scholarly journals Multi-Objective Optimization of Machining Parameters for Multi-Pass CNC Turning to Minimize Carbon Emissions, Energy, Noise and Cost

2021 ◽  
Vol 23 (1) ◽  
pp. 25-34
Author(s):  
Bening Maulina Fittamami ◽  
Eko Pujiyanto ◽  
Yusuf Priyandari
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Manufacturing Process ◽  
Production Cost ◽  
Sustainable Manufacturing ◽  
Machining Process ◽  
Total Carbon ◽  
Machining Parameters ◽  
Cnc Turning ◽  
Multi Objective

Global warming is a huge environmental issue today. This is due to the high level of world carbon emissions. The manufacturing process accounts for 30% of the world's carbon emissions production.  Sustainable manufacturing is necessary to implement to reduce carbon emission levels caused by the manufacturing process. There are three aspects of sustainable manufacturing, namely environmental aspects, economic aspects, and social aspects. These three aspects can be implemented in the machining process by optimizing machining parameters in multi-pass CNC turning. This research aims to optimize CNC turning machining parameters by considering energy consumption, carbon emissions, noise, and production cost. The model is solved using a Multi-objective Genetic Algorithm in Matlab 2016b then the transformation and weighting functions are carried out from the feasible value. Based on the optimization results, the total energy consumption value obtained is 2.50 MJ; total production cost is $ 2.19; total carbon emissions are 5.97 kgCO2, and noise is 236, 19 dB. The sensitivity analysis exhibits the machining parameters that affect the objective function: The cutting speed parameter and the feed rate parameter. This model can be used to improve the manufacturing process and support sustainable manufacturing.

scholarly journals Multi-Objective Optimization Model of Multi-Pass Turning Operations to Minimize Energy, Carbon Emissions, and Production Costs

2020 ◽  
Vol 21 (2) ◽  
pp. 213-224
Author(s):  
Aprilia Dityarini ◽  
Eko Pujiyanto ◽  
I Wayan Suletra
Keyword(s):  
Carbon Emissions ◽  
Optimization Model ◽  
Sustainable Manufacturing ◽  
Cutting Parameters ◽  
Production Costs ◽  
Machining Process ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Decision Variables ◽  
Optimal Values

Sustainable manufacturing aspects are environmental, economic, and social. These aspects can be applied to an optimization model in the machining process. An optimization model is needed to determine the optimum cutting parameters. This research develops a multi-objective optimization model that can optimize cutting parameters on a multi-pass turning. Decision variables are cutting parameters multi-pass turning. This research has three objective functions for minimizing energy, carbon emissions, and costs. Three functions are searched for optimal values using the GEKKO.  A numerical example is given to show the implementation of the model and solved using GEKKO and Interior Point Optimizer (IPOPT). The results of optimization indicate that the model can be used to optimize the cutting parameters.

Multi-Objective Fleet Assignment Problem Based on Self-Adaptive Genetic Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2895-2900 ◽  
Author(s):  
Xiao Yang ◽  
Bo Jiang
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Assignment Problem ◽  
Network Capacity ◽  
Total Carbon ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective ◽  
Route Network ◽  
Assignment Model ◽  
Key Factor

Since the beginning of the twenty-first century, energy conservation has become the theme of the development of the world. China government set the emissions-reduction targets in various industries on the 12th Five-Year Plan. And the airlines were committed to reduce their carbon emissions. From an operational perspective, the airline model assignment problem is a key factor of the total carbon emissions on the entire route network. But the traditional aircraft assignment models approach did not account for this purpose to reduce carbon emissions. By constructing the multi-objective optimization models consider carbon emissions assignment model using a genetic algorithm, numerical example shows that the model is able to meet all aspects demand which include meeting route network capacity demand, minimizing operating costs and reducing total aircraft fleet carbon emissions.

scholarly journals Analysis of total carbon emission of the logistics industry in Guangdong and solutions to emission reduction

2021 ◽  
Vol 245 ◽  
pp. 01020
Author(s):  
Aixia Xu ◽  
Xiaoyong Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Utilization ◽  
Total Carbon ◽  
Utilization Rate ◽  
Low Carbon ◽  
Logistics Industry ◽  
Direct Energy ◽  
Reduce Emissions

The input-output method is employed in this study to measure the total carbon emission of the logistics industry in Guangdong. The findings revealed that the carbon emission of direct energy consumption of the logistics industry in Guangdong is far above the actual carbon emissions, the second and third industries play a significant role in carbon emission of indirect energy consumption in the logistics industry in Guangdong. To reduce energy consumption and carbon emissions in Guangdong, it is not only important to control the carbon emissions in the logistics industry, but strengthen carbon emission detection in relevant industries, improve the energy utilization rate and reduce emissions in other industries, and move towards low-carbon sustainable development.

