This research proposes a novel framework to control the stencil cleaning profile selection in the stencil printing process (SPP). The SPP is a major contributor to yield loss in surface mount technology (SMT). Enhancement in SPP performance is critical to improving the printed circuit board (PCB) assembly line. The selection of a solvent-based or a dry-based cleaning profile is challenging, but the choice determines the effectiveness and efficiency of the stencil cleaning operation. The amount of residue buildup under the stencil is the main criterion used to decide the appropriate cleaning profile in SPP. In this research, a multi-dimensional temporal recurrent neural network (RNN) approach is used to accurately predict the amount of residue buildup on the underneath surface of the stencil in real-time. Specifically, the long short-term memory (LSTM) architecture is trained using actual residue buildup data. The proposed LSTM prediction model is compared with other state-of-the-art regression models such as multilayer perceptron (MLP) and ensemble learning models. Experimental results show the proposed LSTM model outperforms the state-of-the-art regression models and accurately predicts the stencil status. The proposed research aids decision-makers in the SPP line to select the appropriate stencil cleaning profile adaptively and in real-time. As a result, the overall SPP performance is improved.

这项研究提出了一种新颖的框架，用于控制模板印刷过程（SPP）中的模板清洗轮廓选择。SPP是造成表面贴装技术（SMT）产量损失的主要因素。SPP性能的提高对于改善印刷电路板（PCB）装配线至关重要。选择基于溶剂或基于干的清洁工艺非常困难，但是选择决定了模板清洁操作的有效性和效率。模板下残留的残留量是用来确定SPP中合适的清洁特性的主要标准。在这项研究中，多维时域递归神经网络（RNN）方法用于实时准确地预测模板下表面残留的残留量。特别，长短期记忆（LSTM）体系结构是使用实际残留物累积数据进行训练的。拟议的LSTM预测模型与其他最新的回归模型（例如多层感知器（MLP）和集成学习模型）进行了比较。实验结果表明，所提出的LSTM模型优于最新的回归模型，并且可以准确预测模具状态。拟议中的研究可帮助SPP生产线的决策者自适应地，实时地选择合适的模板清洁曲线。结果，改善了整体SPP性能。实验结果表明，所提出的LSTM模型优于最新的回归模型，并且可以准确预测模具状态。拟议中的研究可帮助SPP生产线的决策者自适应地，实时地选择合适的模板清洁曲线。结果，改善了整体SPP性能。实验结果表明，所提出的LSTM模型优于最新的回归模型，并且可以准确预测模具状态。拟议中的研究可帮助SPP生产线的决策者自适应地，实时地选择合适的模板清洁曲线。结果，改善了整体SPP性能。