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.

An Optimization Method of the Heavy Machine Cutting Parameters

2012 ◽  
Vol 544 ◽  
pp. 38-43
Author(s):  
Chao Deng ◽  
Chao Ma ◽  
Yao Xiong ◽  
Yuan Hang Wang
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Mechanical Vibration ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Engineering Problem ◽  
Machining Process ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Mixed Genetic Algorithm

This paper analyzes the machining process of CNC machine tools, and builds an optimization model of the machining process parameters based on the mechanical vibration and the operational research. The model mixed genetic algorithm and particle swarm optimization (PSO) is built. It proposes an optimization algorithm that has higher convergence precision and execute ability to solve engineering problem with nonlinear and multi-extremum. According to case study, it proves the correctness of the model and the efficiency and high-performance nature of the designed optimization algorithm. It also appears the efficiency to solve the common engineering problems by the intelligent optimization algorithms.

scholarly journals Experimental and Numerical Investigation of Hard Rock Breakage by Indenter Impact

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hongxiang Jiang ◽  
Zhiyuan Cai ◽  
Ouguo Wang ◽  
Deguang Meng
Keyword(s):  
Energy Consumption ◽  
Impact Velocity ◽  
Impact Energy ◽  
Specific Energy ◽  
Specific Energy Consumption ◽  
Decisive Role ◽  
Rock Breakage ◽  
Radial Cracks ◽  
The Impact ◽  
Indenter Shape

To investigate the effect of indenter shape, impact energy, and impact velocity on the rock breakage performance, a test device for rock fragmentation by indenter impact was developed to obtain the rock breakage volume, depth, and area under different impact conditions. By comparing the rock breakage volume, depth, area, and specific energy consumption, the results show that indenter shape has a greater influence on the rock breakage performance than that of the impact velocity with the same impact energy, and impact energy plays a decisive role in rock breakage performance with an identical indenter shape and impact velocity. For the lowest to highest specific energy consumption, the order of indenter shape is cusp-conical, warhead, hemispherical, spherical-arc, and flat-top under the same impact energy and velocity, but the cusp-conical indenter is damaged after several impacts. The rock breakage volume, depth, and area all increase with the increase in impact energy, but the effect of the impact velocity could be ignored under the same impact energy. In addition, the rock breakage features of the numerical simulation and experiments are similar, which show that the crushing zone close to the indenter impact point is mainly caused by the high compressive stress, and then radial cracks are caused by the accumulative energy release. The findings of this study will contribute to progress in the performance and efficiency for percussive rock drilling.

scholarly journals Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel

Metals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 217 ◽  
Author(s):  
Yu Su ◽  
Guoyong Zhao ◽  
Yugang Zhao ◽  
Jianbing Meng ◽  
Chunxiao Li
Keyword(s):  
Energy Consumption ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
Relational Analysis ◽  
304 Austenitic Stainless Steel ◽  
Grey Relational ◽  
Optimization Of Cutting Parameters

Energy conservation and emission reduction is an essential consideration in sustainable manufacturing. However, the traditional optimization of cutting parameters mostly focuses on machining cost, surface quality, and cutting force, ignoring the influence of cutting parameters on energy consumption in cutting process. This paper presents a multi-objective optimization method of cutting parameters based on grey relational analysis and response surface methodology (RSM), which is applied to turn AISI 304 austenitic stainless steel in order to improve cutting quality and production rate while reducing energy consumption. Firstly, Taguchi method was used to design the turning experiments. Secondly, the multi-objective optimization problem was converted into a simple objective optimization problem through grey relational analysis. Finally, the regression model based on RSM for grey relational grade was developed and the optimal combination of turning parameters (ap = 2.2 mm, f = 0.15 mm/rev, and v = 90 m/s) was determined. Compared with the initial turning parameters, surface roughness (Ra) decreases 66.90%, material removal rate (MRR) increases 8.82%, and specific energy consumption (SEC) simultaneously decreases 81.46%. As such, the proposed optimization method realizes the trade-offs between cutting quality, production rate and energy consumption, and may provide useful guides on turning parameters formulation.

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.

scholarly journals Modelling of Variable Energy Consumption for CNC Machine Tools

Procedia CIRP ◽  
2021 ◽  
Vol 98 ◽  
pp. 247-251
Author(s):  
Shailendra Pawanr ◽  
Girish Kant Garg ◽  
Srikanta Routroy
Keyword(s):  
Energy Consumption ◽  
Machine Tools ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Variable Energy

scholarly journals New approaches to assessing the impact of stable condensate on the main indicators of marketable oil on the example of the “Novy Port” oil and gas condensate field

2021 ◽  
Vol 6 (1) ◽  
pp. 88-93
Author(s):  
I. M. Zamanov ◽  
P. N. Afanasyev ◽  
A. V. Kim ◽  
S. A. Vershinin ◽  
A. N. Blyablyas
Keyword(s):  
Energy Consumption ◽  
Specific Energy ◽  
Oil And Gas ◽  
Saturated Vapor ◽  
Specific Energy Consumption ◽  
Gas Condensate ◽  
Physicochemical Composition ◽  
Key Indicator ◽  
Pumping Equipment ◽  
The Impact

This article reflects the main points of the influence of light hydrocarbons on the physicochemical composition and properties of marketable oil of the Novy Port oil and gas condensate field, describes the uncertainties that should be taken into account by the time gas reserves are involved. For a specific task, the risks of influencing the key indicator according to GOST R – 51858-2002 (saturated vapor pressure) were removed. For the first time, a shift of the critical point of paraffin formation has been revealed. The result of the work will be the predicted behavior of the fluid when gas reserves are involved, as well as a decrease in energy consumption for heating the pipeline of external transport, an increase in the cleanup period, a decrease in the specific energy consumption (specific energy consumption) of pumping equipment, as a result – a decrease in operating costs.

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