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Optimization of ball mill cylinder structure based on response surface optimization module and multi-objective genetic algorithm

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第7期38卷 3631-3640页

作者： Shang, Yunyue Shen, Jicheng Wei, Weihua Zheng, Bo Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Peoples R China

The cylinder mass affects the ball mill's operation and economy. To reduce the mass of the current cylinder, this paper utilized a response surface optimization module and a multi-objective genetic algorithm to optimize the cylinder structure. First, the optimal parts of the cylinder were determined by static analysis. Subsequently, the mathematical model was established with the goal of achieving structural lightweight. Then, the size of the cylinder was optimized based on the response surface optimization module and multi-objective genetic algorithm. Finally, the static analysis and modal analysis results before and after optimization were compared to verify the stress and resonance of the structure, and the optimization effect was evaluated. The optimized cylinder had a mass reduction of 15.624 % without compromising the strength and deformation. The optimization design can save materials and costs, and provide a theoretical reference for the lightweight research of the ball mill's cylinder.

关键词： Ball mill cylinder Response surface optimization module multi-objective genetic algorithm Finite element analysis Structure optimization

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Active Control of Pressure Resonance in Long Pipeline of Bottom Founded Hydraulic Wind Turbines Based on multi-objective genetic algorithm

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IEEE ACCESS 2018年 6卷 53368-53380页

作者： Ai, Chao Bai, Wenjie Zhang, Tingyuan Kong, Xiangdong Yanshan Univ Minist Educ China Key Lab Adv Forging & Stamping Technol & Sci Qinhuangdao 066004 Peoples R China Yanshan Univ Hebei Heavy Machinery Fluid Power Transmiss & Con Qinhuangdao 066004 Peoples R China

The long pipeline in bottom-founded hydraulic wind turbines will greatly reduce the system natural frequency, and pressure resonances can occur more easily at multiple frequencies under time-varying wind excitation, which will reduce the electrical power quality and endanger the safe operation of the system. First, the hydraulic system impedance model is established based on electric-hydraulic analogy, and the pressure transfer function of two ends of the long pipeline is obtained, based on which the pressure resonance condition equation is solved by theoretical derivation. Furthermore, the multiple parameters sensitivity analysis of resonant frequency is carried out by using Isight and MATLAB. A novel active control strategy of pressure resonance is proposed, where the variable motor is adopted as the control object for real-time control of the terminal impedance to make the resonance frequency avoid the excitation frequency, and its feasibility is verified. Finally, with the objectives of optimizing the pressure pulsation attenuation and wind energy loss, a multi-objective genetic algorithm is used to optimize the parameters of the control object. A reliable Pareto set is obtained through multiple iterations, and different degrees of resonance suppression can be achieved.

关键词： Active control hydraulic wind turbine long pipeline multi-objective genetic algorithm pressure resonance

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Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design

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ENERGY AND BUILDINGS 2015年第Feb.期88卷 135-143页

作者： Yu, Wei Li, Baizhan Jia, Hongyuan Zhang, Ming Wang, Di Chongqing Univ Fac Urban Construct & Environm Engn Chongqing 400045 Peoples R China Chongqing Univ Minist Educ Key Lab Three Gorges Reservoir Reg Ecoenvironm Chongqing 400045 Peoples R China Chongqing Univ Natl Ctr Int Res Low Carbon & Green Bldg Chongqing 400045 Peoples R China State Key Lab Bldg Safety & Built Environm Beijing 100015 Peoples R China

Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA-BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings;Third, the building design multi-objective optimization model was established by using the GA-BP network as a fitness function of the multi-objective genetic algorithm (NSGA-II);Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Building design Energy consumption Thermal comfort Artificial neural network multi-objective genetic algorithm

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A Fault-Tolerant Scheduling algorithm Based on a multi-objective genetic algorithm for Precedence-Constrained Tasks in Real-Time Heterogeneous Distributed Systems

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2013年第5期10卷 1091-1096页

作者： Chen Yuanlong Ma Peijun Li Dong Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

In this paper, we investigate an efficient off-line fault-tolerant scheduling algorithm that generates schedules in which real-time tasks with precedence constraints can tolerate more than one fault at a time in a heterogeneous system with a fully connected network. We assume tasks to be non-preemptable, and each task has two copies scheduled on different processors and mutually excluded. Our fault-tolerant scheduling algorithm based on non-domination sort genetic algorithm (NSGA) employs performance improving techniques, including (1) a dynamic distance concept that decides the relative position of two task copies in the task graph, (2) a multi objectives genetic algorithm to optimise multiple targets. We performed experiments using synthetic workloads and a real-world application. Simulation results show that compared with existing scheduling algorithms, our scheduling algorithm improves guarantee ratio, reliability and overhead.

