# Use of Data-Driven Simulation Modeling and Visual Computing Methods for Workplace Evaluation
## 2 Problem Description
### Visual Computing  
The visualization in manufacturing systems is done mostly with two-dimensional models and simplified three-dimensional representations. Visual computing methods represent a wide field of acquisition, analysis, and synthesis of visual acquisition data, with the help of computers equipped with suitable hardware and software tools [1]. The main advantage of using visual data processing methods is that different experts within the group managing the manufacturing process can understand and use the results of the collected data [2].  
The presented research proposes the use of economically accessible commercially used spherical cameras to capture real-world-object three-dimensional characteristics in the manufacturing system. The hardware ensures easy handling, time-efficient preparation and acquisition of the equipment, satisfactory accuracy and a high degree of application possibilities. From the captured three-dimensional object, data of the point cloud representing the evaluated object can be obtained, and realistic data describing the time and value criteria of the manufacturing system can be obtained on the basis of the captured video. Based on the captured data, we can adjust, modify, and evaluate the system in detail, and the captured data can be hybridized with the help of other tools that ensure high usability of the presented method [5]. The data acquisition with a spherical camera is performed with the proposed three-step approach.  
* Preparation of a scanned object, which includes the layout of all components of the workplace and the associated subassemblies. Adequate fixed installation of a spherical camera with respect to the angle of data acquisition and the illumination of the object requires detailed considerations, since improper placement can significantly affect the quality of the acquired data [22].
* When scanning the object using a spherical camera, we can use multiple static images or video to capture the data. Capturing an object using static images allows us to create a data point cloud, and video allows us to evaluate the changing parameters of the observed object further in relation to dynamics changing inside the manufacturing system.
* Processing the captured data in the primary phase includes filtering unnecessary components of the static image capture and selecting data in video files.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Current State Workplace** & **Desired Workplace** \\  & **Characteristics** & **Characteristics** \\ \hline Assembled products per shift & 1250 & 1680 \\ Assembly operation process time (s) & 30 & 25.5 \\ Assembly workplace costs (\(\ell\)/h) & 17.8 & minimal \\ Reassembly operation (\%) & 8 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Current and desired workplace characteristics.  
Figure 1 shows the proposed two-level architecture model, which highlights two aspects in the context of Industry 4.0: the human factor and the product factor in relation to production. Visual computing methods are becoming increasingly important in the study of the human factor, especially when it comes to collaborative workplace design from a security perspective. The effects of a correct workplace organization and an ergonomic analysis of the workplace environment allow for sustainable efficiency and long-term success of the work process, based on the appropriate level of knowledge and skills of the employees. The competitiveness of small and medium-sized enterprises depends, in most cases, on the ability to adapt to customer needs. High order dynamics and the rapid development of new personalized products require a high degree of flexibility of the production system. Visual computing methods allow for detailed analyses to be carried out, which, if evaluated appropriately, contribute to a higher degree of manufacturing systems' optimization. The bold marked text in Figure 1 presents the Industry 4.0 challenges addressed in the presented work.