In this research, we evaluate on the disassemblability of recycling process for vehicle front door using the symbolic chart method and machine-learning algorithm. It is applied to the front door of 1600cc class vehicle, and then the conventional steel door and CFRP door were compared. Based on the principle symbolic chart method, the number of processes can be different according to decomposer proficiency of suitability of recycling process, so the evaluation method is required to supply this issue. The machine learning algorithm, and artificial intelligence method were applied and the applicable tools for each experiment were used to compensate the variations in the number of processes according to different proficiencies. Because CFRP front door has integrated components compare to steel door, so its disassemblability processes were decreased to 80 from 103 of the conventional steel door’s. It can be confirmed that the disassemblability was increased from the suitability of recycling equation. In case of the steel, disassemblability was approximately 60.6, in case of the CFRP is approximately 72 for car front door. Therefore, it can be concluded that the disassemblability of CFRP was better in the evaluation of suitability of recycling.

Due to the increase of income, people has a car more than one per household. So parking space is needed to minimum size for making many parking spaces. But narrow parking space causes that interference with another car is occurred when car's door is opening or closing. So sliding door is best way for solving the problem. But sliding structure of the door is difficult to apply to all vehicle because existing sliding structure is limited. For this reason, new sliding structure of the door is needed. In this study, we suggest new sliding mechanism of front door for vehicle and confirm the sliding mechanism using a finite element analysis.