scholarly journals Multi-objective optimization method for magnetic media pre-bulging process parameters of spherical bottom cylindrical parts based on response surface

2019 ◽  
Vol 14 ◽  
pp. 102487 ◽  
Author(s):  
Yang Liu ◽  
Feng Li ◽  
Wen-yong Shi ◽  
Xue-wen Li ◽  
Wen-bin Fang
Keyword(s):  
Response Surface ◽  
Process Parameters ◽  
Optimization Method ◽  
Magnetic Media ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Spherical Bottom ◽  
Cylindrical Parts

scholarly journals Mechanical properties optimization of the steering column based on multi-objective optimization design

2016 ◽  
Vol 8 (12) ◽  
pp. 168781401668294 ◽  
Author(s):  
Si Chen ◽  
Zhaohui Wang ◽  
Mi Lv
Keyword(s):  
Mechanical Properties ◽  
Process Parameters ◽  
Optimization Design ◽  
Strain Difference ◽  
Optimization Method ◽  
Equivalent Strain ◽  
Multi Objective Optimization ◽  
Optimal Process ◽  
Multi Objective ◽  
Initial Results

The mechanical properties of the steering column have a significant influence on the comfort and stability of a vehicle. In order for the mechanical properties to be improved, the rotary swaging process of the steering column is studied in this article. The process parameters, including axial feed rate, hammerhead speed, and hammerhead radial reduction, are systematically analyzed and optimized based on a multi-objective optimization design. The response surface methodology and the genetic algorithm are employed for optimal process parameters to be obtained. The maximum damage value, the maximum forming load, and the equivalent strain difference obtained with the optimal process parameters are, respectively, decreased by 30.09%, 7.44%, and 57.29% compared to the initial results. The comparative results present that the quality of the steering column is improved. The torque experiments and fatigue experiments are conducted with the optimal steering column. The maximum torque is measured to be 260 NM, and the service life is measured to be 2 weeks (40 NM, 2500 times), which are, respectively, increased by 8.3% and 8.69% compared to the initial results. The above results display that the mechanical properties of the steering column are optimized to verify the feasibility of the multi-objective optimization method.

scholarly journals Multi-Objective Optimization of Process Parameters of Longitudinal Axial Threshing Cylinder for Frozen Corn Using RSM and NSGA-II

2020 ◽  
Vol 10 (5) ◽  
pp. 1646 ◽  
Author(s):  
Jun Fu ◽  
Haikuo Yuan ◽  
Depeng Zhang ◽  
Zhi Chen ◽  
Luquan Ren
Keyword(s):  
Process Parameters ◽  
Feed Rate ◽  
Loss Rate ◽  
Optimization Method ◽  
Operational Performance ◽  
Multi Objective Optimization ◽  
Optimal Process ◽  
Nsga Ii ◽  
Multi Objective ◽  
Damage Rate

Corn was frozen at harvest time in high-latitude areas, when corn kernel is wetter and more easily broken. When frozen corn was threshed and separated by the longitudinal axial threshing cylinder of a combine harvester, it caused a significantly high kernel damage rate and loss rate. The process parameters of threshing cylinder were optimized using RSM (response surface method) and NSGA-II (Non-Dominated Sorted Genetic Algorithm-II). The drum speed (Ds), feed rate (Fr) and concave clearance (Cc) were determined as the optimized process parameters. The loss rate (Lr) and damage rate (Dr) were indicators of operational performance. The RSM was used to establish a mathematical model between process parameters and indicators. With an elite strategy, NSGA-II was used for multi-objective optimization to obtain the optimal operational performance of the threshing cylinder. Overall, when the drum speed was selected as 384.1 rpm, the feed rate as 8.6 kg/s and the concave clearance as 40.5 mm, according to the requirement of corn harvest, the best operational performance of the longitudinal axial threshing cylinder on frozen corn was obtained. The Lr was 1.98% and the Dr was 3.49%. This result indicated that the applicability of the optimal process parameters and the optimization method of combining NSGA-II and RSM was effective for determining the optimal process parameters. This will provide an optimization method for synchronously reducing the loss rate and damage rate of grain harvesters.

scholarly journals Optimization of process parameters for surface roughness and tool wear in milling TC17 alloy using Taguchi with grey relational analysis

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199653
Author(s):  
Zhe Wang ◽  
Lei Li
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Process Parameters ◽  
Critical Role ◽  
Optimization Method ◽  
Ratio Method ◽  
Multi Objective Optimization ◽  
Processing Efficiency ◽  
Multi Objective ◽  
Process Guidance

To improve machining quality and processing efficiency, the Taguchi analysis method is employed to design the milling tests of titanium alloy TC17. According to results based on the signal-to-noise ratio method, the cutting depth plays a critical role in improving the surface roughness and tool wear. The grey correlation analysis is a multi-objective optimization method that can help to acquire process parameters combination of the optimal surface roughness and the optimal tool wear. Finally, the correctness of multi-objective optimization results is verified through comparison experiments. The research results can provide process guidance and data reference for the actual production processing.

scholarly journals Collaborative improvement of efficiency and noise of bionic vane centrifugal pump based on multi-objective optimization

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199497
Author(s):  
Houlin Liu ◽  
Zhiming Cheng ◽  
Zhipeng Ge ◽  
liang dong ◽  
cui dai
Keyword(s):  
Response Surface ◽  
Centrifugal Pump ◽  
Sound Pressure Level ◽  
Sound Pressure ◽  
Optimization Method ◽  
Pressure Level ◽  
Hydraulic Efficiency ◽  
Multi Objective Optimization ◽  
Acoustic Performance ◽  
Multi Objective

