A method of tool holder optimization based on ahp-gray relational analysis algorithm

Abstract

The invention discloses a tool holder optimization method based on the AHP-gray relational analysis algorithm; firstly, the candidate tool holder set T={H B F} that meets the processing task is obtained, and the tool set H, tool bar The set B and the fixture set F are optimized to obtain the tool optimization set h * ={h 1 h 2 … h i … h l}, tool bar optimization set b * ={b 1 b 2 …b i …b m} with the fixture optimization set f * ={f 1 f 2 … f i … f k}; use AHP‑gray relational analysis algorithm to construct tool optimization set h respectively * , tool bar optimization set b * with fixture optimization set f * The correlation matrix; according to the sum of the correlation between the tool and the matching tool holder, the optimal matching combination of the tool and the tool holder is solved; the optimal matching combination of the tool and the tool holder and the fixture optimization set f * The fixture corresponding to the maximum correlation degree in the correlation degree matrix constitutes the optimal tool holder set, so as to obtain the optimal tool holder scheme. The invention can carry out multi-objective decision-making from the set of tool holder types meeting the processing task requirements, so as to comprehensively optimize the optimal tool holder matching scheme suitable for the processing task.

Description

technical field [0001] The invention belongs to the technical field of machining and manufacturing, and in particular relates to a preferred method for a tool holder. Background technique [0002] In the process of product production and processing, the consumption of tool holders, production costs, machining accuracy and surface quality, etc., depend to a large extent on the selection of tool holder materials and forms; The overall performance, weighing a large number of complex factors, to quickly and scientifically select the tool holder to improve the durability, machining accuracy and productivity of the tool holder, and reduce costs often exceed the existing knowledge and experience of production personnel. It is a Multi-level, multi-objective comprehensive optimization problem. At present, the following problems still exist in the preferred method of the tool holder: [0003] ① At present, the research on the optimization technology of tool holders is mainly aimed a...

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Problems solved by technology

[0003] ① At present, the research on the optimization technology of tool holders is mainly aimed at the processing tools of CNC machine tools, and there are few studies on the optimization technology of cutting tools for gear hobbing machines; Matching, so as to optimize a set of optimal tool holder combination suitable for processing tasks;

[0004] ②Most of the existing tool holder optimization methods aim to select the type of tool holder that can complete the processing content, but do not combine the actual situation of the tool holder resources in the workshop to select specific tools, tool holders and fixtures suitable for processing tasks

[0005] ③At present, most of the tool holders are optimized to use BP neural network model, fuzzy clustering comprehensive evaluation method, etc., but these algorithms need the support of high-level expert experience, and the self-organization and scalability are relatively insufficient; although BP neural network and other models have Self-learning, self-organization and other capabilities, but the samples required for pre-training are still manually selected; therefore, there are problems such as heavy workload, low efficiency and error-prone

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