## 3 Research Results
### Experiment 3a  
Experiment 3a is an extension of experiment 1. In this research, the possibility of implementing AM (P node) technology is examined in addition to the impact of key industry determinants that could influence the decision to adopt AM technology in the company (10.3i), relative to \"IF\" nodes. The parents influencing the P node result are the nodes of the \"IF\", P9.2 with 9.1, 9.3, and P10.3.  
The validity relationships between the \"IF\" part of the network and the \"AM\" part, again refer to the division according to the Pareto principle. This time, due to 3 components in sum, it was decided to share 40% of the weight for P9.2 and P10.3 nodes belonging to the \"AM\" network and 20% of the weight for the IF node. The CPT for P node is described by Equation (5):  
\[\text{P}_{(P=\text{State1})}=\left(0.4*\frac{\sum_{1}^{3}S_{9.2i}*w_{9.2i}}{\sum _{1}^{3}w_{9.2i}}\right)+\left(0.4*\frac{\sum_{1}^{7}S_{10.3i}*w_{10.3i}}{\sum _{1}^{7}w_{10.3i}}\right)+\left(0.2*\sum_{1}^{5}w_{\text{IFi}}\right), \tag{5}\]  
where:  
P\({}_{(P=\text{State1})}\)--the probability of the occurrence of State 1 of \"P\" node, \(S_{9.2i}\)--the alternative state in 9.2i nodes (State 1/0), where i [1, 2, 3: n \(\in\) N], \(S_{10.3i}\)--the alternative state in 10.3i nodes (State 1/0), where i [1,..., 7: n \(\in\) N], \(W_{9.2i}\)--the weight assigned to the 10.1 nodes i [1, 2, 3: n \(\in\) N], \(W_{10.3i}\)--the weight assigned to the 10.3 nodes i [1,..., 7: n \(\in\) N], \(W_{\text{IFi}}\)--the weight assigned to the alternative i [1,..., 5: n \(\in\) N], of \"IF\" node.  
In experiment 3a, there was no need to learn the \"AM\" and \"IF\" fragments of the network because they consisted of previously developed parts, which were connected by relations. Occurrence P:State1, when network the resting-state network is the value 0.0425.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test No.** & **Observed Alternatives of Nodes** & **Target P (P = State1)** \\ \hline
0 & - & 0.07 \\ \hline
1 & P10.1 EBM = 0; 10.1 DMLS = 1; P10.1 Welding = 0; IF = A & 0.04 \\ \hline
2 & P10.1 EBM = 0; 10.1 DMLS = 1; P10.1 Welding = 0; IF = B & 0.07 \\ \hline
3 & P10.1 EBM = 0; 10.1 DMLS = 1; P10.1 Welding = 0; IF = C & 0.11 \\ \hline
4 & P10.1 EBM = 0; 10.1 DMLS = 1; P10.1 Welding = 0; IF = D & 0.03 \\ \hline
5 & P10.1 EBM = 0; 10.1 DMLS = 1; P10.1 Welding = 0; IF = E & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the tests for experiment 2, estimated by the Bayesian network.  
Through experiment 3a, AM technologies planned to be introduced into the company were sought in addition the impact of key industry determinants that could influence the decision to adopt AM technology in the company, with relation to trends in literature research, due to needs of companies in the context of the implementation of AM technology. The modelled network, after learning, indicates the possibility of implementing AM technology at the level of 3.91% (Table 4). Based on the Tornado analysis, it is believed that the greatest impact on a positive change P(P = State1) is to choose the goal: production (P9.2).  
In experiment 3a, which was based on experiment 1, tests were also conducted. Maximum result was at Test 21 = 15% (Table 4), where the enterprises declared as their goal, using AM: production and revitalisation at the knowledge level IF = A, where adding implication motives to analysis (P10.3i) increases the success of the implementation of AM technology.