关键词： Heterogeneous Distributed System Dynamical Distance Fault Tolerant Scheduling multi-objective genetic algorithm

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A multi-objective genetic algorithm based on two-stage reinforcement learning for green flexible shop scheduling problem considering machine speed

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 258卷

作者： Zhuang, Mengzhen Zhang, Wei Tang, Hongtao Li, Xinyu Wang, Kaipu Wuhan Univ Technol Wuhan Sch Mech & Elect Engn Wuhan 430074 Peoples R China Hubei Prov Engn Res Ctr Robot & Intelligent Mfg Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

The consumption of energy and resources in the manufacturing industry has garnered significant attention due to the increasingly severe environmental issues. Green shop scheduling research is focused on optimizing economic and environmental indicators within the current manufacturing model. This paper specifically addresses the flexibility of job-shop scheduling problem considering machine speed (CMS-FJSP), as different machine speeds during the production process can impact energy and resource consumption. The objectives of this study include minimizing the makespan, total energy consumption, and tool wear. To tackle this problem, a multi-objective genetic algorithm that incorporates a two-stage reinforcement learning approach is proposed. In light of the characteristics of the problem, a three-layer encoding approach is suggested, which encompasses machine allocation, operation sequencing, and machine speed selection. Additionally, a decoding method that integrates energy-saving strategies is proposed to enhance the optimization process. To improve the quality of the population, three distinct initialization methods have been developed. Furthermore, a parameter adjustment strategy informed by two-stage reinforcement learning is introduced. This strategy incorporates a state set and action set tailored to the unique characteristics of two-stage reinforcement learning, alongside corresponding reward mechanisms. In 30 test cases, the proposed algorithm demonstrates superior uniformity and convergence compared to five classical algorithms. In a practical case within a hydraulic component production workshop conducted at a hydraulic component company, the proposed algorithm generates 26 scheduling schemes with different focuses, achieving a 14.39% reduction in makespan, a 2.13% decrease in energy consumption, and a 10.65% reduction in tool wear.

关键词： Green shop scheduling multi-objective genetic algorithm Reinforcement learning Machine speed Parameters adjusting strategy

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multi-objective optimization of μ-EDM parameters for μ-hole drilling on Hastelloy C 276 super alloy using Response Surface Methodology and multi-objective genetic algorithm

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CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY 2022年 39卷 115-133页

作者： Dutta, Souradeep Sarma, Deba Kumar Natl Inst Technol Meghalaya Dept Mech Engn Shillong 793003 India Natl Inst Technol Meghalaya Dept Mech Engn Shillong 793003 Meghalaya India

Hastelloy C 276 is one of the nickel-based super alloys which has an excellent resistance against corrosion at high temperature. It has a major application in nuclear and aerospace industry. Micro-electrical discharge machining (mu-EDM) is a highly accurate and precise non-conventional micro-machining process to fabricate this alloy. Thus, in this research paper an effort has been made to examine the impact of input process parameters like capacitance, gap voltage and pulse on time (t(on)) on the response variables i.e., material removal rate (MRR), tool wear rate (TWR) and diametral overcut (DOC) during mu-EDM of Hastelloy C 276. Response Surface Methodology (RSM) is a collection of statistical and mathematical tools which determine the relationship between input and output characteristics. Box-Behnken design (BBD) is a special type of RSM based design that reduces the no of experiments without affecting the variation between the input and response parameters. RSM based BBD is used in the current study to develop the mathematical models for each response variable. RSM based desirability function (RSM-DF) which convert multiple responses into a scale free desirability, is used as an optimization tool in the present study. The current work used the multi -objective genetic algorithm (MOGA), a non-conventional searching based multi-response optimization al-gorithm, to optimize the machining parameters for maximum MRR along with minimum TWR and DOC. The lowest machining time is achieved by experiment no 10 and the least entry mu-hole diameter is drilled by experiment no 15. ANOVA analysis accomplished that capacitance has the highest significant contribution towards MRR, TWR and DOC. 3D response surface plot discloses that MRR, TWR and DOC is increased with capacitance until 5600 pF, 5600 pF and 8900 pF and then there is little reduction took place for all the three responses. The optimization result from RSM-DF is capacitance of 1001.560 pF, gap voltage of 137.6670 vo

关键词： Micro-electrical discharge machining Material removal rate Tool wear rate Diametral overcut Response Surface Methodology multi-objective genetic algorithm

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A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第5-8期51卷 757-767页

作者： Wang, Xiaojuan Gao, Liang Zhang, Chaoyong Shao, Xinyu Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a multi-objective genetic algorithm (MOGA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The proposed algorithm is evaluated on some representative instances, and the comparison with other approaches in the latest papers validates the effectiveness of the proposed algorithm.

