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SURFACE MORPHOLOGY AND MACHINABILITY ASPECTS IN WIRE-EDM OF INCOLOY-800 SUPERALLOY: AN EXPERIMENTAL EVALUATION AND MICROSTRUCTURE ANALYSIS

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SURFACE REVIEW AND LETTERS 2022年第8期29卷 2250105-2250105页

作者： Singh, Ravi Pratap Kataria, Ravinder Abidi, Mustufa Haider Ranjan, Sudhir Gupta, Ashutosh Kumar Al-Ahmari, Abdulrahman Dr BR Ambedkar Natl Inst Technol Dept Ind & Prod Engn Jalandhar 144011 Punjab India NIFT Srinagar Srinagar 191132 Jammu & Kashmir India King Saud Univ Adv Mfg Inst Raytheon Chair Syst Engn Riyadh 11421 Saudi Arabia IIT Roorkee Dept Mech & Ind Engn Roorkee 247667 Uttarakhand India King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia

This study investigated various performance measures - material removal rate (MRR), surface roughness (SR), and dimensional deviation (DD) - in wire cut electrical discharge machining of the Incoloy-800 superalloy under the effect of purposefully varied process inputs. Superalloys comprise a bright category of alloys that possess excellent mechanical strength at elevated temperatures, superior surface stability, magnificent creep resistance, and corrosion and oxidation resistance. Analysis of variance was performed to reveal influential and crucial inputs for machining measures;results were interpreted and modeled statistically. After conducting the experimental runs, the novel attempt has further been made to conduct the multi-response optimization with the desirability method and the teaching-learning-based optimization (TLBO) algorithm to develop the best process setting for optimizing all considered measures jointly. Furthermore, machined sample's microstructure analysis was conducted with the scanning electron microscopy (SEM) analysis. The op- timized parametric setting was obtained as follows: pulse on time: 2 mu s, pulse off time: 4 mu s, peak current: 2 A, and wire feed rate: 5 m/min. Obtained experimental values for MRR, SR, and DD were 0.936 mm/min, 3.797 mu m, and 0.084 mm, respectively, with the combined desirability value of 0.601. optimization values of MRR, SR, and DD through the TLBO method varied from predicted values by 3.01%, 6.86%, and 6.32%, respectively.

关键词： optimization surface morphology superalloy teaching-learning-based optimization wire cut electrical discharge machining

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An adaptive framework to select the coordinate systems for evolutionary algorithms

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Gao, Weifeng Dang, Qianlong Gong, Maoguo Xidian Univ Sch Math & Stat Xian 710126 Peoples R China Xidian Univ Int Res Ctr Intelligent Percept & Computat Key Lab Intelligent Perception & Image Understand Minist Educ Xian 710071 Peoples R China

Many evolutionary algorithms usually utilize the fixed original coordinate system to search and cannot effectively match different function landscapes. To solve this issue, this paper proposes an adaptive framework, named STCS, to select the coordinate systems for evolutionary algorithms. In STCS, the eigen coordinate system is constructed by an archive-based covariance matrix, which can capture the feature of the function landscape. What is more, the selection process of the original coordinate system and the eigen coordinate system is defined as a Markov decision process and is controlled by reinforcement learning algorithm. STCS is applied to three popular evolutionary algorithms, i.e., differential evolution, particle swarm optimization, and teaching-learning-based optimization. The experimental results on IEEE CEC2013, IEEE CEC2014, and IEEE CEC2017 test suites demonstrate that STCS is efficient and competitive. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Evolutionary algorithms Reinforcement learning Differential evolution Particle swarm optimization teaching-learning-based optimization

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Redundancy allocation problem of a Multi-State system with Binary-State continuous performance level components

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期200卷 1页

作者： Sharifi, Mani Taghipour, Sharareh Ryerson Univ Dept Mech & Ind Engn 350 Victoria St Toronto ON M5B 2K3 Canada Ryerson Univ Canada Res Chair Phys Asset Management Dept Mech & Ind Engn 350 Victoria St Toronto ON M5B 2K3 Canada

