Optimal Workpiece Setup for Time-Efficient and Energy-Saving Five-Axis Machining of Freeform Surfaces

2016 ◽  
Vol 139 (5) ◽  
Author(s):  
Ke Xu ◽  
Kai Tang
Keyword(s):  
Energy Consumption ◽  
Alternative Energy ◽  
Tool Path ◽  
Machining Time ◽  
Freeform Surfaces ◽  
Significant Saving ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Workpiece Setup ◽  
Five Axis

Energy consumption in five-axis machining of freeform surfaces can be considerably large for large-size parts. This paper presents a study on how to setup the workpiece in order to minimize the energy consumption without modifying the toolpath itself. For an arbitrary freeform workpiece, the way how it is setup on the working table highly affects the machine's kinematic behavior, which dominates the overall processing time and energy consumption. Taking into account the speed and acceleration limit of each axis of the machine, we first establish the energy consumption model as a function of the workpiece setup. However, this original model involves certain critical physically pertinent coefficients (such as the moment of inertial of a rotary table) which are usually unavailable in practice. Instead, by exploring insightful geometric characteristics of the five-axis machine, an alternative energy consumption model is established which is independent of those hard-to-obtain coefficients. A simple algorithm is then designed to optimize this model. Both computer simulations and physical cutting experiments demonstrate that, when compared with an arbitrary setup, the optimized workpiece setup is able to achieve a significant saving (as much as 50%) in both energy consumption and total machining time, both using a same tool path.

scholarly journals A Systematic Method of Optimization of Machining Parameters Considering Energy Consumption, Machining time and Surface Roughness with Experimental Analysis

2021 ◽  
Author(s):  
Chunhua Feng ◽  
Haohao Guo ◽  
Jingyang Zhang ◽  
Yugui Huang ◽  
Shi Huang
Keyword(s):  
Experimental Data ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Systematic Method ◽  
Machining Time ◽  
Cutting Energy ◽  
Energy Consumption Model ◽  
Consumption Model

Abstract For improving energy efficiency of machining process, extensive studies have focused on how to establish energy consumption model and optimize cutting parameters. However, the existing methods lack a systematic method to promote the widespread use of energy efficiency methods in the industry. This paper proposes a systematic method integrating energy model, experiment design, and multi-objective optimization model. Firstly, the energy model is established considering cutting energy and non-cutting energy. Then, the orthogonal experiment is designed with the three levels of four factors of spindle speed, feed speed, cutting depth, and cutting width in the X and Y cutting directions. The data of energy consumption, surface quality and machining time are obtained to study the effects of different cutting elements and cutting directions. Meanwhile, the standby, spindle idling, feed, SEC, material cutting and idling feed models of the CNC machine tools are established based on the experimental data. In addition, for verifying the accuracy of the established energy consumption model, five sets of experimental data are tested that show the prediction accuracy can reach 99.4%. Finally, a multi-objective optimization model for high efficiency and energy saving of processing process is establishes to optimize the cutting parameters from the three perspectives of energy consumption, processing time and surface quality. Combining the case of milling with constraints including machine tool performance, tool life, processing procedures, and processing requirements, the Pareto solution set is used to solve the Pareto of the target model. Through drawing a three-dimensional needle graph and two-dimensional histogram, the optimal cutting parameter combination for rough machining and semi-finish machining are provided, assisting in promoting the application of the sustainable techniques in the industry.

scholarly journals Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 655
Author(s):  
Huanhuan Zhang ◽  
Jigeng Li ◽  
Mengna Hong
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Gradient Boosting ◽  
Paper Machine ◽  
Steam Consumption ◽  
Tissue Paper ◽  
Paper Machines ◽  
Energy Consumption Model ◽  
Extreme Gradient Boosting ◽  
Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

Research on Five-Axis Dual-NURBS Adaptive Interpolation Algorithm for Flank Milling

2010 ◽  
Vol 443 ◽  
pp. 330-335 ◽  
Author(s):  
Yu Han Wang ◽  
Jing Chun Feng ◽  
Sun Chao ◽  
Ming Chen
Keyword(s):  
Tool Path ◽  
Flank Milling ◽  
Interpolation Algorithm ◽  
Machining Time ◽  
Surface Machining ◽  
Tool Axis ◽  
Scanning Area ◽  
Nurbs Interpolation ◽  
Milling Method ◽  
Five Axis

In order to exploit the advantages of five-axis flank milling method for space free surface machining to the full, a definition of non-equidistant dual-NURBS tool path is presented first. On this basis, the constraint of velocity of points on the tool axis and the constraint of scanning area of the tool axis are deduced. Considering both of these constraints, an adaptive feed five-axis dual-NURBS interpolation algorithm is proposed. The simulation results show that the feedrate with the proposed algorithm satisfies both of the constraints and the machining time is reduced by 38.3% in comparison with the constant feed interpolator algorithm.

scholarly journals Comprehensive Energy Consumption Model for Unmanned Aerial Vehicles, Based on Empirical Studies of Battery Performance

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 58383-58394 ◽  
Author(s):  
Hasini Viranga Abeywickrama ◽  
Beeshanga Abewardana Jayawickrama ◽  
Ying He ◽  
Eryk Dutkiewicz
Keyword(s):  
Energy Consumption ◽  
Unmanned Aerial Vehicles ◽  
Empirical Studies ◽  
Aerial Vehicles ◽  
Energy Consumption Model ◽  
Consumption Model
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