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Abstract

In manufacturing industries, the selection of machine parameters is a very complicated task in a time-bound manner. The process parameters play a primary role in confirming the quality, low cost of manufacturing, high productivity, and provide the source for sustainable machining. This paper explores the milling behavior of MWCNT/epoxy nanocomposites to attain the parametric conditions having lower surface roughness (Ra) and higher materials removal rate (MRR). Milling is considered as an indispensable process employed to acquire highly accurate and precise slots. Particle swarm optimization (PSO) is very trendy among the nature-stimulated metaheuristic method used for the optimization of varying constraints. This article uses the non-dominated PSO algorithm to optimize the milling parameters, namely, MWCNT weight% (Wt.), spindle speed (N), feed rate (F), and depth of cut (D). The first setting confirmatory test demonstrates the value of Ra and MRR that are found as 1:62 μm and 5.69 mm3/min, respectively and for the second set, the obtained values of Ra and MRR are 3.74 μm and 22.83 mm3/min respectively. The Pareto set allows the manufacturer to determine the optimal setting depending on their application need. The outcomes of the proposed algorithm offer new criteria to control the milling parameters for high efficiency.

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Authors and Affiliations

Prakhar Kumar Kharwar
1
Rajesh Kumar Verma
1
Nirmal Kumar Mandal
2
Arpan Kumar Mondal
2

  1. Department of Mechanical Engineering, Madan Mohan Malaviya University of Technology Gorakhpur, India.
  2. Department of Mechanical Engineering, National Institute of Technical Teachers’ Training and Research, Kolkata, India.
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Abstract

The paper presents a methodology for the optimization of a Brushless Direct Current motor (BLDC). In particular it is focused on multiobjective optimization using a genetic algorithm (GA) developed in Matlab/Optimization Toolbox coupled with Maxwell from ANSYS. Optimization process was divided into two steps. The aim of the first one was to maximize the RMS torque value and to minimize the mass. The second part of the optimization process was to minimize the cogging torque by selecting proper magnet angle. The paper presents the methodology and capabilities of scripting methods rather than specific optimization results for the applied geometry.

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Authors and Affiliations

R. Caramia
R. Piotuch
R. Pałka
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Abstract

Reactive distillation (RD) has already demonstrated its potential to significantly increase reactant conversion and the purity of the target product. Our work focuses on the application of RD to reaction systems that feature more than one main reaction. In such multiple-reaction systems, the application of RD would enhance not only the reactant conversion but also the selectivity of the target product. The potential of RD to improve the product selectivity of multiple-reaction systems has not yet been fully exploited because of a shortage of available comprehensive experimental and theoretical studies. In the present article, we want to theoretically identify the full potential of RD technology in multiple-reaction systems by performing a detailed optimisation study. An evolutionary algorithm was applied and the obtained results were compared with those of a conventional stirred tank reactor to quantify the potential of RD to improve the target product selectivity of multiple-reaction systems. The consecutive transesterification of dimethyl carbonate with ethanol to form ethyl methyl carbonate and diethyl carbonate was used as a case study.

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Authors and Affiliations

Tobias Keller
Bjoern Dreisewerd
Andrzej Górak
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Abstract

This paper presents a study on the dry turning of polyoxymethylene copolymer POM-C. The effect of five factors (cutting speed, feed rate, depth of cut, nose radius, and main cutting edge angle) on machinability is evaluated using four output parameters: surface roughness, tangential force, cutting power, and material removal rate. To do so, the study relies on three approaches: (i) Pareto statistical analysis, (ii) multiple linear regression modeling, and (iii) optimization using the genetic algorithm. To conduct the investigation, mathematical models are developed using response surface methodology based on the Taguchi L16 orthogonal array. The results indicate that feed rate, nose radius, and cutting edge angle significantly influence surface quality, while depth of cut, feed, and speed have a notable impact on other machinability parameters. The developed mathematical models have determination coefficients greater than or very close to 95%, making them very useful for the industry as they allow predicting response values based on the chosen cutting parameters. Finally, the optimization using the genetic algorithm proves to be promising and effective in determining the optimal cutting parameters to maximize productivity while improving surface quality.
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Authors and Affiliations

Tallal Hakmi
ORCID: ORCID
Amine Hamdi
ORCID: ORCID
Youssef Touggui
ORCID: ORCID
Aissa Laouissi
ORCID: ORCID
Salim Belhadi
ORCID: ORCID
Mohamed Athmane Yallese
ORCID: ORCID
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Abstract