This paper presents a new single-objective redundancy allocation problem (RAP) for a system with subsystems in a series-parallel configuration. Different types of binary-state components are available for each subsystem, without the choice of component mixing, but the allocated components must be identical. Moreover, the components have a continuous performance level, i.e., their performance level is between zero and their maximum performance level. The presented model aims to maximize the system's availability by determining the optimum number and type of the allocated components to each subsystem in some constraints, such as the system's cost and weight. Since the components have continuous performance levels, the performance levels of the subsystems and the system are also continuous. First, the real-time and instantaneous availability of components, subsystems, and systems is modeled and calculated by adopting a modified universal generating function. Then, the mathematical model for the above-mentioned RAP is presented and solved using a Genetic Algorithm (GA), a Particle Swarm optimization (PSO), and a teaching-learning-based optimization (TLBO) meta-heuristics. Next, a full enumeration technique is used to validate the performance of the adopted meta-heuristics as well as to validate the presented mathematical model. The results show the superiority of the adopted GA to solve the presented RAP. Finally, sensitivity analyses of the model's input parameters are conducted using a GA, and the effects of changing the model's parameters on the system's availability are investigated.

关键词： Redundancy allocation problem Binary-state continuous performance level components Modified universal generating function Genetic algorithm Particle Swarm optimization teaching-learning-based optimization

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A novel technique based on artificial intelligence for modeling the required temperature of a solar bread cooker equipped with concentrator through experimental data

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FOOD AND BIOPRODUCTS PROCESSING 2020年 123卷 437-449页

作者： Nazari, Saeed Karami, Alimohammad Bahiraei, Mehdi Olfati, Mohammad Goodarzi, Marjan Khorasanizadeh, Hossein Razi Univ Dept Mech Engn Kermanshah Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Natl Iranian Gas Co NIGC Tech & Engn Dept Kermanshah Iran Ton Duc Thang Univ Fac Environm & Labour Safety Sustainable Management Nat Resources & Environm R Ho Chi Minh City Vietnam Univ Kashan Dept Mech Engn Kashan Iran Univ Kashan Energy Res Inst Kashan Iran

In the present study, a combination of the modified sine and cosine algorithm (MSCA) and teaching-learning-based optimization (TLBO) algorithm is integrated with the neuro-fuzzy system to obtain three hybrid models. The proposed model predicts the effects of design parameters including the bottom galvanized protective edge of the cooking plate, top insulator cap of the cooking plate, position of the cooking plate, time duration of bread cooking and weather conditions on the dependent parameter which is the required temperature of a solar bread cooker equipped with a concentrator. For this purpose, the networks are trained on the basis of the experimentally measured data. The goal is to assess the ability of the hybrid networks for modeling cooking plate required based on the input variables. The quality of the bread produced by the solar cooker is evaluated by proper selection of the design parameters. The results show twelve breads per hour each with 200 g weight of dough can be produced by the cooker for at least six hours in every sunny day in eight months of the year, and also, the best hybrid network predicts the results with a low error which guarantees the performance of the applied hybrid model. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Solar bread cooker Cooking plate Design parameters Hybrid Model teaching-learning-based optimization Algorithm Experimental study

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A hybrid technique for simultaneous network reconfiguration and optimal placement of distributed generation resources

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SOFT COMPUTING 2020年第15期24卷 11315-11336页

作者： Quadri, Imran Ahmad Bhowmick, S. Delhi Technol Univ Dept Elect Engn Delhi India

A new meta-heuristic method, comprehensive teaching learning harmony search optimization algorithm (CTLHSO), is developed in this paper for the simultaneous reconfiguration and optimal allocation of distributed generation resources in radial distribution systems. The proposed method is a hybridization of the teaching-learning-based optimization (TLBO) and the harmony search (HS) algorithms. Primarily, eleven mathematical benchmark functions are used to test the performance of the CTLHSO algorithm. The results are then compared with that of global best artificial bee colony (G-ABC), particle swarm artificial bee colony (PS-ABC), TLBO and improved TLBO (I-TLBO) with identical parameters and initial conditions. The results show that the CTLHSO performance is better than the G-ABC, PS-ABC, TLBO and I-TLBO. Subsequently, CTLHSO is implemented on the IEEE 33-bus and 69-bus radial distribution systems for network reconfiguration and optimal placement of distributed generation resources to minimize the power losses and improve the voltage profiles. Five case studies at three different load levels are carried out. The results obtained are found to be better than those obtained with HS algorithm, genetic algorithm, refined genetic algorithm and fireworks algorithm.

