Decommissioning plan of nuclear facilities require the radiological characterizations and the establishment of a decommissioning process that can ensure the safety and efficiency of the decommissioning workers. By utilizing the rapidly developed ICT technology, we have developed a technology that can acquire, analyze, and deliver information from the decommissioning work area to ensure the safety of decommissioning workers, optimize the decommissioning process, and actively respond to various decommissioning situations. The established a surveillance system that monitors nuclide inventory and radiation dose distribution at dismantling work area in real time and wireless transmits data for evaluation. Developed an evaluation program based on an evaluation model for optimizing the dismantling process by linking real-time measurement information. We developed a technology that can detect the location of dismantling workers in real time using stereovision cameras and artificial intelligence technology. The developed technology can be used for safety evaluation of dismantling workers and process optimization evaluation by linking the radionuclides inventory and dose distribution in dismantling work space of decommissioning nuclear power plant in the future.

An Ant Colony Optimization Algorithm(ACO) is one of the frequently used algorithms to solve the Traveling Salesman Problem(TSP). Since the ACO searches for the optimal value by updating the pheromone, it is difficult to consider the distance between the nodes and other variables other than the amount of the pheromone. In this study, fuzzy logic is added to ACO, which can help in making decision with multiple variables. The improved algorithm improves computation complexity and increases computation time when other variables besides distance and pheromone are added. Therefore, using the algorithm improved by the fuzzy logic, it is possible to solve TSP with many variables accurately and quickly. Existing ACO have been applied only to pheromone as a criterion for decision making, and other variables are excluded. However, when applying the fuzzy logic, it is possible to apply the algorithm to various situations because it is easy to judge which way is safe and fast by not only searching for the road but also adding other variables such as accident risk and road congestion. Adding a variable to an existing algorithm, it takes a long time to calculate each corresponding variable. However, when the improved algorithm is used, the result of calculating the fuzzy logic reduces the computation time to obtain the optimum value.

This study presents an example of creating and optimizing a task sequence required in an automated remote dismantling system using a digital manufacturing system. An automated remote dismantling system using a robotic arm has recently been widely studied to improve the efficiency and safety of the dismantling operations. The task sequence must be verified in advance through discrete eventbased process simulation in a digital manufacturing system to avoid problems in actual remote cutting operations as the main input of the automated remote dismantling system. The laser cutting method can precisely cut complicated target structures such as reactor internals with versatility, but a robot and a pre-prepared program are required to deploy sophisticated motion of the laser cutting head on the target structure. For safe and efficient dismantling operations, the robot’s program must be verified in advance in a virtual environment that can represent the actual dismantling site. This study presents creating and optimizing the task sequence of a robotic underwater laser cutting as part of the project of developing an automated remote dismantling system. A task sequence is created to implement the desired cutting path for the target structure using the automated remote dismantling system in the virtual environment. The task sequence is optimized for the posture of the laser cutting head and the robot to avoid collisions during the operation through discrete event-based process simulation since the target structure is complicated and the volume occupied by the laser cutting head and the robot arm is considerably large. The task sequence verified in the digital manufacturing system is demonstrated by experiments cutting the target structure along the desired cutting path without any problems. The various simulation cases presented in this study are expected to contribute not only to the development of the automated remote dismantling system, but also to the establishment of a safe and efficient dismantling process in the nuclear facility decommissioning.

The RPV internal structure is a high radio activated part and has very complex geometry. Therefore, it needs to be cut remotely with an automated cutting system to minimize the worker exposures. To do so, we made up the remote laser cutting system with a laser cutter, robot manipulator and control software system and the laser cutter is moved by the robot manipulator based on the command from the control software system. A laser cutter is required to keep the desired standoff position between the nozzle of the laser cutter and surface of the cut target model to cut properly. Moreover, in the remote cutting process, an exact time and sequence control of the air supply and the laser emission is required for the cutting quality and the process safety. In this study, we proposed the PERT chart-based process execution and control methodology. The PERT chart is a graph which is represented by nodes and edges. The node of the PERT chart has the information about the activity details such as activity type, execution time and related device. Using the edge we make the sequence of the desired activity execution. A PERT chart of the cutting scenario is compiled in the control software system to creates data and thread structure to operate the physical device. We built software architecture to interpret and execute the PERT chart efficiently in the digital simulation platform which enables us to use existing pre-built simulation scenario for the laser cutting process. In addition, we have tested various laser cutting test cases in our test bed to verify the performance of our system. The test bed environment has the shape of the RPV internal structure and is placed under water.

For highly contaminated elements such as reactor pressure vessels or reactor internals, it is a viable option to cool-down and dismantle these elements in submerged (e.g. underwater) state. Several tools and processes such as saw cutting, water jet cutting or plasma cutting are currently used for underwater cutting, with each of them having their own advantages and disadvantages. The main disadvantage of these existing methods, especially saw and water jet cutting, is the generation of secondary waste that then needs to be filtered out of the water. In addition, in the case of water jet cutting, a considerable amount of abrasive material is added, which must also be stored. To overcome this drawback, the feasibility of using laser cutting under water to minimize secondary waste production has been actively studied recently. One of the challenges with the underwater laser cutting is to visually monitor the cutting process. Flowing air bubbles generated by the cutting gas and the flashing light emitted from the laser and melting material prohibit visual monitoring of the cutting process. This study introduces a method to enhance the video from a monitoring camera. Air bubbles can be detected by computing optical flows and the video quality can be enhanced by selective removal of the detected bubbles. In addition, suppressing the frame image update from flashing light area can also effectively enhance the video quality. This paper describes the simple yet effective video quality enhancement method and reports preliminary results.

