Method for optimizing cutting parameters considering machining energy efficiency

Abstract

The invention discloses a method for optimizing cutting parameters considering machining energy efficiency. The method comprises the following steps that a cutting parameter optimization model of a numerical control machine tool is built according to a cutting process of the numerical control machine tool, constraint conditions of the cutting parameter optimization model are built according to performance and machining requirements of the numerical control machine tool, and the built cutting parameter optimization model and the constraint conditions of the cutting parameter optimization model are worked out through a modified multiple-target teaching and learning optimization algorithm to obtain optimized cutting parameters, wherein subject scores in the algorithm are the cutting speed v, the feeding speed f and the cutting depth ap of the numerical control machine tool, a student X is an individual possessing the three subject scores (v, f and ap), namely, X=(v, f and ap), a class is a group with NP students, and NP is a random integer. The method can efficiently optimize the machining cutting parameters by considering machining energy consumption, thereby solving the existing technical problems in the cutting parameter optimization process.

Description

technical field [0001] The invention belongs to the field of mechanical cutting processing, and more specifically relates to a method for optimizing cutting parameters of mechanical processing energy efficiency. Background technique [0002] Numerical control technology is the core technology of today's advanced manufacturing equipment industry. Reasonable selection of cutting parameters in the numerical control machining process can save energy and resources, improve production efficiency, thereby enhancing the economic benefits and competitiveness of enterprises. However, at present, most CNC processing enterprises in my country still select cutting parameters based on experience, reference manuals, and trial cutting, and it is often difficult to optimize processing energy efficiency. With the continuous development and improvement of modern intelligent optimization algorithms, cutting theory, mathematical modeling and model analysis methods, improving machining efficiency...

AI Technical Summary

Problems solved by technology

However, all studies are conducted from the perspective of the energy consumption of the entire machine tool, and the energy consumption increases with time, which inevitably overlaps with productivity or processing time.

In addition, meta-heuristic algorithms are used for the optimization algorithms of cutting parameters, and the parameter settings of these algorithms have a great influence on the solution results.

Moreover, when solving the model in the prior art, the multi-objective algorithm (such as the multi-objective teaching and learning optimization algorithm) based on non-dominated solution sorting is rarely used to solve the cutting parameter optimization problem

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