Extrusion casting process parameter design method of improved KNN algorithm based on similarity principle

Abstract

The invention provides an extrusion casting process parameter design method of an improved KNN algorithm based on a similarity principle, and belongs to the technical field of process parameter design, and the process parameter design method comprises the following steps: firstly, measuring the relative change trend of related variables of process parameters based on the similarity principle; on the basis, sample data similar to the relative change trend of related variables of the target case are extracted, KNN clustering is carried out based on the sample data, and weighted regression is carried out on corresponding parameters in clustered samples to serve as technological parameter design values of the target case. According to the design method, based on existing research data, existing related research results are fully referred and utilized, tedious experimental research is not needed, and the design precision of technological parameters is high. Compared with an existing process parameter design method, a large amount of time cost and experiment cost are saved. The method has universality, and is not only suitable for design of extrusion casting process parameters, but also suitable for parameter design in other similar fields.

Description

technical field [0001] The invention relates to the field, in particular to a method for designing squeeze casting process parameters based on an improved KNN algorithm based on a similar principle. Background technique [0002] Squeeze casting process parameters directly affect the mechanical properties and microstructure of castings. Reasonable process parameters are the premise and key to produce squeeze castings with the above advantages. The existing squeeze casting process parameter design methods mainly include obtaining process parameters based entirely on experimental research, obtaining process parameters through modeling optimization based on experimental data, and theoretical calculation. The method of obtaining process parameters based entirely on experimental research and the method of obtaining process parameters based on experimental data modeling optimization require experiments, rely on experimental data, and can only conduct experiments on a single materia...

AI Technical Summary

Problems solved by technology

The method of obtaining process parameters based entirely on experimental research and the method of obtaining process parameters based on experimental data modeling optimization require experiments, rely on experimental data, and can only conduct experiments on a single material and casting each time, while material composition and casting geometry There are various shapes, so its adaptability and flexibility are poor (even for numerical simulation, replacing castings requires remodeling, designing molds, iterations, and time-consuming); and the cost is high, and it takes a long time to obtain process parameters

As for the third method, currently there are only some process parameter design formulas, and most of them are aimed at a certain type of casting with uniform characteristics, which has relatively large limitations, ignores the coupling effect between process parameters, and rarely takes into account the interaction between process parameters. interaction, leading to large errors in theory and practice

It can be seen that the existing process parameter design methods have their own shortcomings, and it is difficult to meet the needs of the rapid development of squeeze casting process parameters and rating applications

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