A Method of Energy Consumption Prediction for CNC Machine Tool in Digital Workshop

Abstract

The invention discloses an energy consumption prediction method for a digitalized workshop numerical control machine tool, and the method comprises the steps: firstly enabling a digitalized workshop to be divided into a machine tool layer, a task layer, and a workshop layer; secondly calculating the energy consumption index parameters of all layers, wherein the energy consumption index parameter of the machine tool layer comprises the specific energy of material removal of the machine tool layer, the effective energy of material removal, the effective energy of machining, and the specific energy of the machine tool, the energy consumption index parameter of the task layer comprises the specific energy of material removal of the task layer, the effective energy of material removal of task, the effective energy of machining in a task, and the effective energy of the workshop, and the energy consumption index parameter of the workshop layer comprises the effective energy of the workshop layer, a power load factor, and the carbon intensity of energy; and finally predicting the energy consumption of all machining processes of one workpiece according to the energy consumption index parameters, thereby predicting the total energy consumption of a batch of products. The method can help an enterprise obtain energy consumption of products, achieves the energy management of all layers of the workshop, and is wide in application prospect of the actual production of the workshop.

Description

[0001] 【Technical field】 [0002] The invention belongs to the fields of machine tool cutting energy consumption control and energy consumption monitoring, and in particular relates to a method for collecting machine tool energy consumption data through hardware equipment and combining the collected data and empirical formulas to calculate energy consumption during machine tool cutting. [0003] 【Background technique】 [0004] Machine tools are the main components of discrete manufacturing systems. A large number of surveys and statistics also show that: the average energy utilization rate of machine tools is low, averaging less than 30%; under Toyota's mass production mode, 85.2% of the total energy consumption of machine tools is used in the production links of non-processing operations. Studies have shown that the total energy consumption and carbon dioxide emissions of a machine tool running for one year are equivalent to the emissions of 61 SUV cars. With the advancement ...

AI Technical Summary

Problems solved by technology

[0006] However, due to limitations in technology and capital, many enterprises currently use manual statistics for the energy consumption statistics of the workshop and the entire enterprise, and the energy consumption analysis at the end of the year or at the end of the quarter is only for the entire workshop or the entire enterprise.

Enterprises with high energy consumption have improved the management level of energy consumption to a certain extent, but it is still in its infancy. Most of the energy management systems used by enterprises do not include the functions of energy consumption analysis and energy consumption prediction. , can not fundamentally find out the utilization of energy and realize the effect of energy saving

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