Multi-Objective Optimization for the Micro-Milling Process With Adaptive Data Modeling

2011 ◽  
Author(s):  
Xinyu Liu ◽  
Weihang Zhu ◽  
Victor Zaloom
Keyword(s):  
Surrogate Model ◽  
Data Modeling ◽  
Milling Process ◽  
Machining Process ◽  
Micro Milling ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Study ◽  
Machining Parameter

This paper presents a multi-objective optimization study for the micro-milling process with adaptive data modeling based on the process simulation. A micro-milling machining process model was developed and verified through our previous study. Based on the model, a set of simulation data was generated from a factorial design. The data was converted into a surrogate model with adaptive data modeling method. The model has three input variables: axial depth of cut, feed rate and spindle speed. It has two conflictive objectives: minimization of surface location error (which affects surface accuracy) and minimization of total tooling cost. The surrogate model is used in a multi-objective optimization study to obtain the Pareto optimal sets of machining parameters. The visual display of the non-dominated solution frontier allows an engineer to select a preferred machining parameter in order to get a lowest cost solution given the requirement from tolerance and accuracy. The contribution of this study is to provide a streamlined methodology to identify the preferred best machining parameters for micro-milling.

scholarly journals Designing a sustainable biofuel supply chain by considering carbon policies: a case study in Iran

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Naeme Zarrinpoor ◽  
Aida Khani
Keyword(s):  
Trade Policy ◽  
Carbon Emissions ◽  
Fossil Fuels ◽  
Biofuel Production ◽  
Total Carbon ◽  
Multi Objective ◽  
Switch Grass ◽  
Regulatory Policies ◽  
The Social

Abstract Background Carbon emissions and global warming have increased as a result of population growth and greater usage of fossil fuels. Finding a long-term replacement for fossil fuels, such as biofuels, has become a major problem for energy supply management in recent years. Sustainability must be addressed as a key problem in building biofuel supply chains (BSCs), given the pressing need for societies to limit environmental consequences and promote social responsibility of company activities. Various modeling frameworks have been established so far to design a BSC. At the same time, no research exists that examines both the sustainable development paradigm and the influence of various carbon regulatory policies on the strategic and operational decisions made by BSCs. Methods This study develops a multi-objective, multi-period, multi-echelon BSC from switch grass regarding the economic, environmental and social aspects of sustainability. Four carbon policies are taken into account when assessing the environmental aspect: the carbon cap, the carbon tax, the carbon trade, and the carbon offset. To solve the multi-objective model, the fuzzy interactive programming method is used, and the fuzzy best–worst method is used to weight the social objective components. Results An actual case study in Iran is studied to demonstrate the model’s applicability. Under various carbon policies, different network configurations are obtained based on the location of switch grass resources and installed facilities. Biofuel production and transportation activities account for approximately 28% and 51% of total carbon emissions, respectively, according to numerical results. Furthermore, these activities account for roughly 62% of overall expenses. In the suggested case study, implementing the carbon trade policy reduces carbon emissions by more than 30% while increasing total profit by about 27%. In comparison to other policies, the carbon trade policy has a substantial impact on enhancing social considerations. Overall, the carbon trade policy can greatly improve the economic and environmental components of sustainability without significantly decreasing in the social sustainability. Conclusions The proposed model can assist policymakers and governments in simultaneously optimizing BSC profitability, carbon emission reduction, and social concern.

scholarly journals A Composite Evaluation Model of Sustainable Manufacturing in Machining Process for Typical Machine Tools

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 110 ◽  
Author(s):  
Lishu Lv ◽  
Zhaohui Deng ◽  
Tao Liu ◽  
Linlin Wan ◽  
Wenliang Huang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Machine Tools ◽  
Manufacturing Industry ◽  
Orthogonal Experiment ◽  
Evaluation Model ◽  
Sustainable Manufacturing ◽  
Machining Process ◽  
Machining Parameters ◽  
Machining Processes ◽  
Carbon Efficiency

