## 3 Research Results
### Experiment 2  
Experiment 2 aimed to examine the impact of AM technologies planned to be introduced in the company (10.1i) relative to \"IF\" node. The parents influencing the P result are the nodes of the \"IF\" and P10.1. The important relations between the \"IF\" parts of the network and \"AM\" is justified just like in experiment 1, and is 30% (\"IF\" network) to 70% (\"AM\" network). There, the CPT for P node is described by Equation (4).  
\[\mathrm{P}_{(\mathrm{P=State1})}=\left(0.7*\frac{\sum_{1}^{3}S_{10.1i}*w_{10.1i} }{\sum_{1}^{3}w_{10.1i}}\right)+\left(0.3*\frac{5}{1}w_{1\mathrm{F}_{1i}} \right), \tag{4}\]  
where:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test No.**} & \multirow{2}{*}{**Observed Alternatives of Nodes**} & \multirow{2}{*}{**Target P (P = State1)**} & \multirow{2}{*}{**Test No.**} & \multirow{2}{*}{**Observed Alternatives of Nodes**} & **Target P (P = State1)** \\ \cline{5-6}  & & & 10 & 9.2.1 = 0; 9.2.2 = 1; IF = E & 0.01 \\ \hline
1 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 1 & 0.05 & 12 & 9.2.1 = 0; 9.2.2 = 1; IF = B & 0.07 \\ \hline
2 & 9.2.1 = 0; 9.2.2 = 1 & 0.09 & 13 & 9.2.1 = 0; 9.2.2 = 1; IF = C & 0.11 \\ \hline
3 & 9.2.2 = 1 & 0.09 & 14 & 9.2.1 = 0; 9.2.2 = 1; IF = D & 0.03 \\ \hline
4 & 9.2.1 = 1 & 0.08 & 15 & 9.2.1 = 0; 9.2.2 = 1; IF = E & 0.01 \\ \hline
5 & 9.2.2 = 1; 9.2.3 = 0 & 0.13 & 16 & 9.2.1 = 1; IF = A & 0.19 \\ \hline
6 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 0; IF = A & 0.48 & 17 & 9.2.1 = 1; IF = B & 0.07 \\ \hline
7 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 1; IF = B & 0.07 & 18 & 9.2.1 = 1; IF = C & 0.11 \\ \hline
8 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 1; IF = C & 0.11 & 19 & 9.2.1 = 0; IF = D & 0.03 \\ \hline
9 & 9.2.1 = 0; 9.2.2 = 1; 9.2.3 = 1; IF = D & 0.03 & 20 & 9.2.1 = 0; IF = E & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the tests for experiment 1, estimated by the Bayesian Network.  
Figure 3: The Tornado analysis for node P = State1 in the experiment 1.  
P\({}_{(P=\text{state1})}\)--the probability of the occurrence of State 1 of \"P\" node, \(S_{10.11}\)--the alternative state in 10.1i nodes (State 1/0), where i [1, 2, 3: n \(\in\) N], \(W_{10.11}\)--the weight assigned to the 10.1 nodes i [1, 2, 3: n \(\in\) N], \(W_{\text{IFi}}\)--the weight assigned to the alternative i [1,..., 5: n \(\in\) N], of \"IF\" node.  
The learning was conducted for the \"AM\" network, where \(Log(p)\), in experiment 2, was \(-117\). The networks after the learning process were connected by relations and the CPT table for the node P was determined according to the equation 4. The effect of the P: State1, when the resting-state network is 0.0739.  
Through experiment 2, AM technologies planned to be introduced in the company were sought in relation to trends in the research literature, in order to meet the needs of companies to implement AM technology. The modelled network, after learning, indicates the possibility of implementing AM technology at the level of 7.74% (Table 3). Based on the Tornado analysis, it is believed that the greatest impact on positive change P(P = State1) is the implementation of DMLS technology in the enterprise (P10.1). AM technologies, with the accessible studies, i.e., with the almost lowest IF (in the range 2 \(<\) B \(\leq\) 3), give the most promising possibilities for implementing AM technologies.