Identifying the optimal sequence of joining is an exhaustive combinatorial optimization problem. On each assembly, there is a specific number of weld points that determine the geometrical deviation of the assembly after joining. The number and sequence of such weld points play a crucial role both for sequencing and assembly planning. While there are studies on identifying the complete sequence of welding, identifying such joints are not addressed. In this paper, based on the principles of machine intelligence, black-box models of the assembly sequences are built using the support vector machines (SVM). To identify the number of the critical weld points, principle component analysis is performed on a proposed data set, evaluated using the SVM models. The approach has been applied to three assemblies of different sizes, and has successfully identified the corresponding critical weld points. It has been shown that a small fraction of the weld points of the assembly can reduce more than 60% of the variability in the assembly deviation after joining.

确定最佳的加入顺序是一个详尽的组合优化问题。在每个组件上，都有特定数量的焊接点，这些焊接点确定了连接后组件的几何偏差。此类焊接点的数量和顺序对于顺序和装配计划均至关重要。尽管已经进行了确定完整焊接顺序的研究，但仍未涉及确定此类接头。在本文中，基于机器智能的原理，使用支持向量机（SVM）构建装配序列的黑匣子模型。为了确定关键焊接点的数量，对建议的数据集执行主成分分析，并使用SVM模型对其进行评估。该方法已应用于三个不同大小的组件，并已成功识别出相应的关键焊接点。已经表明，组件的焊接点的一小部分可以减少接合后组件偏差的60％以上的变化。