# Hybrid Decision-Making-Method-Based Intelligent System for Integrated Bogie Welding Manufacturing
###### Abstract  
To address the challenges of incomplete knowledge representation, independent decision ranges, and insufficient causal decisions in bogie welding decisions, this paper proposes a hybrid decision-making method and develops a corresponding intelligent system. The collaborative case, rule, and knowledge graph approach is used to support structured documents and domain causality decisions. In addition, we created a knowledge model of bogie welding characteristics and proposed a case-matching method based on empirical weights. Several entity categorizations and relationship extraction models were trained under supervised conditions while building the knowledge graph. CRF and CR-CNN obtained high combined F1 scores (0.710 for CRF and 0.802 for CR-CNN) in the entity classification and relationship extraction tasks, respectively. We designed and developed an intelligent decision system based on the proposed method to implement engineering applications. This system was validated with some actual engineering data. The results show that the system obtained a high score on the accuracy test (0.947 for Corrected Accuracy) and can effectively complete structured document and causality decision-making tasks, having large research significance and engineering value. welding; case-based; rule-based; knowledge graph; entity classification; relationship extraction +
Footnote †: journal: Medical  
In recent years, decision reasoning methods have mainly focused on case-based [9; 10], rule-based [11; 12], and hybrid decision methods [13; 14; 15]. Case-based decision making is the process of obtaining the best matching case by calculating the similarity between old and new cases. This technique can be used to make decisions about future events based on a complete experience database, so it is widely employed in several domains, such as assembly management [16], process engineering design [17], and fault diagnosis [18]. However, effective decision making may be challenging when there is a lack of a case base. Rule-based methods [19] are also often used for decision making, where a priori experience and knowledge are transformed into mathematical logic to carry out decision tasks. Compared with the case-based approach, the rule-based system reduces the reliance on a priori data, but complex logic construction still needs to be solved. In order to synergize the advantages of case-based and rule-based approaches, hybrid decision-making methods have become a major research focus. [PERSON] et al. [20] proposed a hybrid approach to rule- and case-based material failure analysis and demonstrated that the hybrid approach obtains better analytical results than individual decisions. In weld manufacturing, [PERSON] et al. [21] offered a hybrid case- and rule-based system for welding processes and implemented system-level applications in weld manufacturing. In addition, algorithms such as Bayesian [22], data envelopment analysis [23], multi-criteria [24; 25; 26], fuzzy theory [27; 28; 29; 30], and neural networks [31; 32] have been introduced into decision making. These approaches have shown positive performance in several domains. However, incomplete knowledge representations are not addressed further due to data and logical form limitations. Secondly, most welding systems focus on a single stage of the production cycle and need to be adapted to adequately meet the integrated manufacturing model. Thirdly, end-to-end decision making alone does not satisfy the search for causality. A hybrid decision method based on a knowledge graph is proposed for the challenges of incomplete knowledge representation, independent decision scope, and insufficient causal decisions in integrated bogie welding manufacturing. This approach is centered on a knowledge graph and collaborates with rule-based and case-based decision methods to enable full-cycle decision making for welding and to support causal decision making across production stages.