Multi-objective integrated optimization of tool geometry angles and cutting parameters for machining time and energy consumption in NC milling

2021 ◽  
Author(s):  
Junhua Zhao ◽  
Li Li ◽  
Hua Nie ◽  
Xingzheng Chen ◽  
Jiwei Liu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Cutting Parameters ◽  
Tool Geometry ◽  
Machining Time ◽  
Integrated Optimization ◽  
Multi Objective ◽  
Nc Milling ◽  
Time And Energy

Minimizing the Energy Consumption of Holes Machining Integrating the Optimization of Tool Path and Cutting Parameters on CNC Machines

2021 ◽  
Author(s):  
Chunhua Feng ◽  
Xiang Chen ◽  
Jingyang Zhang ◽  
Yugui Huang ◽  
Zibing Qu
Keyword(s):  
Energy Consumption ◽  
Path Planning ◽  
Ant Colony Algorithm ◽  
Tool Path ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Tool Path Planning ◽  
Machining Time ◽  
Cnc Machines ◽  
Multi Objective

Abstract The application of sustainable manufacturing technologies is the new challenge faced by enterprises, industries, and researchers under the background of supporting carbon peak and carbon neutral. This paper studies how to reduce the energy consumption of holes machining through optimizing tool path and cutting parameters simultaneously. The integrated optimization methodology can further reduce the energy consumption comparing with optimizing the tool path or cutting parameters separately. Firstly, the energy model of holes machining is established based on machine tools’ energy composition, tool path planning, and process parameters. Due to tool path planning as air cutting process has big relationship with reducing energy, especially for holes group with a big proportion in the whole process. The tool path of holes processing is optimized by the improved ant colony algorithm to solve the issue considering the distance from one hole to the next hole. Based on this optimized path, a multi-objective optimization model for hole cutting parameters is established, considering the spindle speed and feed rate as the optimization variables and machining time, energy consumption, and surface roughness as the objective function. The non-dominated sorting genetic algorithm (NSGA-Ⅱ) is employed to solve the multi-objective optimization problem of holes machining. The case study with 50 holes is used to testify the application of the proposed method to provide the practical energy efficiency strategy for holes group or multi-hole parts on CNC machines assisting in achieving sustainable production in manufacturing sectors.

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

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.

A Methodology of Off-Line Optimization for NC Milling Process

2006 ◽  
Vol 315-316 ◽  
pp. 1-5 ◽  
Author(s):  
Ying Xue Yao ◽  
Chang Qing Liu ◽  
Jian Guang Li ◽  
H.J. Jing ◽  
S.D. Chen
Keyword(s):  
End Milling ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Milling Process ◽  
Machining Process ◽  
Machining Time ◽  
Cutting Power ◽  
Nc Program ◽  
Nc Milling ◽  
Control Technologies

Traditional adaptive control technologies in machining process optimization are limited in applications because they depend much on sensors, controllers and other hardware. An off-line optimization method for end milling process with constant cutting power is presented. On taking advantage of virtual machining which simulates milling process, acquires cutting parameters and predicts cutting forces, method taking constant cutting power as an objective is discussed to optimize feed rates and cutting speeds. Based on optimal result, the feed rates and spindle revolutions in NC program are re-scheduled. Controlled milling experiments show that machining time is reduced and machining stability is improved by using the optimized NC program.

scholarly journals Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach

2018 ◽  
Vol 160 ◽  
pp. 126-140 ◽  
Author(s):  
Luoke Hu ◽  
Renzhong Tang ◽  
Ying Liu ◽  
Yanlong Cao ◽  
Ashutosh Tiwari
Keyword(s):  
Energy Consumption ◽  
Machining Time ◽  
Multi Objective

Trade-off analysis between machining time and energy consumption in impeller NC machining

2017 ◽  
Vol 43 ◽  
pp. 164-170 ◽  
Author(s):  
Oscar Velásquez Arriaza ◽  
Dong-Won Kim ◽  
Dong Yoon Lee ◽  
Mohd. Azlan Suhaimi
Keyword(s):  
Energy Consumption ◽  
Nc Machining ◽  
Machining Time ◽  
Trade Off ◽  
Time And Energy

scholarly journals Analysis and Multi-Objective Optimization for Reducing Energy Consumption and Improving Surface Quality during Dry Machining of 304 Stainless Steel

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4693
Author(s):  
Feilong Du ◽  
Lin He ◽  
Haisong Huang ◽  
Tao Zhou ◽  
Jinxing Wu
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Energy Consumption ◽  
Surface Quality ◽  
Feed Rate ◽  
Cutting Parameters ◽  
304 Stainless Steel ◽  
Multi Objective Optimization ◽  
Multi Objective

Cutting quality and production cleanliness are main aspects to be considered in the machining process, and determining the optimal cutting parameters is a significant measure to reduce energy consumption and optimize surface quality. In this paper, 304 stainless steel is adopted as the research objective. The regression models of the specific cutting energy, surface roughness, and microhardness are constructed and the inherent influence mechanism between cutting parameters and output responses are analyzed by analysis of variance (ANOVA). The desirability analysis method is introduced to perform the multi-objective optimization for low energy consumption (LEC) mode and low surface roughness (LSR) mode. Optimal combination of process parameters with composite desirability of 0.925 and 0.899 are obtained in such two modes respectively. As indicated by the results of multi-objective genetic algorithm (MOGA), genetic algorithm (GA) combined with weighted-sum-type objective function and experiment, the relative deviation values are within 10%. Moreover, the results also reveal that the feed rate is the most significant factor affecting the three responses, while the correlation of cutting depth is less noticeable. The effect of low feed rate on microhardness is primarily related to the mechanical load caused by extrusion, and the influence at high feed rate is determined by plastic deformation.

Sign in / Sign up

Export Citation Format

Share Document