In view of the following problems existed in the data-driven resistance spot welding (RSW) quality judgment methods: relying on expert experience, resulting in a high misjudgment rate; affected by the initial value, it is easy to fall into the local extreme point; multiple parameters needed to be modulated to increase the training time of the model. Therefore, this paper proposed a RSW quality judgment method based on revised quantum genetic algorithm (RQGA) and hidden markov model (HMM). Which used quantum rotation gate to replace the evolution process of genetic algorithm (GA), reducing the number of parameters that needed to be modulated in the model, and optimizing the initial model of HMM by RQGA to avoid falling into local extreme point. At the same time, combining expert experience with HMM to make quality judgment to ensure the accuracy of judgment.

中文翻译：

---

基于修正量子遗传算法和隐马尔可夫模型的不锈钢薄板电阻点焊质量判断研究


针对数据驱动的电阻点焊（RSW）质量判定方法存在以下问题：依赖专家经验，导致误判率较高；受初值影响，容易陷入局部极值点；需要调节多个参数以增加模型的训练时间。因此，本文提出一种基于修正量子遗传算法（RQGA）和隐马尔可夫模型（HMM）的RSW质量判断方法。利用量子旋转门代替遗传算法（GA）的进化过程，减少模型中需要调制的参数数量，并通过RQGA优化HMM的初始模型，避免陷入局部极值点。同时，结合专家经验与HMM进行质量判断，保证判断的准确性。