scholarly journals A Generalized Analysis of Energy Saving Strategies Through Experiment for CNC Milling Machine Tools

2021 ◽  
Author(s):  
Chunhua Feng ◽  
Xiang Chen ◽  
Jingyang Zhang ◽  
Yugui Huang
Keyword(s):  
Energy Consumption ◽  
Correlation Coefficient ◽  
Machine Tools ◽  
Tool Path ◽  
Energy Requirement ◽  
Piecewise Linear ◽  
Specific Energy Consumption ◽  
Cutting Parameters ◽  
Linear Representation ◽  
Cnc Milling

Abstract This paper proposes the elaboration model of energy requirement prediction taking into account the power of standby, spindle rotation in non-load, feeding and rapid movement in X, Y, Z+ and Z- axially, and specific energy consumption (SEC) in the X and Y cutting directions respectively, which could not be considered completely in other models. Each part energy of specific machine tools could be obtained through little experiments for identifying the relationship between energy and tool path with cutting parameters. The method is validated by 27 trial cutting experiment in X and Y cutting directions in VMC850E machine, the results show that the SEC in the X and Y cutting directions are exactly different. Moreover, it is found that spindle power should be piecewise linear representation according to spindle speed characteristic, due to the correlation coefficient of power model only has 25.45% without segmented. Additionally, the correlation coefficient of improved SEC model could reach to more than 99.98% in each segment. The contribution of this paper is mainly the elaboration energy consumption model considering the cutting direction, which is an efficient approach for predicting energy consumption through tool path to achieve sustainable production in manufacturing sectors.

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

2020 ◽  
pp. 095440542097106
Author(s):  
Yu Su ◽  
Congbo Li ◽  
Guoyong Zhao ◽  
Chunxiao Li ◽  
Guangxi Zhao
Keyword(s):  
Surface Roughness ◽  
Energy Consumption ◽  
Tool Wear ◽  
Specific Energy ◽  
Machine Tools ◽  
Prediction Models ◽  
Specific Energy Consumption ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Support Vector

The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

Machine Specific Energy Consumption Analysis for CNC-Milling Toolpaths

2015 ◽  
Author(s):  
Sushrut Pavanaskar ◽  
Sara McMains
Keyword(s):  
Energy Consumption ◽  
Removal Rate ◽  
Energy Requirement ◽  
Specific Energy Consumption ◽  
Cnc Machining ◽  
Machine Construction ◽  
Cnc Milling ◽  
Cnc Machine ◽  
Energy Consumption Model ◽  
Consumption Model

This paper describes our work on analyzing and modeling energy consumption in CNC machining with an emphasis on the geometric aspects of toolpaths. We address effects of geometric and other aspects of toolpaths on energy consumed in machining by providing an advanced energy consumption model for CNC machining. We performed several controlled machining experiments to isolate, identify, and analyze the effects of various aspects of toolpaths (such as path parameters, angular change, etc.) on energy consumption. Based on our analyses, we developed an analytical energy consumption model for CNC machining that, along with the commonly used input of material removal rate (MRR), incorporates the effects of geometric toolpath parameters as well as effects of machine construction when estimating energy requirements for a toolpath. We also developed a simple web-based software interface to our model, that, once customized for a particular CNC machine, provides energy requirement estimates for a toolpath given its G/M code. Such feedback can help process planners and CNC machine operators make informed choices when generating/selecting toolpath alternatives using commercial CAM software.

scholarly journals Feature-based sequencing optimization method for minimizing non-cutting energy consumption for CNC machine tools

2021 ◽  
Author(s):  
Chunhua Feng ◽  
Yugui Huang ◽  
Yilong Wu ◽  
Jingyang Zhang
Keyword(s):  
Energy Consumption ◽  
Orthogonal Experiment ◽  
Specific Energy Consumption ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Processing Stage ◽  
Processing Sequence ◽  
The Impact

Abstract There is variety scheme when a part with multiple features is processed in CNC machines, and hence, different feature sequencing during processing affects not only productivity but also energy consumption. This paper concentrates on the energy-saving strategy by optimizing the feature processing sequence in the part processing stage through reducing the energy consumption of the non-cutting process. The detailed energy model is established considering rapid feed and general feed path in the X, Y, Z+, Z- directions for analyzing the impact of processing feature sorting on reducing the energy consumption of parts processing. The feature sequencing optimization is carried out under the condition of fixed cutting parameters for specific machining features to better reveal the sequence influence on energy consumption and non-cutting time. Meanwhile, the energy consumption of the non-cutting of parts specifically includes the empty pass and an automatic tool change model, while the normal feed and the rapid feed are established in different moving axis, respectively. Based on the developed model, the genetic algorithm is used to solve the optimal processing sequence and the lowest processing energy consumption. Finally, a cutting orthogonal experiment is executed to collect energy consumption data, analyze the data and fit the data to establish a specific energy consumption model for each processing stage. A case study of a part with nine features is used to optimize sequencing, which shows the effectiveness and validity of the proposed method.

