A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

Jianhua Cao,Xiang Liu,Xuhui Xia,Lei Wang,Zelin Zhang

doi:10.3390/su132413918

Jianhua Cao, Xiang Liu + Show 3 more

Open Access

https://doi.org/10.3390/su132413918

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Abstract

Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.

Highlights

Published: 16 December 2021In 2020, China announced that it will strive to achieve carbon peak by 2030 and carbon neutrality by 2060, and for the first time to include carbon peak and carbon neutrality in the government work report [1]
In order to verify the energy consumption prediction model of computer numerical control (CNC) machine tools based on improved parallel BPNN (IPBPNN) proposed in this paper, the basic data of machining energy consumption of CNC machine tools under different instructions and different process parameters are obtained through design experiments
A novel CNC milling energy consumption prediction method based on program decomposition and IPBPNN is presented

Summary

Introduction

Published: 16 December 2021In 2020, China announced that it will strive to achieve carbon peak by 2030 and carbon neutrality by 2060, and for the first time to include carbon peak and carbon neutrality in the government work report [1]. The impact of the machine tool construction, regarding raw materials and energy consumption, is understood to be relatively small, as it is amortized over numerous products during the long lifetime of the machine. As the basic energy-consuming equipment of the manufacturing industry, CNC machine tools have the characteristics of large quantity and wide range, large total energy consumption, and low efficiency, etc., and have great energy-saving potential. Fast and accurate prediction of its machining energy consumption is an effective way to achieve optimal management of energy consumption and achieve carbon peak and carbon neutrality [3]. CNC machine tools are a complex multi-source energy consumption system. Have varying degrees of impact on machining energy consumption [4]. How to accurately and Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations

Methods

Results

Conclusion