关键词： Flexible job-shop scheduling multi-objective genetic algorithm Pareto-optimality Immune and entropy principle

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Allocation of multi-type FACTS devices using multi-objective genetic algorithm approach for power system reinforcement

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ELECTRICAL ENGINEERING 2010年第6期92卷 227-237页

作者： Gitizadeh, M. Shiraz Univ Technol Sch Elect & Elect Engn Shiraz Iran

This paper presents a novel approach to find optimum locations and capacity of flexible alternating current transmission systems (FACTS) devices in a power system using a multi-objective optimization function. Thyristor controlled series compensator (TCSC) and static var compensator (SVC) are the utilized FACTS devices. Our objectives are: active power loss reduction, new introduced FACTS devices cost reduction, voltage deviation reduction, and increase on the robustness of the security margin against voltage collapse. The operational and controlling constraints, as well as load constraints, are considered in the optimum allocation. A multi-objective genetic algorithm (MOGA) is used to approach the Pareto-optimal front (non-dominated) solutions. In addition, the estimated annual load profile has been utilized in a sequential quadratic programming (SQP) optimization sub-problem to the optimum siting and sizing of FACTS devices. IEEE 14-bus Network is selected to validate the performance and effectiveness of the proposed method.

关键词： FACTS devices allocation multi-objective optimization multi-objective genetic algorithm

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Stable cutting zone prediction in CNC turning using adaptive signal processing technique merged with artificial neural network and multi-objective genetic algorithm

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2018年 70卷 238-248页

作者： Shrivastava, Y. Singh, B. Jaypee Univ Engn & Technol Mech Engn Dept Guna MP India

In metal cutting, surface quality and material removal rate are the key parameters investigated by several researchers. It has been already established that, at high-speed machining, tool chatter deteriorates the work-piece surface and effects the material removal rate too. Numerous crucial investigations have been carried out regarding the enhancement of these parameters considering tool chatter as a major thread. In the recent advancement, signal processing techniques are being used for suppression of chatter. Moreover, it has been found these advance techniques helps in predicting the actual nature of chatter. However, the chatter signal recorded during machining usually contain contaminations merged with actual signal. Hence, it becomes a task for researchers to rectify the signal and predict a suitable cutting zone that is capable of obtaining good surface finish with acceptable material removal rate. In the present work, ensemble empirical mode decomposition technique has been used to rectify the signal and optimal cutting zone has been predicted using the artificial neural network and multi-objective genetic algorithm. Machining in the obtained optimal zone will upsurge the productivity, by decreasing tool chatter and increasing material removal rate simultaneously. To validate the proposed methodology, experiments have been performed within the obtained optimal zone. The results indicate the effectiveness of the proposed methodology.

关键词： Tool chatter Data acquisition Ensemble empirical mode decomposition Intrinsic mode function Artificial neural network multi-objective genetic algorithm

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Application of elitist multi-objective genetic algorithm for classification rule generation

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APPLIED SOFT COMPUTING 2008年第1期8卷 477-487页

作者： Dehuri, S. Patnaik, S. Ghosh, A. Mall, R. Fakir Mohan Univ PG Dept Informat & Commun Technol Balasore 756019 Orissa India Indian Stat Inst Machine Intelligence Unit Kolkota W Bengal India Indian Stat Inst Ctr Soft Comp Res Kolkota W Bengal India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

We present an elitist multi-objective genetic algorithm (EMOGA) for mining classification rules from large databases. We emphasize on predictive accuracy, comprehensibility and interestingness of the rules. However, predictive accuracy, comprehensibility and interestingness of the rules often conflict with each other. This makes it a multi-objective optimization problem that is very difficult to solve efficiently. We have proposed a multi-objective genetic algorithm with a hybrid crossover operator for optimizing these objectives simultaneously. We have compared our rule discovery procedure with simple genetic algorithm with a weighted sum of all these objectives. The experimental result confirms that our rule discovery algorithm has a clear edge over simple genetic algorithm. (C) 2007 Elsevier B. V. All rights reserved.

关键词： genetic algorithm multi-objective genetic algorithm rule mining data mining

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