关键词： Distributed generation Harmony search algorithm teaching-learning-based optimization Network reconfiguration

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Control and performance analyses of a DC motor using optimized PIDs and fuzzy logic controller

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RESULTS IN CONTROL AND optimization 2023年 13卷

作者： Manuel, Nelson Luis Inanc, Nihat Lily, Murat Kirikkale Univ Dept Elect & Elect Engn TR-71450 Kirikkale Turkiye

based on the no-free-lunch theorem, researchers have been proposing optimization algorithms for solving complex engineering problems. This paper analyzes the performance of five metabased optimization (TLBO), Differential Evolution (DE), and Genetic Algorithm (GA) in finetuning the gains of a Proportional-Integral-Derivative (PID) to control the speed of a DC motor. The selected metaheuristics, in addition to being from distinct classes, are well established in their respective groups. The methods and findings of this study can be summarized in three phases. First, the mathematical model of the DC motor is deduced. Second, detailed descriptions of the aforementioned algorithms are presented. Furthermore, the structures of the applied controllers are discussed. Third, comparisons based on statistical indicators and analyses in the time and frequency domains, in addition to robustness and load disturbance tests, are performed. The results revealed that if a sufficient number of runs is given for each metaheuristic, despite being in different runs, all algorithms are able to propose the same optimal gain values. TLBO presented the highest speed, while GA and DE were the slowest in finding optimal values. Additionally, the results were compared with the Opposition-based learning Henry Gas Solubility optimization (OBL/HBO)-based PID, reported to have better results than some previously published works on this topic, and a Fuzzy Logic Controller (FLC). The five optimized controllers obtained approximately the same results and outperformed the OBL/HGO-based PID, but the FLC was superior compared to the metaheuristic-based PIDs.

关键词： DC motor speed control Metaheuristic algorithms Equilibrium optimizer Particle swarm optimization teaching-learning-based optimization Differential evolution Genetic algorithm

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A hybrid teaching-learning-based optimizer with novel radix-5 mapping procedure for minimum cost power generation planning considering renewable energy sources and reducing emission

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ELECTRICAL ENGINEERING 2020年第4期102卷 2567-2582页

作者： Ashraf, Muhammad Mansoor Malik, Tahir Nadeem Univ Engn & Technol Dept Elect Engn Taxila 47050 Pakistan HITEC Univ Dept Elect Engn Taxila 47050 Pakistan

Power generation planning (PGP) is a vigorous stage in power system planning subsequently forecasting the load demand. The nature of the PGP problem is highly constrained, dynamic, discrete, nonlinear, stochastic, and mixed-integer. The amalgamation of the renewable energy sources in strategic PGP generates the complexity in terms of feeble reliability because of variability. The meta-heuristic algorithms are referred to as efficient solution techniques for the optimization of challenging and minimum-cost PGP. In this paper, a novel PGP optimizer has been proposed to plan the power generation plants reflecting the minimum cost and attaining an explicit reliability evaluation for specific planning prospect. The formulated and proposed optimizer is called teaching-learning-based optimization employing correction matrix method-with-indicators (TLBO-CMMI). In TLBO-CMMI, a new radix-5-based mapping procedure has been developed for the representation of population search agents. An innovative constraint handling procedure known as correction matrix method-with-indicators and incremental cost-based strategy to compute variable operation and maintenance cost, have been embedded from our previous research work. The optimizer has been employed to perform the PGP for different test scenarios in the literature including reliability and emission constraints. The optimizer gives promising results considering minimum cost and computational time when compared with results by current methods in the literature. The suitability of the optimizer has also been assessed by applying to an actual scenario of the Pakistan power system to create a viable power generation plan.

