چکیده مقاله
This research addresses the significant challenge of high defect rates in additive manufacturing AM processes, which primarily stem from unstable material deposition, concentrated thermal stress, and inadequate interlayer bonding The study aims to enhance the geometric accuracy, mechanical properties, and overall quality of fabricated components To achieve this, a high resolution visual sensing system is integrated to capture key process data, such as melt pool dynamics and thermal distribution, in real time Advanced image processing algorithms, including feature extraction and state recognition techniques, are employed to analyze this data Furthermore, an intelligent control strategy, potentially leveraging machine learning or adaptive models, is implemented to dynamically adjust critical process parameters like laser power and scanning speed This approach establishes a fully integrated perception decision control closed loop system Experimental results demonstrate that the proposed system significantly improves manufacturing quality, enabling micron level error control and high repeatability It also proves highly effective in suppressing typical defects, including warping and cracks, thereby advancing the reliability of AM for precision applications
کلیدواژهها
نویسندگان
شیوه ارجاع
Haghsefat, Kianoush and Zhang, Kai and Liu, Tingting,1404,Research on AI-Driven Modeling and Real-Time Control for Enhancing Additive Manufacturing Process Quality,8th International Conference on Welding and Non Destructive Testing & 26th National Conference on Welding & Inspection & 15th National Conference on NDT & 4th National Conference on Additive Manufacturing,Tehran
ارائهشده در
مجموعه مقالات هشتمین کنفرانس بین المللی جوشکاری و آزمایش های غیرمخرب، بیست و ششمین کنفرانس ملی جوش و بازرسی، پانزدهمین کنفرانس ملی آزمایش های غیرمخرب و چهارمین کنفرانس ملی ساخت افزایشی20 بهمن 1404 · تهران