The hydraulic and acoustic performance of centrifugal pump is closely related to hydraulic structure parameters, and they are contradictory. In order to solve this contradiction, this paper introduces the pit bionic structure, and proposes an optimization method based on multi-objective test design and response surface to improve the hydraulic and acoustic performance. Taking the bionic vane pit diameter, axial spacing and radial spacing as design variables. Taking the maximum hydraulic efficiency and total sound pressure level reduction of centrifugal pump as the corresponding objectives. The multiple regression response surface model was constructed to determine the optimal parameter combination of hydraulic performance and noise collaborative optimization. The optimization results were verified by numerical simulation and experimental test. The results show that the response surface multi-objective optimization method has high prediction accuracy, has obvious synergistic effect on the hydraulic and acoustic performance. The highest point of the efficiency curve after optimization is shifted to the direction of large flow, which widens the high efficiency working area of centrifugal pump. Under the rated condition, the hydraulic efficiency is increased by 3.03%, the efficiency increase rate is 4.21%, the total sound pressure level is reduced by 4.96 dB, and the noise reduction rate is 3.01%.

Multi-Objective Optimization of Abrasive Waterjet Machining Process Parameters Using Particle Swarm Technique

2014 ◽  
Vol 1 (2) ◽  
pp. 62-79
Author(s):  
V. Murugabalaji ◽  
M. Kanthababu ◽  
J. Jegaraj ◽  
S. Saikumar
Keyword(s):  
Response Surface ◽  
Process Parameters ◽  
Particle Swarm ◽  
Mesh Size ◽  
Machining Process ◽  
Abrasive Waterjet ◽  
Depth Of Cut ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Abrasive Waterjet Machining

Multi-objective optimization is carried out for the first time to optimize abrasive water jet machining (AWJM) process parameters for graphite. Experiments are carried out by Response Surface Methodology (RSM) using Box-Behnken method. The input process parameters considered are pressure (P), traverse rate (TR) and mesh size (MS). Results are analyzed using Analysis of Variance (ANOVA) and response surface considering individually output parameters such as depth of cut (DOC) and surface roughness (Ra). ANOVA and response surface analyses indicated that similar combinations of AWJM process parameters such as high pressure (176 MPa), medium mesh size (# 100) and low traverse rate (1000 mm/min) resulted in higher depth of cut as well as lower Ra. Therefore, in order to verify the above combinations and to improve productivity, multi-objective optimization is carried out using Particle Swarm Optimization (PSO) to achieve higher depth of cut and low Ra together. From the PSO analysis, it is observed that pressure of 154 MPa, traverse rate of 1877 mm/min and mesh size of # 100 result in high depth of cut and low Ra together. The result obtained from the PSO is compared with that of ANOVA. The outcome of this study will be useful to the manufacturing engineers for selecting appropriate input AWJM process parameters for machining graphite, which has various applications such as aerospace, defence, etc.

Machining Process Parameters Optimization Based on Grid Optimization Algorithm

2012 ◽  
Vol 562-564 ◽  
pp. 2021-2025 ◽  
Author(s):  
Chao Deng ◽  
Yao Xiong ◽  
Yuan Hang Wang ◽  
Jun Wu
Keyword(s):  
Process Parameters ◽  
Optimization Algorithm ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Machining Process ◽  
Cnc Machine Tools ◽  
Multi Objective Optimization ◽  
Cnc Machine ◽  
Multi Objective ◽  
Grid Optimization

Machining process parameters directly affect the machining quality and efficiency of heavy-duty CNC machine tools, selecting correctly machining process parameters can improve the machine’s machining performance effectively. This paper presents a machining process parameters optimization method based on grid optimization algorithm for heavy-duty CNC machine tools. In this method, a multi-objective optimization model will be established, which considers not only the linear constrains of machining process parameters, such as machining time and machining cost, but also the non-linear constrain, such as chatter in machining process. Grid optimization algorithm will be adapted to search the optimal combination of machining process parameters from the multi-objective optimization model. In the end, this paper will present an example to verify superiority of the multi-objective optimization method by comparing with single-objective optimization method.

Multi-Objective Optimization of a Bolt-Flange Structure

2011 ◽  
Vol 233-235 ◽  
pp. 2800-2804
Author(s):  
Yuan Dong Liu ◽  
Yi Hui Yin ◽  
Ying Chun Lu
Keyword(s):  
Response Surface ◽  
Design Method ◽  
Equivalent Stress ◽  
Optimization Method ◽  
Surface Model ◽  
Geometrical Parameters ◽  
Multi Objective Optimization ◽  
Surface Design ◽  
Multi Objective ◽  
Maximum Equivalent Stress

The bolt-flange structure is most one of joint mode, and stress and mass are its major performance parameters. The multi-object optimization of a bolt-flange structure can be performed by using Finite element method and optimization method unitedly. The response surface design method was employed to determine the combination of geometrical parameters to be designed of the bolt-flange structure. The stress of the bolt-flange structure which has the different geometrical parameters was numerically simulated and analyzed by using the software ANSYS. The response surface model is obtained. The optimized geometrical parameters of the bolt-flange structure were obtained by using MATLAB multi-objective optimization method. The results showed that the maximum equivalent stress in the optimized bolt-flange structure decreased 13.4% than that in the original one and the mass of the optimized bolt-flange structure was lower 14.3% than that of the original one.

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