关键词： Renewable energy sources Power generation planning teaching-learning-based optimization Correction matrix method-with-indicators Equivalent energy function method

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Solving energy management of renewable integrated microgrid systems using crow search algorithm

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SOFT COMPUTING 2020年第14期24卷 10433-10454页

作者： Dey, Bishwajit Bhattacharyya, Biplab Srivastava, Apoorv Shivam, Kumar Indian Sch Mines Dept Elect Engn Indian Inst Technol Dhanbad Bihar India

This paper aims to percolate energy management of microgrid systems by minimizing the generation cost of the same. Energy management of microgrid refers to the optimal sizing and scheduling of the distributed energy resources to reduce the generation cost and pollutant emission. A recently developed crow search algorithm (CSA) is implemented to execute the optimization. The proposed CSA imitates the crows' memory and tactics of hiding and chasing their food. Six renewable integrated microgrid test systems and a total of eighteen different cases are considered for this study. Various practical complexities such as valve point loading effect, combined economic-emission dispatch using price penalty factor method, modeling of the renewable energy sources and energy storage systems are taken into consideration for energy management of the microgrid systems. Results obtained are then compared to a number of different soft computing techniques such as genetic algorithm and particle swarm optimization and the likes to justify the effectiveness of the proposed algorithm. A statistical analysis, viz. Wilcoxon signed-rank test, is performed to prove the superiority of the proposed approach over the various other optimization techniques used in the paper.

关键词： Combined economic-emission dispatch Penalty factor Microgrid Grey wolf optimization teaching-learning-based optimization Sine cosine algorithm Crow search algorithm

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Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

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SN APPLIED SCIENCES 2020年第2期2卷 273页

作者： Ezugwu, Absalom E. Univ KwaZulu Natal Sch Comp Sci King Edward RdPietermaritzburg Campus ZA-3201 Pietermaritzburg Kwazulu Natal South Africa

The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be

关键词： Automatic clustering Clustering analysis Differential evolution Particle swarm optimization Firefly algorithm Invasive weed optimization Artificial bee colony Bees algorithm Biogeography-based optimization Harmony search Symbiotic organisms search teaching-learning-based optimization DB validity index CS validity index

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A JIT part supply scheduling strategy with electric transport device between central receiving store and supermarkets in the automobile industry considering energy

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ETRANSPORTATION 2021年 9卷

作者： Jin, Wenrui He, Zhaoxu Peng, Tao Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China Tongji Univ Sino German Coll Appl Sci Shanghai 201804 Peoples R China

Sustainable scheduling has been attracting arousing attention of modern manufacturers, and energy efficiency becomes a critical issue related to sustainability. This paper aims at providing an effective solution method for a real-world energy-efficient part supply scheduling problem (EPSSP) between the central receiving store and supermarkets with electric transport device on purpose of coordinating production and transportation in the automobile industry. A bi-objective mixed integer linear programming (MILP) model is formulated to jointly optimize the total energy consumption and a just-in time (JIT) metric. An epsiloneconstraint approach is presented to obtain the optimal solutions in small-scale problems. Owing to EPSSP's NP-hard nature, an indicator-based bi-objective teaching learning-based optimization (I-BTLBO) algorithm is proposed by combining the binary quality indicator and teaching-learning-based optimization (TLBO) metaheuristic. I-BTLBO adopts a novel solution codification embedded with an adjustment technique to accommodate EPSSP's characteristic. For infeasible solutions, penalty function is applied to deal with the solution that violate vehicle capacities. A variable neighborhood search-based local optimizer is introduced to enhance I-BTLBO's convergence ability. In addition, an external population (EP) is employed to preserve non-dominated solutions' diversity and to guide the evolution process based on the Pareto dominance and preference indicators. Experimental studies demonstrate that the developed novel approach can effectively and efficiently solve EPSSP problem at different scales, and a case study is carried out to give instructive significance on real-world production. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Scheduling Part supply Energy-efficiency Electric transport device Bi-objective optimization teaching-learning-based optimization

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