A multi-objective comprehensive optimization method for tool holders

Abstract

The invention discloses a multi-objective comprehensive optimization method for a tool holder, which includes the following steps: Step 1: Construct a tool holder matching model based on RBR, and infer a candidate tool holder set T={H B F} that meets the processing task requirements; Step 2 : The AHP-grey relational analysis algorithm is used to perform multi-objective comprehensive optimization on the candidate tool holder set T={H B F}, so as to obtain the optimal tool holder combination that meets the multi-objective. The present invention combines rule reasoning with the AHP-Grey relational analysis algorithm and applies it to the optimization of tool holders. Matching is no longer restricted by the existing knowledge and experience of production personnel, which improves the accuracy and efficiency of matching and has important practical value.

Description

technical field

[0001] The invention belongs to the technical field of machining and manufacturing, and in particular relates to a matching method of a tool holder. Background technique

[0002] As a component element of a gear system, gears are widely used and play a key role in mechanical equipment such as wind power generation, aerospace, automobiles and shield machines; the quality of their dynamic characteristics will directly affect the performance of the whole machine . For the processing of gears, gear hobbing is currently the most widely used gear cutting method at home and abroad, and the possession of gear hobbing machines in some countries accounts for about 45% to 50% of the total gear machine tools. Therefore, the hobbing machine tool, as a typical type of manufacturing machine tool, has a significant impact on the manufacturing and processing of gears, especially the reasonable selection of tool holders for gear hobbing machines is of great significance in th...

Examples

Embodiment Construction

[0074] A multi-objective comprehensive optimization method for tool holders, refer to figure 1 shown, including the following steps:

[0075] Step 1: Construct a tool holder selection model based on RBR, and infer a set of candidate tool holders T = {H B F} that meets the requirements of the processing task; where H is the set of candidate tools, B is the set of candidate toolholders, and F is the set of candidate fixtures. There is a mapping relationship between the candidate tool set H and the elements in the candidate tool holder set B;

[0076] Step 2: Use the AHP-gray relational analysis algorithm to perform multi-objective comprehensive optimization on the candidate tool holder set T={H B F}, so as to obtain the optimal tool holder combination that meets the multi-objective.

[0077]The preferred method of the present invention is applied to an actual case to prove the feasibility of the preferred method; a case study is carried out by taking a gear processing workshop ...

technical field

[0001] The invention belongs to the technical field of machining and manufacturing, and in particular relates to a matching method of a tool holder. Background technique

[0002] As a component element of a gear system, gears are widely used and play a key role in mechanical equipment such as wind power generation, aerospace, automobiles and shield machines; the quality of their dynamic characteristics will directly affect the performance of the whole machine . For the processing of gears, gear hobbing is currently the most widely used gear cutting method at home and abroad, and the possession of gear hobbing machines in some countries accounts for about 45% to 50% of the total gear machine tools. Therefore, the hobbing machine tool, as a typical type of manufacturing machine tool, has a significant impact on the manufacturing and processing of gears, especially the reasonable selection of tool holders for gear hobbing machines is of great significance in th...

Examples

Embodiment Construction

[0074] A multi-objective comprehensive optimization method for tool holders, refer to figure 1 shown, including the following steps:

[0075] Step 1: Construct a tool holder selection model based on RBR, and infer a set of candidate tool holders T = {H B F} that meets the requirements of the processing task; where H is the set of candidate tools, B is the set of candidate toolholders, and F is the set of candidate fixtures. There is a mapping relationship between the candidate tool set H and the elements in the candidate tool holder set B;

[0076] Step 2: Use the AHP-gray relational analysis algorithm to perform multi-objective comprehensive optimization on the candidate tool holder set T={H B F}, so as to obtain the optimal tool holder combination that meets the multi-objective.

[0077]The preferred method of the present invention is applied to an actual case to prove the feasibility of the preferred method; a case study is carried out by taking a gear processing workshop ...

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