The stencil printing process (SPP) is a critical operation in surface mount technology (SMT) because it contributes to 60% of soldering defects. The complex relationships between solder paste volume transfer efficiency (TE) and the SPP variables make the control of the solder paste volume TE during production a challenging problem. This research aims to optimize the stencil printing parameters in real time to control the solder paste volume TE and increase the first-pass yields of printed circuit boards (PCBs). A Reinforcement learning (RL) approach, specifically $Q$-learning, is used to control and maintain the volume TE within the spec limits in an optimal adaptive control system. RL deals with the problem of building a control system or an autonomous agent that can learn how to take the proper actions to reach its objectives through interaction with its environment. The application of RL in SPP is not yet fully explored; therefore, this study investigates the impacts of applying $Q$-learning to control the volume TE in real time. The proposed control systems capture the induced variations in the SPP for two printing directions and consequently adjust the significant and easy-to-change printing parameters in real time. Two types of $Q$-learning are explored: $Q$-table that uses a tabular format to store the $Q$-values and $Q$-network that uses an artificial neural network (ANN) to approximate the $Q$-value function. Moreover, a new heterogeneous reward function-based clustering is proposed, which is integrated into the $Q$-network to enhance its performance. The results show that the developed control systems can learn the optimal policy and take the proper actions to transit from initial states to terminal states. The proposed control systems using $Q$-network with a function approximator and heterogeneous reward function converge fully much faster than $Q$-table using continuous state space. Moreover, $Q$-network control systems are capable to transit more states to terminal states with a lower number of actions when compared to $Q$-table control systems.

模版印刷工艺（SPP）是表面贴装技术（SMT）中的关键操作，因为它可造成60％的焊接缺陷。焊膏体积转移效率（TE）和SPP变量之间的复杂关系使得在生产过程中控制焊膏体积TE成为一个难题。这项研究旨在实时优化模板印刷参数，以控制锡膏量TE并提高印刷电路板（PCB）的首过合格率。强化学习（RL）方法，特别是$问$-学习，用于在最佳自适应控制系统中将TE的体积控制和保持在规格范围内。RL处理建立控制系统或自治代理的问题，该代理可以学习如何通过与环境交互来采取适当的措施来实现其目标。RL在SPP中的应用尚未得到充分探索。因此，本研究调查了应用$问$学习实时控制音量TE。所提出的控制系统在两个打印方向上捕获了SPP中的感应变化，因此可以实时调整重要且易于更改的打印参数。两种类型$问$探索学习： $问$-使用表格格式存储 $问$-值和 $问$-网络，使用人工神经网络（ANN）近似 $问$值函数。此外，提出了一种新的基于异构奖励函数的聚类算法，并将其集成到$问$网络以增强其性能。结果表明，所开发的控制系统可以学习最佳策略，并采取适当的措施从初始状态过渡到最终状态。建议的控制系统使用$问$具有函数逼近器和异构奖励函数的网络的收敛速度远快于 $问$-table使用连续状态空间。而且，$问$与-相比，网络控制系统能够以较少的动作将更多的状态转换为终端状态 $问$表控制系统。