The accuracy and quality of part formation are crucial considerations. However, the laser directed energy deposition (L-DED) process often leads to irregular changes in deposition contours and mechanical properties across parts due to complex flow fields and temperature variations. Hence, to ensure the forming accuracy and quality, it is necessary to achieve precise monitoring and appropriate parameter adjustments during the processing. In this study, a machine vision method for real-time monitoring is proposed, which combines target tracking and image processing techniques to achieve accurate recognition of deposition contours under noisy conditions. Through comparative verification, the measurement accuracy reaches as high as 98.98 %. Leveraging the monitoring information, a bidirectional prediction neural network is proposed to accomplish layer-by-layer forward prediction of layer height. Meanwhile, inverse prediction is employed to determine the processing parameters required for achieving the desired layer height, facilitating the optimization of the deposition contours. It was found that as the processing parameters were adjusted layer-by-layer to achieve consistent deposition contours, there was also a tendency towards consistent changes in microstructure and mechanical properties. The standard deviations of primary dendrite arm spacing (PDAS) and ultimate tensile strength (UTS) at different positions decrease by over 52.2 % and 61.4 %, respectively. This study reveals the consistent patterns of variation in deposition contours and mechanical properties under data-driven variable parameter processing, laying an important foundation for future exploration of the complex process-structure-performance (PSP) relationship in L-DED.

中文翻译：

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一种新颖的数据驱动框架，用于增强金属增材制造中沉积轮廓和机械性能的一致性


零件成型的精度和质量是至关重要的考虑因素。然而，由于复杂的流场和温度变化，激光定向能量沉积（L-DED）工艺通常会导致零件的沉积轮廓和机械性能发生不规则变化。因此，为了保证成形精度和质量，需要在加工过程中实现精确的监控和适当的参数调整。本研究提出了一种实时监测的机器视觉方法，结合目标跟踪和图像处理技术，实现噪声条件下沉积轮廓的准确识别。经对比验证，测量精度高达98.98%。利用监测信息，提出了一种双向预测神经网络来完成层高的逐层前向预测。同时，采用逆预测来确定实现所需层高所需的工艺参数，有利于沉积轮廓的优化。研究发现，随着逐层调整加工参数以实现一致的沉积轮廓，微观结构和机械性能也有一致变化的趋势。不同位置的初生枝晶臂间距（PDAS）和极限拉伸强度（UTS）的标准差分别降低了52.2%和61.4%以上。这项研究揭示了数据驱动的可变参数处理下沉积轮廓和力学性能的一致变化模式，为未来探索L-DED中复杂的过程-结构-性能（PSP）关系奠定了重要基础。