Using the Segmented Iterative Learning Control Method to Generate Volumetric Error-Compensated Part Programs for Three-Axis CNC Milling Machine Tools

2018 ◽  
Vol 2 (3) ◽  
pp. 53 ◽  
Author(s):  
Ying-Chen Lu ◽  
Syh-Shiuh Yeh
Keyword(s):  
Error Compensation ◽  
Iterative Learning Control ◽  
Machine Tools ◽  
Tool Path ◽  
Cnc Milling ◽  
Volumetric Error ◽  
Cnc Milling Machine ◽  
Working Volume ◽  
Part Program ◽  
Rate Of Improvement

This study proposes using the iterative learning control method to adjust the volumetric error-compensated tool path, where the working volume motion accuracy of three-axis computerized numerical control (CNC) milling machine tools is increased by segmented modification of the part program. As the three-axis CNC milling machine tools generally have volumetric error of working volume, this study refers to the measured and established table of volumetric errors and uses the method of the modifying part program for volumetric error compensation of machine tools. This study proposes using part-program single-block positioning segmented for volumetric error compensation, as the generated compensated part program with multiple compensated blocks can effectively compensate the volumetric error of working volume in the tool moving process. In terms of the compensated tool path computing method, this study uses the iterative learning control (ILC) method and refers to compensated tool path and volumetric errors along the compensated tool path for iterative computation. Finally, a part program with multiple blocks is modified by the converged optimal compensated tool path, in order that the modified part program has higher-precision volumetric error compensation effect. The simulation result shows that the rate of improvement of error of the volumetric error compensation method proposed in this study is 70%. The result of cutting tests shows that the average rate of improvement of the straightness error of the test workpiece is 60%, while the average rate of improvement of height error is 80%. Therefore, the results of simulation and cutting tests can prove the feasibility of using the ILC method for segmented modification of the volumetric error-compensated part programs proposed in this study.

scholarly journals Calculation and Estimation of Surface Roughness and Energy Consumption in Milling of 6061 Alloy

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Burak Öztürk ◽  
Fuat Kara
Keyword(s):  
Surface Roughness ◽  
Energy Consumption ◽  
Cutting Parameters ◽  
Coolant Fluid ◽  
Power Indices ◽  
Cnc Milling ◽  
Dry Cutting ◽  
Subsequent Increase ◽  
Industrial Enterprises ◽  
Cnc Milling Machine

The best surface quality that can be achieved in manufactured products has become the main goal of industrial enterprises in recent years. Due to the subsequent increase in energy consumption costs from rising energy efficiency rates, manufacturers are contributing to this issue by applying advanced design functions for their machines. In line with the same objective, this study investigated the machinability of 6061 aluminum alloy, which has a high throughput rate and low machinability featuring built up edge. The aim of the research was to optimize the cutting parameters for minimum surface roughness (Ra) and energy consumption (EC) using a CNC milling machine. At the same time, measurements of power indices (A) of both the spindle and the X-axis motors were carried out with the goal of improved chip removal as compared to literature studies. The experiment was designed according to the Taguchi L16 (21 × 43) orthogonal index. Four different cutting speeds (60, 120, 180, and 240 m/min), feed rates (0.10, 0.15, 0.20, and 0.25 mm/rev), and cutting depths (0.5, 0.10, 0.15, and 0.20 mm) and two different cooling methods (coolant fluid and dry cutting) were selected as cutting parameters.

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

2021 ◽  
Vol 13 (24) ◽  
pp. 13918
Author(s):  
Jianhua Cao ◽  
Xuhui Xia ◽  
Lei Wang ◽  
Zelin Zhang ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Machine Tools ◽  
Prediction Method ◽  
Cnc Milling ◽  
Cnc Machine ◽  
The Sustainable Development ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

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.

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