Intrinsic energy efficiency element function acquisition system and acquisition method of CNC machine tools

Abstract

The invention discloses a system for acquiring inherent energy efficiency element functions of a numerically controlled machine tool, which comprises a device information management module, a test parameter setting module, an NC code generation module, a field test management module, a data analysis module, an effectiveness verification module and a power sensor. The NC code generation module is used to generate NC codes to control the operation of the CNC machine tool to be tested according to the information of the CNC machine tool to be tested and the test parameters; the field test management module is used to analyze the power data information of the power sensor and generate each input of the CNC machine tool during each test operation process Power set; the data analysis module is used to generate the inherent energy efficiency factor function of the CNC machine tool and its energy consumption subsystem according to the input power set. The invention also discloses a method for obtaining the inherent energy efficiency elements of the numerical control machine tool. The invention solves the problems of difficult detection process of inherent energy efficiency elements of numerical control machine tools, incomplete detection data, complicated data processing and the like, and can provide technical support for energy efficiency evaluation of new machine tools and energy efficiency improvement of old machine tools.

Description

technical field [0001] The invention relates to the technical field of mechanical processing, in particular to a system and method for acquiring inherent energy efficiency elements for numerically controlled machine tools. Background technique [0002] CNC machine tool is a typical manufacturing system with machine tool as the main body. Due to the large quantity and wide range of CNC machine tools, the total energy consumption is huge, but the energy efficiency is very low, and it has not been paid enough attention in the past; therefore, how to improve the energy efficiency of CNC machine tools is rapidly emerging in the world. Our research in recent years has found that the fundamental reason for the different energy efficiencies of different CNC machine tools lies in their different inherent energy efficiency elements; moreover, the inherent energy efficiency elements are produced in the design and construction stages of the machining and manufacturing system, and act on...

AI Technical Summary

Problems solved by technology

The existing detection methods focus on the power and energy consumption of the spindle system and feed axis system of the machine tool and their detection, while the inherent energy efficiency elements of the CNC machine tool include the standby power function of the CNC machine tool, the power function of the auxiliary system of the CNC machine tool, and the spindle system. The energy consumption function and start time function of the starting process, the power function of the spindle system dry running process, the power function of the feed axis system cutting feed process, the energy consumption function and time function of the feed axis system rapid feed and retract process, The energy consumption function and time function of the automatic workpiece loading and unloading system and the energy consumption function and time function of the automatic tool change system; therefore, the existing detection methods lack a systematic and comprehensive measurement of the inherent energy efficiency elements of CNC machine tools from an overall perspective

[0006] (2) Data processing is cumbersome and complicated

Existing detection methods require multiple measurements and records artificially, the operation is cumbersome, and it is not easy to popularize and implement

[0007] (3) The fitting model is single

Existing detection methods focus on using a linear regression to fit the no-load power of the spindle, and a quadratic polynomial to fit the power of the feed shaft; however, the existing theoretical research and actual measurement results show that: different spindle systems and feed shaft systems, often There are different power demand laws; simple linear regression or quadratic polynomial fitting cannot meet the requirements

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