3D imaging equipment is essential for automated robotic operations that cut radiologically contaminated structure and transfer segmented pieces in nuclear facility dismantling site. Automated dismantling operations using programmed robotic arms can make conventional nuclear facility dismantling operations much more efficient and safer, so dismantling technologies using robotic arms are being actively researched. Resolving the position uncertainty of the target structure is very important in automated robot work, and in general industries, the problem of position uncertainty is solved through the method of teaching the robot in the field, but at the nuclear facility dismantling site, the teaching method by workers is impossible due to activated target structures. Therefore, 3D imaging equipment is a key technology for a remote dismantling system using automated robotic arms at nuclear facility dismantling site where teaching methods are impossible. 3D imaging equipment available in radioactive and underwater environments is required to be developed for a remote dismantling system using robotic arms because most commercial 3D scanners are available in air and certain 3D scanners available in radioactive and underwater environments cannot satisfy requirements of the remote dismantling system such as measurement range and radiation resistance performance. The 3D imaging equipment in this study is developed based on an industrial 3D scanner available in air for efficient development. To protect the industrial 3D scanner against water and radiation, a housing is designed by using mirrors, windows and shieldings. To correct measurement errors caused by refraction, refraction model for the developed 3D imaging equipment is defined and parameter studies for uncertain variables are performed. The 3D imaging equipment based on the industrial 3D scanner has been successfully developed to satisfy the requirements of the remote dismantling system. The 3D imaging equipment can survive up to a cumulative dose of 1 kGy and can measure a 3D point cloud in the air and in water with an error of less than 1 mm. To achieve the requirements, a proper industrial 3D scanner is selected, a housing and shielding for water and radiation protection is designed, refraction correction are performed. The developed 3D imaging equipment is expected to contribute to the wider application of automated robotic operations in radioactive or underwater environments.

The remote dismantling system proposed in this paper is a system that performs the actual dismantling process using the process and program predefined in the digital manufacturing system. The key to the successful applying this remote dismantling system is how to overcome the problem of the difference between the digital mockup and the actual dismantling site. In the case of nuclear facility decommissioning, compensation between the virtual world and the real world is difficult due to harsh environments such as unsophisticated dismantling sites, radiation, and underwater, while offline programming can be proposed as a solution for other industries due to its sophisticated and controllable environment. In this paper, the problem caused by the difference in the digital mockup is overcome through three steps of acquisition of 3D point cloud in radiation and underwater environment, refraction correction, and 3D registration. The 3D point cloud is acquired with a 3D scanner originally developed in our laboratory to achieve 1 kGy of radiation resistance and water resistance. Refraction correction processes the 3D point cloud acquired underwater so that the processed 3D point cloud represents the actual position of the scanned object. 3D registration creates a transformation matrix that can transform a digital mockup of the virtual world into the actual location of a scanned object at the dismantling site. The proposed remote dismantling system is verified through various cutting experiments. In the experiments, the cutting test object has a shape similar to the reactor upper internals and is made of the same material as the reactor upper internals. The 105 successful experiments demonstrate that the proposed remote dismantling system successfully solved the key problem presented in this paper.

For application in nuclear decommissioning, underwater laser cutting studies were conducted on thick stainless-steel plates for various cutting directions using a 6 kW fiber laser. For cutting along the horizontal direction with horizontal laser irradiation, the maximum cutting speed was 110 mm∙min−1 for a 48 mm thick stainless-steel plate. For cutting along the vertical direction with horizontal laser irradiation, a maximum speed of 120 mm∙min−1 was obtained for the same thickness, which confirmed that the cutting performance was similar but slightly better. Moreover, when cutting with vertically downward laser irradiation, the maximum cutting speed was 120 mm∙min−1 for a plate of the same thickness. Thus, the cutting performance for vertical irradiation was nearly identical to that for horizontal irradiation. In conclusion, it was possible to cut thick stainless-steel plates regardless of the laser irradiation and cutting directions, although the assist gas rose up due to buoyancy. These observations are expected to benefit laser cutting procedures during the actual dismantling of nuclear facilities.

Meta-heuristic algorithms have been developed to efficiently solve difficult problems and obtain a global optimal solution. A common feature mimics phenomenon occurring in nature and reliably improves the solution through repetition. And at the same time, the probability is used to deviate from the regional optimal solution and approach the global optimal solution. This study compares the algorithm created based on the above common points with existed SA and HS to show advantages in time and accuracy of results. Existing algorithms have problems of low accuracy, high memory, long runtime, and ignorance. In a two-variable polynomial, the existing algorithms show that the memory increases and the accuracy decrease. In order to improve the accuracy, the new algorithm increases the number of initial inputs and increases the efficiency of the search by introducing a direction using vectors. And, in order to solve the optimization problem, the results of the last experiment were learned to show the learning effect in the next experiment. The new algorithm found a solution in a short time under the experimental conditions of long iteration counts using a two-variable polynomial and showed high accuracy. And, it shows that the learning effect is effective in repeated experiments.