# AI-Based Quality Control of Wood Surfaces with Autonomous Material Handling
## 5 Conclusions and Future Work  
This paper discusses concepts found in Industry 4.0, such as smart factories and flexible automation. Several independent, of the shelf systems, that utilizes IIoT, AI image object identifier, modular design, and cyber physical systems are integrated into a physical demonstrator. The purpose of the integration is to carry out a surface inspection process, of a CLT panel, in order to find defects. The off the shelf systems which are used in the inspection process are as follows:  
* A camera with software utilizing CNN and object detection from Cognex called D900;
* A transportation system using autopilot and path planning algorithms from Mobile Industrial Robots called MIR200;
* A Wireless router from NETGEAR called N150. The Plug and Produce cell, in Figure 1, which is used in the inspection process is part of University West's Plug and Produce research. The Plug and Produce cell house an IRB 6700 robot together with an installed safety system including light beams, proximity sensors and a safety PLC. The Cognex D900 camera system is trained to detect one type of defect. However, additional defect classification is possible. The limitation of the number of defects that can be classified into CNN remains to be seen. Indicators of the number of defects, such as the confusion matrix, are available. Further investigation can be performed by retraining the AI classification tool. This retraining can be performed after the inspection process has been running for some time, and new image data has been acquired. This can be useful for improving the AI to recognize knots in different kinds of wood or different lighting conditions. It can also be interesting to investigate how this improvement can be made easy for an operator, and also to find out if it is possible to annotate defects manually and save it for later training material. In general, using this approach for image postprocessing on a smart camera is beneficial since it reduces the network load and further modularizes the setup in a factory. The research clearly shows that an AI based vision inspection system can be setup by utilizing off the shelf products and has great potential of being implemented in the industry. An implementation of a similar system will gain the quality of processed wood in a cost efficient manner. The AI based vision inspection system is trained to detect defects and deviation on wood surfaces, but it can be trained for other inspection tasks in other fields than the wood industry. Concerning the communication an initial test was performed in the effort to connect the Cognex D900 to a local Wi-Fi network. This test was unsuccessful, and a separate Wi-Fi router is used instead. The separate Wi-Fi router needs its own power source which is proved to be bulky. A future improvement could be a power over ethernet (PoE) adapter and an ethernet to Wi-Fi adapter connected to the camera instead of using a Wi-Fi router and its own power source. Such an improvement also allows the TCP connection to be conducted directly through an access point. In future work, testing of connectivity and Wi-Fi, security, and the use of access points in a bigger scale needs to be addressed. The OPC UA framework has security functionality that is possible to integrate and is a part of what makes the OPC UA the new paradigm in industry communication. After this research was conducted, ABB realest their first OPC UA server which will replace their OPC DA server inside the robot controller. The hope is to be able to make a direct connection to the OPC UA server from our own OPC UA server in future research. The connection between DA and UA servers in this research is not wireless and which is an ambition for future research. Furthermore, an implementation of the inspection system in a CLT factory will be conducted to test and validate the inspection system in real industrial environment. Another future implementation is to develop methods for automatic reparation of the found defects in the CLT plates. Conceptualization, M.E., D.J. and D.S.