## 3 Research Results
### Experiment 1  
To examine the specific requirements for the implementation of new additive technologies in companies in experiment 1, AM technologies currently used in the enterprise (P9.1i) were taken into account. These are chiefly related to the materials used by the company for production in these technologies (P9.3i). Having assumed this, the company which has shown interest in the adoption of AM technology (node group: P9), also influences the decision on the adoption of AM technology (P10.2i) orientate the goal that the company sees in the use of AM technology (P9.2i). IF node with alternative: A, B, C, D, E, always represent literature knowledge. The most frequently occurring IF helps to achieve a higher chance to implement the AM technology represented by P(P = State1), because the largest pool of knowledge gives a chance for successful implementation. Parents influencing the P node result are the nodes: IF, P9.2 with predecessors. Due to the Pareto principle known as 20:80, where [81] is known that only around 20% of factors have a significant impact on final effect, it was decided that knowledge in the literature about AM technologies influences the practical effect, vis-a-vis use and implementation in production enterprises. This assumption was used to build the rules to fill in CPT in the P output node in each of the experiments. However, it was decided to reduce the disproportion in the weight of the components of the rule, i.e., the \"IF\" node resulting from literature research and the nodes from empirical research. It is therefore claimed that 30% of literature-based knowledge (\"IF\" network) causes 70% of practical results in enterprises (\"AM\" network). In all experiments is always assumed that the more times each element of the network is selected, the more popular and accessible and therefore the more important it becomes, as symbolised by weight. Thus, the CPT for P node was described by Equation (3) which includes only the state of the parents for P node):  
\[\mathrm{P}_{(\mathrm{P=State1})}=\left(0.7*\frac{\sum_{1}^{3}S_{9.2i}*w_{9.2i}} {\sum_{1}^{3}w_{9.2i}}\right)+\left(0.3*\sum_{1}^{5}w_{\mathrm{IF}_{1}}\right), \tag{3}\]  
where:  
\(\mathrm{P}_{(\mathrm{P=State1})}\)--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 \(\epsilon\) N],  
\(w_{9.2i}\)--the weight assigned to the P9.2 nodes i [1, 2, 3: n \(\epsilon\) N],  
\(W_{\mathrm{IFi}}\)--the weight assigned to the alternative i [1,..., 5: n \(\epsilon\) N], of \"IF\" node. Learning, which was conducted for BN is described by measure \(Log(p)\)\(\epsilon\) (\(-\infty\);\(+\infty\)) which represent fitting between the model and the data [79]. Due to the fact, that the \"IF\" network did not change its structure in subsequent experiments, this indicator always remained the same \(=-460\). \(Log(p)\) for the \"AM\" network in experiment 1, was \(-1188\). The networks were connected, after the learning process, by close association and the CPT table for the node \"P\" was determined according to the equation 3. In each test, a _Tornado analysis_ was performed in the GeNiE programme for the resting-state network. This means a state in the network where no nodes were selected for observation. The analysis shows the sensitivity at the target node P:State1 with its positive change on the left side of the vertical axis, and negative change on the right. Figure 3 shows the combinations of nodes affecting the change in the designated probability for P. Assumed parameter spread is 100% of the current value: 0.0767. Through this experiment, areas of demand for AM technologies were sought, in relation to trends in the research literature, due to meet the 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 a level of 7.67% (Table 2). Tornado analysis was performed for goal node where P = State1 for each experiment.