为了解决以往基于聚类分析的工艺路线发现方法重用价值低的难题，使最终提取到的典型工艺路线能够更好地支持基于制造资源的重用，提出一种融入多维制造信息的产品典型工艺路线发现方法。方法研究定义了融入多维制造信息的工艺信息元和工艺路线信息模型，并通过运用核主成分分析法对组成工艺路线的工艺信息元进行了维数约简，得到工艺路线本质维度的低维信息模型。在低维工艺路线信息模型的基础上，提出了工艺路线聚类距离的计算方法，结合演化细胞学习自动机实现了工艺路线的智能聚类，并从聚类簇中提取到了典型工艺路线。通过实例验证说明了所提方法的有效性。

To solve the difficulty of low reuse value in the traditional process route discovery methods based clustering analysis and to make the extracted process routes support the effective reuse based manufacturing resources, a multi-dimensional manufacturing information based typical product process route discovery method is presented. A process information element and process route information model based multi-dimensional manufacturing information are established, and consequently the lower-dimensional process route model of its own dimensionality is obtained by using kernel principal component analysis(KPCA) to reduce the dimensionality of process information element. Based on the lower-dimensional process route model, a distance calculation method for calculate the similarity between process routes is proposed and evolutionary cellular learning automata was applied to realize the intelligent clustering division of process routes. The typical process routes are extracted from the clustering clusters consequently. Experimental results show that the effectiveness of proposed method is verified.