Energy Optimization for Milling 304L Steel using Artificial Intelligence Methods

IF 1 Q4 ENGINEERING, MECHANICAL

International Journal of Automotive and Mechanical Engineering Pub Date : 2022-09-30 DOI:10.15282/ijame.19.3.2022.05.0765

Kamel Bousnina, Anis Hamza, N. Ben yahia

{"title":"Energy Optimization for Milling 304L Steel using Artificial Intelligence Methods","authors":"Kamel Bousnina, Anis Hamza, N. Ben yahia","doi":"10.15282/ijame.19.3.2022.05.0765","DOIUrl":null,"url":null,"abstract":"With increased production and productivity in modern industry, particularly in the automotive, aeronautical, agro-food, and other sectors, the consumption of manufacturing energy is rapidly increasing, posing additional precautions and large investments to industries to reduce energy consumption at the manufacturing system level. This research proposes a novel energy optimisation using a response surface methodology (RSM) with artificial neural network (ANN) for machining processes that saves energy while improving productivity.The feed rate was discovered to be the most influential factor in this study, accounting for 84.13 percent of total energy consumed. Furthermore, it has been established that as the material removal rate (MRR) increases, energy efficiency (EE) declines. This optimization of cutting conditions gives us the optimal values of cutting speed Vc = 129.37 m/min, feed rate f = 0.098 mm/rev and depth of cut ap = 0.5 mm. This approach will allow us to decrease the total energy consumed (Etc) by 49.74 % and increase the energy efficiency (EE) by 13.63 %.","PeriodicalId":13935,"journal":{"name":"International Journal of Automotive and Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Automotive and Mechanical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15282/ijame.19.3.2022.05.0765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}

引用次数: 7

Abstract

With increased production and productivity in modern industry, particularly in the automotive, aeronautical, agro-food, and other sectors, the consumption of manufacturing energy is rapidly increasing, posing additional precautions and large investments to industries to reduce energy consumption at the manufacturing system level. This research proposes a novel energy optimisation using a response surface methodology (RSM) with artificial neural network (ANN) for machining processes that saves energy while improving productivity.The feed rate was discovered to be the most influential factor in this study, accounting for 84.13 percent of total energy consumed. Furthermore, it has been established that as the material removal rate (MRR) increases, energy efficiency (EE) declines. This optimization of cutting conditions gives us the optimal values of cutting speed Vc = 129.37 m/min, feed rate f = 0.098 mm/rev and depth of cut ap = 0.5 mm. This approach will allow us to decrease the total energy consumed (Etc) by 49.74 % and increase the energy efficiency (EE) by 13.63 %.

查看原文本刊更多论文

基于人工智能方法的304L钢铣削能耗优化

随着现代工业，特别是汽车、航空、农业食品和其他部门的产量和生产率的提高，制造能源的消耗正在迅速增加，为减少制造系统一级的能源消耗，对工业提出了额外的预防措施和大量投资。本研究提出了一种新的能量优化方法，利用响应面方法(RSM)和人工神经网络(ANN)在加工过程中节省能源，同时提高生产率。在本研究中，进料速率是影响最大的因素，占总能量消耗的84.13%。此外,它已被证实为材料去除率(MRR)增加,能源效率(EE)下降。通过对切削条件的优化，得到切削速度Vc = 129.37 m/min，进给速度f = 0.098 mm/rev，切削深度ap = 0.5 mm的最优值。这种方法将使我们能够减少总能量消耗(等)49.74%,增加能源效率(EE) 13.63%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Automotive and Mechanical Engineering ENGINEERING, MECHANICAL-

CiteScore

2.40

自引率

10.00%

发文量

审稿时长

20 weeks

期刊介绍： The IJAME provides the forum for high-quality research communications and addresses all aspects of original experimental information based on theory and their applications. This journal welcomes all contributions from those who wish to report on new developments in automotive and mechanical engineering fields within the following scopes. -Engine/Emission Technology Automobile Body and Safety- Vehicle Dynamics- Automotive Electronics- Alternative Energy- Energy Conversion- Fuels and Lubricants - Combustion and Reacting Flows- New and Renewable Energy Technologies- Automotive Electrical Systems- Automotive Materials- Automotive Transmission- Automotive Pollution and Control- Vehicle Maintenance- Intelligent Vehicle/Transportation Systems- Fuel Cell, Hybrid, Electrical Vehicle and Other Fields of Automotive Engineering- Engineering Management /TQM- Heat and Mass Transfer- Fluid and Thermal Engineering- CAE/FEA/CAD/CFD- Engineering Mechanics- Modeling and Simulation- Metallurgy/ Materials Engineering- Applied Mechanics- Thermodynamics- Agricultural Machinery and Equipment- Mechatronics- Automatic Control- Multidisciplinary design and optimization - Fluid Mechanics and Dynamics- Thermal-Fluids Machinery- Experimental and Computational Mechanics - Measurement and Instrumentation- HVAC- Manufacturing Systems- Materials Processing- Noise and Vibration- Composite and Polymer Materials- Biomechanical Engineering- Fatigue and Fracture Mechanics- Machine Components design- Gas Turbine- Power Plant Engineering- Artificial Intelligent/Neural Network- Robotic Systems- Solar Energy- Powder Metallurgy and Metal Ceramics- Discrete Systems- Non-linear Analysis- Structural Analysis- Tribology- Engineering Materials- Mechanical Systems and Technology- Pneumatic and Hydraulic Systems - Failure Analysis- Any other related topics.