인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Non ferrous metal casting ingots must carry relevant production information, usually using manual pasting of copper coated paper, manual lifting of spray code, and pneumatic marking methods. These methods have a low degree of automation and severe material waste. To this end, genetic algorithm (GA) is used to guide sampling of random sample consensus algorithm (RANSAC) based on probability, and the two are combined for simulation to optimize the shortcomings of RANSAC algorithm in random sampling. On the ground of the optimized RANSAC to fit the plane equation, the normal vector of the plane is calculated, and the angle between the coordinate axis and the normal vector in the pendulum coordinate system is determined through the normal vector, enabling automatic alignment and vertical focusing functions to be achieved. Finally, based on the actual situation, the marking position is determined using set relationships to achieve motion control of mechanical functions. A laser marking method for non-ferrous metal casting ingots based on the improved RANSAC algorithm was designed. Through experimental analysis, it was found that the average F1 value of the method is 96.42, the average accuracy is 98.24%, the RMSE is 0.236, and the running time is 18.40 seconds. The F1 value represents the combined performance of the model's accuracy and recall rate when dealing with the marking task. Combined with the above results, it can be seen that the research and design method can efficiently and accurately laser marking metal casting ingot, and improve production efficiency.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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