本研究的發想起源於矽晶型太陽能板在廣泛使用下所產生的迫切回收需求。首先，文獻回顧了矽晶型太陽能板的組成、各項回收技術，指出現有回收方法在處理效率及永續性方面的不足，也強調人工智慧（AI）與機器人技術在提升回收過程中的潛力。
本研究方法結合破碎方法、AI影像辨識及機器手臂系統整合，探討破碎後材料的解離程度，以產出適合AI系統辨識的碎片，並以YOLOv8x-seg模型進行即時辨識，透過Modbus TCP將影像辨識結果及3D位置傳遞給KUKA機器手臂系統。機器手臂控制器作為中控，協調震動盤、餵料器和相機的即時反應。實驗結果顯示，爪刀型破碎機對於矽晶型太陽能板的材料單離更有效率，破碎尺寸介於1.19至4毫米可顯著提升材料回收率和回收處理量。
YOLOv8x-seg模型在邊界框預測、分割遮罩和物件分類的損失函數上呈現穩定下降趨勢。精確率和召回率達到平衡，在各類別中（特別是銅碎片）呈現高指標度。mAP分析顯示，模型性能在短訓練週期內快速提升並穩定，呈現快速收斂。驗證批次預測顯示模型在檢測和分類上的穩健性，但在處理銅分類偶有辨識錯誤情形。
在成功夾取率方面，最佳速度為10%，平均成功率達87.63%，而在更高速度下由於真空吸力不足，成功率有所下降。準確度在所有速度下保持穩定，平均為78.81%，顯示AI影像辨識結合到機器人應用時需考量實際機台設計對於辨識表現的影響。處理量分析顯示處理量趨勢穩定，15%的速度可每小時處理13克太陽能板碎片。本研究的AI影像辨識呈現高準確度的穩定運行，但在機器手臂的末端治具設計仍需要改進，以便達到更高的成功夾取率來應對未來需求。

This study addresses the urgent need to recycle crystalline silicon solar panels, motivated by their widespread use and the substantial waste they generate. It begins with a comprehensive examination of the composition of solar panels and identifies deficiencies in the efficiency and sustainability of current recycling methods. The literature review highlights the advancement of recycling techniques and the potential of artificial intelligence (AI) and robotics to enhance these processes.
The proposed method focuses on refining crushing processes to produce fragments that are optimal for recognition by AI, utilizing the YOLOv8x-seg algorithm for real-time object detection and classification of these fragments. Image recognition results interface with KUKA robot arms via Modbus TCP to relay 3D positions. To coordinate this system, the robot controller manages the integration of a vibration plate, feeder, and CCD. Experimental results show that the Claw-Blades Crusher is more effective for material separation in crystalline solar panels. Focusing on mid-size ranges (1.19 to 4 mm) significantly enhances material recovery and recycling throughput.
The optimized YOLOv8x-seg model shows consistent decreases in loss metrics, indicating improved accuracy in bounding box predictions, segmentation masks, and object classifications. Precision and recall metrics are balanced, with high scores for most classes, particularly copper fragments. mAP analysis revealed steady performance improvement and stabilization over epochs. Validation batch predictions confirmed the robustness of the model in detecting and classifying fragments despite challenges with copper fragments.
The success picking rate was highest at 10%, averaging 87.63%, with a decrease at higher speeds due to insufficient vacuum force. Accuracy remained stable across all speeds, averaging 78.81%, highlighting the need to integrate AI performance with practical robotic design. Capacity analysis showed stable processing trends, with 15% speed processing approximately 13 grams per hour. The system performs stably with high-accuracy AI image recognition, though the robot's end effector design needs improvement for higher success picking rates to meet future demands.

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