# Optimization Method of Assembly Tolerance Types Based on Degree of Freedom
## 2 Related Work  
The optimization of tolerance type is an important work after the tolerance specification is automatically generated. This section firstly introduces the current status of automatic generation of tolerance specifications; secondly, it reviews the methods of tolerance type optimization. In terms of automatic generation of tolerance types, many researchers have conducted a lot of research and summarized it into four categories: Technology and topology related surface (TTRS) based methods [24], assembly positioning constraint-based methods [25; 26; 27; 28], case-based learning methods [29; 30; 31], and rule-based reasoning methods [8; 9; 10; 11; 12; 13; 14; 15; 16; 32; 33; 34]. TTRS-based method [24; 25]. [PERSON] et al. [25] firstly defines the geometric surfaces of mechanical parts as seven basic types, and secondly proposes to represent the above seven geometric surfaces with different combinations of three minimal geometric reference elements such as point, line, and plane. These combinations are then applied, along with a given set of rules to describe the assembly relationship between two assembly parts to achieve the tolerance topology of the assembly layer. Finally, a specific tolerance design software is developed in conjunction with the modern technical standard ASMEY14.5 to achieve automatic generation of tolerance items for assembly parts. The use of this method for designing tolerance types only ensures that the shape-tolerance type corresponds to the shape characteristics of the functional surfaces of the assembly, and the directional position tolerance corresponds to the spatial position relationship between the smallest geometric elements. It does not take into account the geometric functional requirements of the assembly. Method based on assembly positioning constraints [26; 27; 28; 29]. [PERSON] et al. [26] proposed a positioning table to describe the assembly constraint relationships between the target part and other assembly parts, as well as to determine the positioning priority of the assembly feature surfaces of the target part. The automatic generation of tolerance specifications is achieved through CEXEL software. [PERSON] [27] designed assembly constraint tables with dimensional attributes by studying the functional relationships in the product assembly process, on the basis of which interactive tolerance design presentation software is developed. [PERSON] [28] et al. and [PERSON] et al. [29] combined graph theory with assembly positioning constraint information to establish a part-level geometric transfer paths for tolerance items; and implement the tolerance specification design of parts step by step. However, they did not develop corresponding software for automatic tolerance generation. Case-based learning method [30; 31; 32]. [PERSON] et al. [31] normalize the part feature and annotation tolerance information from previous tolerance specification schemes to form a tolerance specification database and build a machine learning model. [PERSON] et al. [32] use a descriptive logic language to build an ontological knowledge database, and a similarity measure is used to evaluate the similarity between the target tolerance problem and the tolerance information of the cases. On this basis, a search algorithm is built to obtain a recommended tolerance scheme. At present, however, the successful application is limited due to the lack of sufficient model data. The metric values obtained by the recommendation still require designers to make decisions based on design experience in the case of equality. Rule-based reasoning method [8; 9; 10; 11; 12; 13; 14; 15; 16; 33; 34; 35]. [PERSON] [8], [PERSON] [9] and others introduced the theory of polychromatic sets to describe the assembly relationships, constraint types and assembly feature characteristics between parts. On this basis, a reasoning relationship matrix is established between the above research objects and tolerance types, and an automatic tolerance item generation algorithm is formed. [PERSON] et al. [13; 33] and [PERSON] et al.