to the vast network of neurons in a human brain. Duan and Yeh [27] have practiced and explored artificial intelligence-based decision-making. They have implemented the said approach to make the decision for accounting choice evaluation and selection through an intelligent system-based methodology. The basic illustration of an ANN model has been represented in Figure 4.

decision-making methods such as fuzzy ELECTRE, fuzzy GTMA, and interval

Decision-Making in Real-Life Industrial Environment through Graph Theory Approach

Singh et al. [10] have utilized the graph theory-based matrix method for the study of machinability of commercially pure titanium. In general, the single and multiple response optimization of any machining processes gives a different shape to the problem to elaborate it in the most better way and further makes the system more reliable and productive [29, 30]. They further said that any type of processing method is well subjective by the machinability of the work material under study. They have proposed a GTMM-based practice for the valuation of machinability of

Identification of numerous process attributes along with their relative prominence was undertaken and analyzed by mounting a mathematical function by engaging GTMM. Furthermore, an attribute digraph was also established, which has provided them with a visual image of reflected attributes with their relative connections. The developed digraph was further embodied by using matrix relation. A permanent machinability index for all the investigational runs was also attained from matrix form demonstration built on attribute digraph. The blend of all the attributes for any processing approach has made the proposed method quite versatile. The results have revealed that an experimental run having the combination consisting tool material of titanium, grit size of 500, and a power supply of 300 W

The application and the capability of artificial intelligence-inspired fuzzy logicbased decision-making have been discussed. The graph theory-based decisionmaking method has also been explored to employ in practical industrial situations. The following major inferences can be drawn from the proposed chapter. These are:

• The discussed methodology, namely, graph theory, is capable enough to handle the versatile real-life situation as this method includes the several input factors

• The graph theory and matrix methods consist of the digraph representation, the matrix representation, and the mathematical representation, i.e., a permanent function. The digraph is the visual representation of the variables

• From the domain of AI, there are some observed major practices, namely, artificial neural networks, evolutionary computing, fuzzy logic, probabilistic analysis models, intelligent agents, etc., which usually define the basic artificial

• The artificial intelligence-inspired logics and practices can make the traditional decision-making more effective and versatile too. The fuzzy logic-based decision-making has emerged as one of the basic collaborative exercise conducted to offer viable solutions to any domain of real-life practical

problems. The computation involved in these methods is simple, effective, and

moreover quite friendly for the decision-makers.

3.8 Machinability study of commercial pure titanium

titanium workpiece in ultrasonic drilling.

yielded optimized results for machinability.

and their sub-factors too.

and their interdependencies.

intelligence system.

129

4. Conclusions

GTMA as a future direction.

DOI: http://dx.doi.org/10.5772/intechopen.82011

### 3.7 Selection of appropriate equipment for industrial purpose

Safari et al. [28] have explained out that proper equipment selection is a very important activity for manufacturing systems due to the fact that improper equipment selection can negatively affect the overall performance and productivity of a manufacturing system. They have further implemented a two-step fuzzy-analytical hierarchy process (AHP) and graph theory matrix approach (GTMA) methodology, i.e., GTMA uses fuzzy-AHP result weights as input weights.

They have presented a real-life study to reflect the applicability and performance of the proposed methodology. It was concluded that using linguistic variables, the evaluation process can become more realistic. The usage of fuzzy-AHP weights in GTMA has made the application more realistic and reliable. The proposed model was only implemented on an equipment selection problem in the company. They have further suggested the possibilities to employ other

Figure 4. Illustration of an artificial neural network (ANN) model.

decision-making methods such as fuzzy ELECTRE, fuzzy GTMA, and interval GTMA as a future direction.