Machine tool is the basic manufacturing equipment in today’s mechanical manufacturing industry. A considerable amount of energy and carbon emission are consumed in machining processes, the realization of sustainable manufacturing of machine tools have become an urgent problem to be solved in the field of industry and academia. Therefore, five types of machine tools were selected for the typical machining processes (turning, milling, planning, grinding and drilling). Then the model of the energy efficiency, carbon efficiency and green degree model were established in this paper which considers the theory and experiment with the resource, energy and emission modeling method. The head frame spindle and head frame box were selected to verify the feasibility and practicability of the proposed model, based on the orthogonal experiment case of the key machining process. In addition, the influence rules of machining parameters were explored and the energy efficiency and green degree of the machine tools were compared. Finally, the corresponding strategies for energy conservation and emission reduction were proposed.

scholarly journals A Dynamic Multi-Swarm Particle Swarm Optimizer for Multi-Objective Optimization of Machining Operations Considering Efficiency and Energy Consumption

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2616
Author(s):  
Lijun Song ◽  
Jing Shi ◽  
Anda Pan ◽  
Jie Yang ◽  
Jun Xie
Keyword(s):  
Energy Consumption ◽  
Particle Swarm ◽  
Economic Benefits ◽  
Machining Parameters ◽  
Manufacturing Companies ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Multi Objective ◽  
Use Of Resources ◽  
Machining Operations

Facing energy shortage and severe environmental pollution, manufacturing companies need to urgently energy consumption, make rational use of resources and improve economic benefits. This paper formulates a multi-objective optimization model for lathe turning operations which aims to simultaneously minimize energy consumption, machining cost and cutting time. A dynamic multi-swarm particle swarm optimizer (DMS-PSO) is proposed to solve the formulation. A case study is provided to illustrate the effectiveness of the proposed algorithm. The results show that the DMS-PSO approach can ensure good convergence and diversity of the solution set. Additionally, the optimal machining parameters are identified by fuzzy comprehensive evaluation (FCE) and compared with empirical parameters. It is discovered that the optimal parameters obtained from the proposed algorithm outperform the empirical parameters in all three objectives. The research findings shed new light on energy conservation of machining operations.

scholarly journals Regular Neural Network Based Machining Parameters Optimization Model Of NC Machine Tool

2018 ◽  
Vol 232 ◽  
pp. 01006
Author(s):  
Sanping Wang ◽  
Junwen Chen ◽  
Wei Yan
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Machine Tool ◽  
Process Parameters ◽  
Machine Tools ◽  
Machining Process ◽  
Machining Parameters ◽  
Nc Machine Tool ◽  
Nc Machine

Energy consumption process is the basis for energy efficiency improvement of machine tools. Most of the existing researches focus on the static modelling of energy consumption of a machine tool; however, there are a few studies that paid attention to that how process parameters influence the energy consumption of machine tools during processing. It is noted that the process parameters can be selected to reduce energy consumption during machining processes without additional investment. In this paper, a characteristic energy consumption model for NC machine tool was proposed. Then, the mapping rule between process parameters and energy consumption of machine tool was studied, and the model was solved with the regular neural network (RNN). Finally, the result was verified with an experiment of milling the surface of aluminium block, which can effectively improve the energy efficiency of machine tool. The experiment results are shown that regular neural network is used to optimize the process parameters and process the same machining characteristics; we analyze the in machining process of machine tool based on the three cutting parameters, and then, a model of energy consumption. We employ to learn, and use this trained model to select optimal parameters.

Research on Regional Differences of Carbon Productivity in China

2014 ◽  
Vol 962-965 ◽  
pp. 1866-1870
Author(s):  
Jing Zhang ◽  
Jian Feng Cai
Keyword(s):  
Carbon Dioxide ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Regional Differences ◽  
Total Carbon ◽  
Eastern Region ◽  
The West ◽  
Consumption Data ◽  
Policy Suggestions ◽  
Carbon Productivity

This paper calculates the total carbon emissions on the basis of energy consumption data of 29 provinces by using IPCC reference approach and analysis the emission structure of carbon dioxide. Carbon productivity is calculated and the interprovincial and regional differences are studied. The results show that the main source of emission structure of carbon dioxide is coal. Significant differences on carbon productivity exist in inter-provincial as well as the three regions, carbon productivity in the eastern region is the highest, followed by the middle, the west is the lowest. According to the result, relative policy suggestions are put forward in the last.

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