## 2 Real Time Data Technologies for SMEs
### Automatic Quality Control  
Increasing in popularity are optical methods to observe displacements, velocities, and strains of surfaces. One of the biggest advantages of these methods is the ability to implement non-contact methods and receive information instantly [10]. This is the key factor for our method. Information could be transferred immediately; thus, the response would be much faster. The laboratory of composite materials and adaptive structures presented optical devices to measure complex structures. They are an accurate, reliable, and easy-to-usedevice that examines the deformation behavior of complex three-dimensional surfaces and are made from innovative materials. Figure 1 shows the principle of the device--the light is split into two coherent beams. One is called \"reference\", whereby optical fiber is wrapped around a piezo tube, which can be controlled to induce phase shifting. The second beam enters a switch box to modify the ESPI (electronic speckle pattern interferometry) configuration [11]. These two light fields interfere, and the resulting light field has random amplitude, phase, and intensity; thus, is a speckle pattern. If the object is deformed, the distance between the object and the image will change; hence, the phase of the image speckle pattern will change. Any inadequacy of such a chain will provide an automatic response for the whole process [12]. Many examples could be found of how optical checks are used in production. To perform high-quality control only visual human inspection is not enough since it relies on the experience of the inspector [13]. Furthermore, automation cannot be reached with such checks. Indeed, Ref. [14] presents how a novel visual sensing system for keyhole TIG is designed. The deep learning framework allowed the system to sort welding as good, incomplete, misaligned, undercut, or burnt through from just an image, taken with a camera, and the system could decide the quality of the welding. The received accuracy was not lower than 0.985. Combining two main subsystems--image acquisition and image processing--an automatic visual inspection (AVI) system could be implemented. To implement such a system, some overall configuration must be followed. A lightning system generates light in a specific manner to illuminate the object and receive better-quality images. This illuminated scene is projected onto the sensor of a digital camera to create a digital image. This image is sent to a processing platform that controls all inspection processes. The processing platform analyzes and processes the acquired images to give the final inspection information. These are the basic descriptions of an AVI system, which also incorporates multiple software solutions to control different elements of the system (cameras and lighting system) [15]. Such automatic defect detections would lead to further automatization--automatic production order release for rejected quantities. Since the camera would not only be able to detect scratches, surface imperfections, and oiled surfaces but also categorize the part that has the inspection failure; thus, the system could automatically release production orders for the specific operation--surface cleaning or grinding. Further, having this data in the system allows the statistics of rejected parts to be collected and improvements to be made based on the information of which operation has failed the most. Here, neural networks would be used. To detect any defects, the system must be trained, and its sample flow chart is presented in Figure 2.  
Figure 1: Optical device for measuring complex structures [8]. The process starts with the need to resize images to reduce the load of the system [16] since the general requirements of any AVI system are fast, cost-effective, and reliable systems [15]. Then, the system would do two checks--oil and scratch detections. If a part has any oil surplus on the surface, the part is rejected, and a new production order is released for a cleaning operation.