We provide a detailed analysis of a unifying theoretical framework forinnovation and corporate dynamics that encompasses the Gibrat’s Law of Proportionate Effect and the Simon growth process as particular instances. The predictions of the model are derived in terms of (i) firm size distribution, (ii) the distribution of firm growth rates, and (iii-iv) the relationships between firm size and the mean and variance of firm growth rates. We test the model against data from the worldwide pharmaceutical industry and find its predictions tobe in good agreement with empirical evidence on all four dimensions. Our unifying stochastic framework can also be used to describe corporate dynamics in different industries and as a benchmark for discriminating among alternative data generating processes.

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Authors and Affiliations

Jakub Growiec
Fabio Pammolli
Massimo Riccaboni
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Abstract

In order to optimise the operation state of the distribution network in the presence of distributed generation (DG), to reduce network loss, balance load and improve power quality in the distribution system, a multi-objective fruit fly optimisation algorithm based on population Manhattan distance (pmdMOFOA) is presented. Firstly, the global and local exploration abilities of a fruit fly optimisation algorithm (FOA) are balanced by combining population Manhattan distance ( PMD) and the dynamic step adjustment strategy to solve the problems of its weak local exploration ability and proneness to premature convergence. At the same time, Chebyshev chaotic mapping is introduced during position update of the fruit fly population to improve ability of fruit flies to escape the local optimum and avoid premature convergence. In addition, the external archive selection strategy is introduced to select the best individual in history to save in external archives according to the dominant relationship amongst individuals. The leader selection strategy, external archive update and maintenance strategy are proposed to generate a Pareto optimal solution set iteratively. Lastly, an optimal reconstruction scheme is determined by the fuzzy decision method. Compared with the standard FOA, the average convergence algebra of a pmdMOFOA is reduced by 44.58%. The distribution performance of non-dominated solutions of a pmdMOFOA, MOFOA, NSGA-III and MOPSO on the Pareto front is tested, and the results show that the pmdMOFOA has better diversity. Through the simulation and analysis of a typical IEEE 33-bus system with DG, load balance and voltage offset after reconfiguration are increased by 23.77% and 40.58%, respectively, and network loss is reduced by 57.22%, which verifies the effectiveness and efficiency of the proposed method.
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Authors and Affiliations

Minan Tang
1
Kaiyue Zhang
1
Qianqian Wang
2
Haipeng Cheng
3
Shangmei Yang
1
ORCID: ORCID
Hanxiao Du
1

  1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, China
  2. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China
  3. CRRC Qingdao Sifang Co., Ltd. Qingdao, China
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Abstract

This article presents a new efficient optimization technique namely the Multi- Objective Improved Differential Evolution Algorithm (MOIDEA) to solve the multiobjective optimal power flow problem in power systems. The main features of the Differential Evolution (DE) algorithm are simple, easy, and efficient, but sometimes, it is prone to stagnation in the local optima. This paper has proposed many improvements, in the exploration and exploitation processes, to enhance the performance of DE for solving optimal power flow (OPF) problems. The main contributions of the DE algorithm are i) the crossover rate will be changing randomly and continuously for each iteration, ii) all probabilities that have been ignored in the crossover process have been taken, and iii) in selection operation, the mathematical calculations of the mutation process have been taken. Four conflicting objective functions simultaneously have been applied to select the Pareto optimal front for the multi-objective OPF. Fuzzy set theory has been used to extract the best compromise solution. These objective functions that have been considered for setting control variables of the power system are total fuel cost (TFC), total emission (TE), real power losses (RPL), and voltage profile (VP) improvement. The IEEE 30-bus standard system has been used to validate the effectiveness and superiority of the approach proposed based on MATLAB software. Finally, to demonstrate the effectiveness and capability of the MOIDEA, the results obtained by this method will be compared with other recent methods.
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Authors and Affiliations

Murtadha Al-Kaabi
1
ORCID: ORCID
Jaleel Al Hasheme
2
ORCID: ORCID
Layth Al-Bahrani
3
ORCID: ORCID

  1. Ministry of Education Baghdad, Iraq
  2. University Politehnica of Bucharest, Bucharest, Romania
  3. Al-Mustansiriyah University Baghdad, Iraq

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